WO2023165500A1 - Processing method and apparatus for data processing task, storage medium, and electronic device - Google Patents

Abstract

Embodiments of the present description provide a processing method and apparatus for a data processing task, a storage medium, and an electronic device. The method comprises: obtaining data processing task data, wherein a data processing task is a solving task of a differential equation, and the data processing task data comprises the differential equation; constructing a differentiable quantum circuit according to the data processing task data; and predicting a target processing result of the data processing task on the basis of the differentiable quantum circuit until when the value of a loss function constructed according to a predicted processing result satisfies a specified precision condition, the predicted processing result enabling the value of the loss function to satisfy the specified precision condition is used as the target processing result. According to the method provided by the embodiments of the present description, the loss function can be constructed on the basis of a predicted solution of the differential equation, and another processing manner of the solving task of the differential equation is realized by updating the value of the loss function, so that the resource consumption caused by grid division is reduced.

Description

Data processing task processing method, device, storage medium and electronic equipment technical field

The embodiments of this specification relate to the field of data processing, and specifically relate to a processing method, device, storage medium, and electronic device for a data processing task.

Background technique

In the field of computing, hardware or software functions are primarily implemented in the form of processing data processing tasks. Execute the processing process of the data processing task according to the input data, and use the output processing result of the data processing task as the direct output of the specified function or the instruction to realize the specified function.

In many fields of science and technology (for example, fluid mechanics, finance, biology, chemistry, etc.), a large number of data processing tasks involve the solution of differential equations. How to accurately and quickly process the data processing tasks of solving differential equations shows great important application value.

The existing methods for solving differential equations directly solve the solution of differential equations in the entire computational domain, and there is a problem of large resource consumption for grid division in the computational domain.

Quantum computing is a new type of computing. The principle is to construct a computing framework with the theory of quantum mechanics. Compared with the optimal classical algorithm, quantum computing has the effect of exponential speed-up when solving certain problems.

Contents of the invention

Embodiments of this specification provide a data processing task processing method, device, storage medium, and electronic equipment, which are used to reduce resource consumption caused by grid division.

An embodiment of this specification provides a processing method for a data processing task, the processing method comprising: acquiring data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes The differential equation; constructing a differentiable quantum circuit according to the data processing task data; predicting the target processing result of the data processing task based on the differentiable quantum circuit, and constructing the predicted processing result according to the prediction When the value of the loss function meets the specified accuracy condition, the prediction processing result that makes the value of the loss function meet the specified accuracy condition is taken as the target processing result.

In another embodiment of this specification, predicting the target processing result of the data processing task based on the differentiable quantum circuit includes: predicting the target of the differential equation based on the variational parameters in the differentiable quantum circuit solution to predict.

In another embodiment of this specification, the data processing task data further includes the initial conditions and boundary conditions satisfied by the solution of the differential equation; the step of constructing a differentiable quantum circuit according to the data processing task data includes: The differential equation and the initial conditions and boundary conditions satisfied by the solution of the differential equation construct the differentiable quantum circuit, and determine the variational parameters in the differentiable quantum circuit.

In another embodiment of this specification, constructing the differentiable quantum circuit according to the differential equation and the initial conditions and boundary conditions satisfied by the solution of the differential equation includes: acquiring a group of qubits and The initial state of is set to |0>; use the first type of quantum logic gates to construct the first sub-quantum circuit module that generates the function space basis set; use the second type quantum logic gate to construct the function space basis set for combining the differential The second sub-quantum circuit module of the predicted solution of the equation; construct the measurement operation module used to obtain the predicted solution of the differential equation; sequentially connect the first sub-quantum circuit module, the second sub-quantum circuit module and the The measurement operation modules are combined to obtain the differentiable quantum circuit.

In another embodiment of this specification, the step of predicting the target solution of the differential equation based on the variational parameters in the differentiable quantum circuit includes: updating the variational parameters through an optimization algorithm, based on the updated The latter variational parameters predict the target solution of the differential equation.

In another embodiment of the present specification, the updating of the variational parameters through an optimization algorithm includes: updating the variational parameters θ through the following formula: Wherein, the n is an integer not less than 1, α is the learning rate, and L is the loss function, is the gradient of the loss function to θ.

In another embodiment of this specification, the loss function is:

Wherein, the L _θ ^(diff) represents that the predicted solution of the differential equation does not satisfy the error of the differential equation, the d _x f represents the derivative term in the differential equation, and the f represents the difference between the differential equation and the differential equation Corresponding function, said x represents a variable, said L _θ ^(boundary) represents the error that the predicted solution of said differential equation does not satisfy said boundary condition and said initial condition, said Indicates the regularization term error.

In another embodiment of this specification, the differential equation is a time-dependent partial differential equation; the target processing result of the data processing task is predicted based on the differentiable quantum circuit, and the predicted processing obtained according to the prediction When the value of the loss function constructed as a result meets the specified accuracy condition, the step of taking the predicted processing result that makes the value of the loss function meet the specified accuracy condition as the target processing result includes: based on the initial variable of the differentiable quantum circuit Predict the target solution of the time-dependent partial differential equation by sub-parameters, until according to If the value of the loss function at the current moment constructed by the prediction processing result obtained by the prediction meets the specified accuracy condition, the predicted solution of the time-dependent partial differential equation that makes the value of the loss function meet the specified accuracy condition is used as The objective solution of the time-dependent partial differential equation.

In another embodiment of this specification, the time-dependent partial differential equation includes:

Wherein, the u(x, t) is the solution of the time-dependent partial differential equation, x is a space variable, t is a time variable, N'[u(x, t)] is a nonlinear term, and Ω is a computational domain , T is time.

In another embodiment of this specification, the data processing task data also includes the initial conditions and boundary conditions of the time-dependent partial differential equation; after the step of obtaining the data processing task data, it also includes: A semi-discrete form of the time-dependent partial differential equation is determined.

In another embodiment of this specification, the pre-selected difference format is an implicit difference format; the step of determining the time-dependent partial differential equation in a semi-discrete form according to the pre-selected difference format includes: determining a semi-discrete The time-dependent partial differential equation of the form is:

u(x, t+Δt)=u(x, t)+Δt·N′[u(x, t+Δt)], wherein, the u(x, t+Δt) is the time-dependent partial differential The solution of the equation at time t+△t.

In another embodiment of this specification, predicting the target solution of the time-dependent partial differential equation based on the initial variational parameters of the differentiable quantum circuit includes: obtaining a group of qubits and Set the state to |0>; use the first type of quantum logic gates to construct the first sub-quantum circuit module that generates the function space basis set; obtain the initial variational parameters and use the second type of quantum logic gates to construct the function space basis set Combining the second sub-quantum circuit module to form the predicted solution of the time-dependent partial differential equation; constructing a measurement operation module for obtaining the predicted solution of the time-dependent partial differential equation; sequentially combining the first sub-quantum circuit module, the The second sub-quantum circuit module is combined with the measurement operation module to construct the differentiable quantum circuit, and determine the predicted solution of the time-dependent partial differential equation according to the obtained final quantum state corresponding to the initial variational parameter.

In another embodiment of this specification, the determination of the predicted solution of the time-dependent partial differential equation includes: obtaining a pre-selected measurement operator; determining the expected value corresponding to the measurement operator according to the final quantum state; according to The expected value determines a predicted solution of the time-dependent partial differential equation.

In another embodiment of this specification, the first type of quantum logic gates includes: Rx quantum logic gates, Ry quantum logic gates and Rz quantum logic gates; the second type of quantum logic gates includes: Rx quantum logic gates, Ry quantum logic gates Quantum logic gates, Rz quantum logic gates and CNOT quantum logic gates.

In another embodiment of this specification, the loss function at the current moment constructed according to the prediction processing result obtained from the prediction includes: if the current moment is the initial moment, the loss function at the initial moment is:

Wherein, the N' is the number of discrete points, M is the number of boundary points, i is the ith discrete point, j' is the j'th boundary point, is the prediction processing result at the initial moment, g( _xi ) is the initial condition, h(x _j' ,0) is the boundary condition at the initial moment; if the current moment is △t time, then the loss function at △t time is:

Wherein, the N'[u(x,t)] is determined according to the differentiable quantum circuits at the previous moment.

In another embodiment of the present specification, the method further includes: updating the variational parameter θ through the following formula: Wherein, the n is an integer not less than 1, α is the learning rate, and L is the loss function, is the gradient of the loss function to θ.

In another embodiment of the present specification, after the step of acquiring data processing task data, the method further includes: acquiring the computational domain of the differential equation and dividing the computational domain into several non-overlapping sub-computational domains; The step of constructing a differentiable quantum circuit according to the data processing task data includes: constructing a differentiable quantum circuit corresponding to the sub-computational domain; wherein, one sub-computational domain corresponds to a predicted solution of the differential equation; based on the The differentiable quantum circuit predicts the target processing result of the data processing task, and when the value of the loss function constructed according to the predicted processing result meets the specified accuracy condition, the value of the loss function meets the specified accuracy condition. The step of specifying the predicted processing result of the accuracy condition as the target processing result includes: predicting the target solution of the differential equation based on the differentiable quantum circuit, and then constructing the sub-calculation based on the predicted solution of the differential equation The value of the joint loss function of the domain conforms to the specified precision In the case of the accuracy condition, the predicted solution of the differential equation that makes the value of the loss function meet the specified accuracy condition is taken as the target solution of the differential equation.

In another embodiment of this description, the differential equation is:

Wherein, the F is a functional function, the d _x u is a derivative term, the u is the solution of the differential equation, and the x is a variable.

In another embodiment of this specification, predicting the target processing result of the data processing task based on the differentiable quantum circuit includes: the target solution of the differential equation based on the variational parameters in the differentiable quantum circuit Make predictions.

In another embodiment of this specification, the step of constructing a differentiable quantum circuit corresponding to the sub-computational domain includes: obtaining a group of qubits and setting the initial state of the qubits to |0>; using the first type Quantum logic gates, constructing the first sub-quantum circuit module for generating function space basis sets; utilizing the second type of quantum logic gates, constructing the second sub-quantum circuit module for combining function space basis sets into predicted solutions of differential equations; constructing A measurement operation module for obtaining a predicted solution of the differential equation; sequentially combining the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module to construct the differentiable quantum circuit.

In another embodiment of this specification, the step of predicting the target solution of the differential equation based on the variational parameters in the differentiable quantum circuit includes: obtaining a pre-selected measurement operator; The final quantum state corresponding to the variational parameter in the circuit determines the expected value corresponding to the measurement operator; and determines the predicted solution of the differential equation according to the expected value.

In another embodiment of this specification, the first type of quantum logic gates includes: Rx quantum logic gates, Ry quantum logic gates and Rz quantum logic gates; the second type of quantum logic gates includes: Rx quantum logic gates, Ry quantum logic gates Quantum logic gates, Rz quantum logic gates and CNOT quantum logic gates.

In another embodiment of this specification, the joint loss function is:

Wherein, the n is the number of sub-computational domains, the nb is the number of interfaces, and the L _i ^(diff) [d _x f _i , f _i , x] is the differential equation corresponding to the i-th sub-computational domain The error caused by the prediction solution not satisfying the differential equation, the L _i ^(boundary) [f _i , x] is the prediction solution of the differential equation corresponding to the i-th sub-calculation domain does not satisfy the solution of the differential equation The error caused by the boundary conditions satisfied, the L _i ^(interface) [f, x] is the predicted solution of the differential equation corresponding to the adjacent areas on both sides of the i-th interface does not satisfy the continuity condition of the interface and caused by the error and The n _i is the number of discrete points on the i-th interface, and f ⁺ (x _j ) and f ^- (x _j ) are the predicted solutions of the differential equations corresponding to the sub-calculation domains on both sides of the interface.

In another embodiment of this specification, the step of predicting the target solution of the differential equation based on the variational parameters in the differentiable quantum circuit includes: using an optimization algorithm to update the variational parameters in the differentiable quantum circuit Parameters; according to the updated variational parameters in the differentiable quantum circuit, an updated predicted solution of the differential equation is obtained.

In another embodiment of this specification, the step of using an optimization algorithm to update the variational parameters in the differentiable quantum circuit includes:

The variational parameter θ is updated by the following formula: Wherein, the n is an integer not less than 1, α is the learning rate, and L is the joint loss function, is the gradient of the joint loss function to θ.

In another embodiment of this specification, the data processing task data further includes the derivative term of the differential equation; the step of constructing a differentiable quantum circuit according to the data processing task data includes: according to the differential equation and the The derivative terms of the differential equation respectively construct the differentiable quantum circuit; predict the target processing result of the data processing task based on the differentiable quantum circuit, and to the loss function constructed according to the predicted processing result obtained by the prediction When the value meets the specified precision condition, the step of taking the predicted processing result that makes the value of the loss function meet the specified precision condition as the target processing result includes: the target solution of the differential equation based on the differentiable quantum circuit and The value of the derivative term of the differential equation is predicted to the value of the loss function constructed from the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation will make the loss A predicted solution of the differential equation whose value of the function satisfies the specified accuracy condition is used as a target solution of the differential equation.

In another embodiment of this description, the differential equation is:

Wherein, the F is a functional function, the d _x u is the derivative term of the differential equation, the u is the predicted solution of the differential equation, and the x is the variable quantity.

In another embodiment of this specification, the step of respectively constructing the differentiable quantum circuit according to the differential equation and the derivative term of the differential equation includes: respectively constructing the predicted solution for solving the differential equation and the solving the differentiable quantum circuit of the predicted value of the derivative term of the differential equation, and respectively determining the predicted solution of the differential equation and the prediction of the derivative term of the differential equation corresponding to the variational parameters in the differentiable quantum circuit value.

In another embodiment of this specification, said respectively constructing differentiable quantum circuits for solving the predicted solution of the differential equation and for solving the predicted value of the derivative term includes: respectively obtaining a group of qubits and The initial state of the qubit is set to |0>; using the first type of quantum logic gates, respectively constructing the first sub-quantum circuit modules that generate the function space basis set; using the second type of quantum logic gates, respectively constructing the function The space basis set is combined into the second sub-quantum circuit module of the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation; respectively constructed for obtaining the predicted solution of the differential equation and the derivative of the differential equation The measurement operation module of the predicted value of the item; respectively combining the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module to construct the differentiable quantum circuit.

In another embodiment of the present specification, the respectively determining the predicted solution of the differential equation corresponding to the variational parameter in the differentiable quantum circuit and the predicted value of the derivative term of the differential equation includes: obtaining a pre-selected measurement operator; determine the expected value corresponding to the measurement operator according to the final quantum state corresponding to the variational parameter in the differentiable quantum circuit; determine the predicted solution of the differential equation and the derivative of the differential equation according to the expected value item's predicted value.

In another embodiment of this specification, the loss function is:

L[f ⁱ , f, x]=L ^(diff) [f ⁱ , f, x]+L ^(boundary) [f, x], wherein said f _i represents the i-th order derivative in said differential equation item, the L ^(diff) represents that the predicted solution of the differential equation does not satisfy the error of the differential equation, the f is the differential equation, the x represents a variable, and the L ^(boundary) represents the differential equation The error in which the predicted solution of the equation does not satisfy the boundary conditions and initial conditions of the solution of the differential equation.

In another embodiment of this specification, determining the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation corresponding to the variational parameters in the differentiable quantum circuit respectively includes: updating the predicted value of the differential equation by using an optimization algorithm Variational parameters in the differentiable quantum circuit; according to the updated variational parameters in the differentiable quantum circuit, an updated predicted solution of the differential equation and a predicted value of a derivative term of the differential equation are obtained.

In another embodiment of the present specification, the step of using an optimization algorithm to update the variational parameters in the differentiable quantum circuit includes: updating the variational parameters θ by the following formula: Wherein, the n is an integer not less than 1, α is the learning rate, and L is the loss function, is the gradient of the loss function to θ.

An embodiment of this specification provides a processing device for a data processing task, and the determining device includes:

An acquisition module, configured to acquire data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes the differential equation;

a building block, configured to build a differentiable quantum circuit according to the data processing task data;

A prediction module, configured to predict the target processing result of the data processing task based on the differentiable quantum circuit, until the value of the loss function constructed according to the predicted processing result obtained from the prediction meets the specified accuracy condition; The prediction processing result that makes the value of the loss function conform to the specified accuracy condition is taken as the target processing result.

An embodiment of the present specification provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the method described in any one of the above when running.

One embodiment of the present specification provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor is configured to run the computer program to perform any of the above-mentioned method.

Compared with related technologies, the present application obtains data processing task data, constructs a differentiable quantum circuit based on the data processing task data, and then predicts the target processing result of the data processing task based on the differentiable quantum circuit, until When the value of the loss function constructed according to the prediction processing result obtained by the prediction meets the specified accuracy condition, the prediction processing result that makes the value of the loss function meet the specified accuracy condition is taken as the target processing result, and can be based on the prediction processing result Build a loss function and update the value of the loss function to solve the differential equation and reduce the resource consumption caused by grid division.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of this specification or related technologies, the following will briefly introduce the drawings that need to be used in the descriptions of the embodiments or related technologies. Obviously, the drawings in the following description are only Some implementations of the description should therefore not be regarded as limiting the scope. For those skilled in the art, other drawings can also be obtained according to these drawings without creative work.

FIG. 1 is a block diagram of the hardware structure of a computer terminal according to a data processing task processing method provided by an embodiment of this specification;

FIG. 2 is a schematic flowchart of a processing method for a data processing task provided by an embodiment of this specification;

Fig. 3 is a schematic diagram of a differentiable quantum circuit provided by an embodiment of this specification;

Fig. 4 is a schematic diagram of another differentiable quantum circuit provided by the embodiment of this specification;

FIG. 5 is a schematic flowchart of another data processing task processing method provided by the embodiment of this specification;

FIG. 6 is a schematic flowchart of another data processing task processing method provided by the implementation mode of this specification;

FIG. 7 is a schematic diagram of a calculation domain division provided by an embodiment of this specification;

Fig. 8 is a schematic diagram of another differentiable quantum circuit provided by the embodiment of this specification;

FIG. 9 is a schematic flowchart of another method for processing a data processing task provided by an embodiment of this specification;

FIG. 10 is a schematic structural diagram of a processing device for a data processing task provided by an embodiment of this specification.

Detailed ways

The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

An embodiment of this specification provides a method for processing data processing tasks, and the method can be applied to electronic devices, such as computer terminals, specifically, ordinary computers, quantum computers, and the like.

The following will describe it in detail by taking it running on a computer terminal as an example. FIG. 1 is a block diagram of a hardware structure of a computer terminal according to a data processing task processing method provided by an embodiment of this specification. As shown in FIG. 1 , the computer terminal may include one or more (only one is shown in FIG. 1 ) processors 102 and a memory 104 for storing data, wherein the processor 102 may include but not limited to a microprocessor (Microcontroller Unit, MCU) or a processing device such as a programmable logic device (Field Programmable Gate Array, FPGA). In some implementations, the above-mentioned computer terminal may further include a transmission device 106 and an input and output device 108 for communication functions. Those skilled in the art can understand that the structure shown in FIG. 1 is only for illustration, and it does not limit the structure of the above computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 1 , or have a different configuration than that shown in FIG. 1 .

The memory 104 can be used to store software programs and modules of application software, such as program instructions/modules corresponding to a processing method for implementing a data processing task in the embodiment of this specification, and the processor 102 runs the software programs stored in the memory 104 and module, so as to execute various functional applications and data processing, that is, to realize the above-mentioned method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory that is remotely located relative to the processor 102, and these remote memories may be connected to a computer terminal through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used to receive or transmit data via a network. The specific example of the above-mentioned network may include a wireless network provided by the communication provider of the computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

It should be noted that a real quantum computer has a hybrid structure, which consists of two parts: one is a classical computer, which is responsible for performing classical calculation and control; the other is a quantum device, which is responsible for running quantum programs and realizing quantum computing. The quantum program is a series of instruction sequences written in a quantum language such as QRunes that can be run on a quantum computer, which supports the operation of quantum logic gates and finally realizes quantum computing. Specifically, a quantum program is a series of instruction sequences that operate quantum logic gates in a certain sequence.

In practical applications, due to the limitation of the development of quantum device hardware, quantum computing simulations are usually required to verify quantum algorithms, quantum applications, etc. Quantum computing simulation is the process of simulating the quantum program corresponding to a specific problem using a virtual architecture built with the resources of an ordinary computer (that is, a quantum virtual machine). Often, quantum programs corresponding to specific problems need to be constructed. The quantum program referred to in the implementation mode of this specification refers to a program written in a classical language that characterizes qubits and their evolution, in which qubits, quantum logic gates, etc. related to quantum computing are represented by corresponding classical codes.

As an embodiment of quantum programs, quantum circuits are also called quantum logic circuits. They are the most commonly used general-purpose quantum computing models. They represent circuits that operate on qubits under an abstract concept. The components include qubits, circuits (timelines) , and various quantum logic gates, the results often need to be read out through quantum measurement operations.

Unlike traditional circuits, which are connected by metal wires to transmit voltage signals or current signals, in quantum circuits, the circuits can be regarded as connected by time, that is, the state of qubits evolves naturally with time, in the process according to The instruction of the Hamiltonian operator is operated until it encounters a logic gate.

A quantum program as a whole corresponds to a total quantum circuit, and the quantum program mentioned in this specification refers to the total quantum circuit, wherein the total number of qubits in the total quantum circuit is the same as the total number of qubits in the quantum program. It can be understood as: a quantum program can be composed of quantum circuits, measurement operations for qubits in quantum circuits, registers for saving measurement results, and control flow nodes (jump instructions). A quantum circuit can contain tens, hundreds or even thousands of Tens of thousands of quantum logic gate operations. The execution process of a quantum program is the process of executing all quantum logic gates according to a certain time sequence. It should be noted that timing refers to the time sequence in which a single quantum logic gate is executed.

It should be noted that in classical computing, the most basic unit is a bit, and the most basic control mode is a logic gate. The purpose of controlling a circuit can be achieved through the combination of logic gates. Similarly, the way to handle qubits is quantum logic gates. Quantum logic gates can be used to evolve quantum states. Quantum logic gates are the basis of quantum circuits. Quantum logic gates include single-bit quantum logic gates, such as Hadamard Gate (H gate, Hadema gate), Pauli-X gate (X gate), Pauli-Y gate (Y gate), Pauli-Z gate (Z gate), RX gate, RY gate, RZ gate, etc. etc.; multi-bit quantum logic gates, such as CNOT gates, CR gates, iSWAP gates, Toffoli gates, etc. Quantum logic gates are generally represented by unitary matrices, and unitary matrices are not only in the form of matrices, but also a kind of operation and transformation. Generally, the effect of a quantum logic gate on a quantum state is calculated by multiplying the left side of the unitary matrix by the matrix corresponding to the right vector of the quantum state.

Those skilled in the art can understand that in a classical computer, the basic unit of information is a bit, and a bit has two states of 0 and 1, and the most common physical implementation method is to represent these two states through the level. In quantum computing, the basic unit of information is the qubit. A qubit also has two states of 0 and 1, denoted as |0> and |1>, but it can be in the superposition state of the two states of 0 and 1, which can be expressed for Among them, a and b are complex numbers representing the amplitude (probability amplitude) of |0> state and |1> state, which is not available in classical bits. After measurement, the state of the qubit will collapse to a certain state (eigenstate, here is |0> state, |1> state), where the probability of collapse to |0> is |a| ² , The probability of collapsing to |1> is |b| ² , |a| ² + |b| ² =1, and |> is the Dirac symbol.

The quantum state refers to the state of a qubit, and its eigenstate is expressed in binary in a quantum algorithm (or called a quantum program). For example, a group of qubits is q0, q1, and q2, representing the 0th, 1st, and 2nd qubits, and the order from high to low is q2q1q0, and the quantum states of this group of qubits are 2 ³ eigenstates , the 8 eigenstates (determined states) refer to: |000>, |001>, |010>, |011>, |100>, |101>, |110>, |111>, each The eigenstates are consistent with the qubit bits, such as the |000> state, and 000 corresponds to q2q1q0 from high to low. In short, a quantum state is a superposition state composed of eigenstates. When the probability amplitude of other states is 0, it is in one of the definite eigenstates. In related technologies, solving fluid dynamics problems with numerical methods generally involves the solution of differential equations, which are involved in many scientific and technological fields. Therefore, it is very important to develop effective methods for solving differential equations.

It can be understood that, no matter the classical computer or the quantum computer is used to solve the differential equation, it is realized by establishing a data processing task, executing the processing process of the data processing task, and obtaining the processing result of the data processing task.

This specification proposes a processing method for data processing tasks, wherein the data processing tasks include solving general differential equations (including but not limited to partial differential equations containing time items) to solve the problem of spatial derivatives in the case of complex computational domain shapes. The problem of high resource consumption for numerical discreteness; it can further improve the utilization rate of existing prior data.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a data processing task processing method provided in an embodiment of this specification, which may include the following steps:

S101: Acquire data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes the differential equation.

Specifically, any equation that expresses the relationship between the unknown function, the derivative of the unknown function, and the independent variable is called a differential equation. Among them, the unknown function is a one-variable function, called an ordinary differential equation; the unknown function is a derivative of a multivariate function containing an unknown function, called a partial differential equation, for example etc. are differential equations. Exemplarily, the data processing task may include solving the following differential equations:

Among them, u is the data processing result of the data processing task, that is, the solution of the differential equation, and x is a variable.

S102: Construct a differentiable quantum circuit according to the data processing task data.

Specifically, in order to clearly illustrate the idea of the technical solution in this specification, the differentiable quantum circuit will first be introduced in detail below, and then the technical solution will be described on this basis. For the construction of differentiable quantum circuits, the following steps can be included:

Step 1: Obtain a group of qubits and set the initial state of the qubits to |0>.

Step 2: using the first type of quantum logic gates to construct the first sub-quantum circuit module that generates the basis set of the function space.

Specifically, use the first type of quantum logic gates to construct the first sub-quantum circuit module for obtaining the basis set of the function space, which is used to convert the predefined nonlinear function The magnitude of the quantum state transformed into the initial state As a function space basis group, where, N is the number of qubits, j is the serial number of the qubit, is the Ry quantum logic gate on the jth qubit.

Exemplary, assume a predefined nonlinear function and bring in get:

Among them, T _n (x) and U _n (x) are Chebyshev n-degree polynomials of the first and second kind respectively, and they have three very key properties, which are linkability, nesting, and easiness of differentiability, These characteristics greatly enrich the characterization capabilities of Chebyshev polynomial basis sets, specifically:

Linkability: 2T _n (x)T _m (x)=T _m+n (x)+T _|mn| (x)

Nesting: T _n (T _m (x)) = T _mn (x)

Differentiability:

Correlation approximation theory points out that any smooth function f(x) can be expressed as

Wherein, the first type of quantum logic gates may include: Rx quantum logic gates, Ry quantum logic gates and Rz quantum logic gates.

Step 3: Using the second type of quantum logic gates, constructing a second sub-quantum circuit module for combining function space basis sets into predicted solutions of differential equations.

Specifically, use the second type of quantum logic gates to construct the second sub-quantum circuit module for combining the function space basis set into the predicted solution of the differential equation, so that the quantum state amplitude of the initial state into the final quantum state and solve the predicted solution of the differential equation according to the final quantum state, where, is the unitary matrix corresponding to the second sub-quantum circuit module.

Among them, the second sub-quantum circuit may include but not limited to: Hardware Efficient Ansatz (HEA) circuit and Alternating Blocks Ansatz (ABA) circuit, HEA consists of a single quantum rotating connection layer and global entanglement Layer composition, with the deepening of the number of layers, the expressive ability of the circuit is constantly improving, and it will also increase the difficulty of training the circuit; unlike HEA, ABA does not use the global entanglement layer, but divides the circuit into multiple sub-blocks , and use HEA-style circuits in sub-blocks, that is, ABA first establishes local entanglement, and then gradually forms correlated states by interleaving sub-blocks, which helps improve the trainability of circuits and maintains a high degree of expressiveness , and prevent the phenomenon of gradient disappearance in the iterative process.

Wherein, the second type of quantum logic gates may include: Rx quantum logic gates, Ry quantum logic gates, Rz quantum logic gates and CNOT quantum logic gates.

Step 4: Build the measurement operation module for obtaining the predicted solution of the differential equation.

Specifically, a measurement operation module acting on the qubit is constructed to measure the final quantum state of the qubit.

Step 5: sequentially combining the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module to obtain a differentiable quantum circuit.

Specifically, the first sub-quantum circuit module, the second sub-quantum circuit module, and the measurement operation module are combined in turn to construct a schematic diagram of a differentiable quantum circuit as shown in Figure 3. The black dot and ⊕ icon in the figure represent CNOT Quantum logic gate, wherein, the black dot is on the control bit of the CNOT quantum logic gate, and ⊕ is on the target bit of the CNOT quantum logic gate.

After passing through the first sub-quantum circuit and the second sub-quantum circuit, it is necessary to read the information of the quantum circuit and obtain the final quantum state corresponding to the variational parameters.

S103: Predict the target processing result of the data processing task based on the differentiable quantum circuit, and when the value of the loss function constructed according to the predicted processing result obtained from the prediction meets the specified accuracy condition, make the The predicted processing result whose value of the above loss function meets the specified accuracy condition is used as the target processing result.

Predicting the target processing result of the data processing task based on the differentiable quantum circuit may include the following steps:

Step 1: Obtain the information of the differential equation to be solved and the initial conditions and boundary conditions satisfied by the solution of the differential equation to be solved.

Specifically, there are generally multiple solutions to differential equations, but when solving specific physical problems, the required solution must be selected from them. Therefore, additional conditions must be known, that is, initial conditions and boundary conditions. Following the above example, where u(x ₀ )=u ₀ can be used as an initial condition or a boundary condition.

Step 2: Constructing a differentiable quantum circuit and determining variational parameters in the differentiable quantum circuit.

Step 3: Predict the solution of the differential equation through the differentiable quantum circuit according to the initial condition and the boundary condition.

Specifically, predicting the solution of the differential equation through the differentiable quantum circuit may include:

a. Obtain a pre-selected measurement operator.

After going through the above differentiable quantum circuits, the final state is obtained In order to read the quantum state information, the measurement operator is required Measure the final state to obtain a predicted solution to the differential equation The key to this process is the preselection of the measurement operator Optionally, but not limited to, the magnetization of the entire system can be selected as the measurement operator Right now Among them, Z is the Pauli operator; in addition, the Ising Hamiltonian with additional transverse and longitudinal magnetic fields can also be selected as the measurement operator.

b. Determine the expected value corresponding to the measurement operator according to the final quantum state.

Specifically, according to the final quantum state Determine the measurement operator corresponding expected value Among them, the expected value

c. Determining a predicted solution of said differential equation based on said expected value.

Specifically, the expected value Determine the predicted solution f(x) for the differential equation.

Exemplarily, to solve the above differential equation, for the convenience of illustration, only one qubit circuit is used for demonstration, and another differentiable quantum circuit schematic diagram is constructed as shown in Figure 4, and the measurement operator The output result of the quantum circuit after the measurement operation is the predicted solution f(x).

Among them, when the predefined nonlinear function When , according to the expression of the function space basis set generated by the first sub-quantum circuit The formula is:

Utilizing Rx and Rz quantum logic gates to construct a second sub-quantum circuit for combining the above-mentioned function space basis sets into a predicted solution of the differential equation, and combining the above-mentioned function space basis sets into the prediction solution of the differential equation according to the second sub-quantum circuit The expression is:

So the final quantum state is:

Through the measurement operation module, after the measurement operation, the output result of the quantum circuit, that is, the predicted solution f(x) is:

Constructing a loss function according to the prediction processing result obtained from the prediction may include:

The form of constructing the loss function is as follows:

Wherein, the L _θ ^(diff) represents that the prediction processing result does not satisfy the error of the above differential equation, the d _x f is the derivative term in the above differential equation, and the f is the function to be found of the above differential equation, so Said x represents a variable, said L _θ ^(boundary) represents the error that said prediction processing result does not satisfy boundary conditions and initial conditions, said is the regular term error.

Exemplarily, following the above example, f(x) is the prediction processing result obtained by predicting the above differential equation. In the initial stage, since the prediction processing result does not satisfy the differential equation and the initial conditions, the following loss function will be generated:

in,
L _θ ^(boundary) [f, x]=(f(x ₀ )-u ₀ ) ²

Among them, M is the number of regular points, u ^reg ( _xi ) is the value of known regular points, and the definition of F[d _x f( _xi ), f( _xi ), _xi ] in the above formula is as follows:

If the value of the loss function constructed according to the prediction processing result meets the preset accuracy condition, then the prediction processing result that makes the value of the loss function meet the specified accuracy condition is just the target processing result of the above differential equation; otherwise, it can be updated through the optimization algorithm Variational parameters in differentiable quantum circuits.

For example, using the traditional optimization method - gradient descent method, the variational parameter θ is updated by the following formula, namely Among them, n is an integer not less than 1, α is the learning rate, is the gradient of the loss function to θ, and L is the loss function.

Then, the updated variational parameters are passed to the differentiable quantum circuit, the evolution and measurement of the above steps are continued, and the prediction processing results and their corresponding loss functions are updated by continuously iterating the variational parameters until the value of the loss function is obtained The predicted processing results that meet the preset accuracy conditions are used as the target processing results of the differential equation.

Exemplarily, following the above example, after completing the construction of the loss function, the variational parameters θ ₁ , θ ₂ and θ ₃ as shown in Figure 4 need to be updated. The specific update method is as follows:

It can be seen from the above formula that the update of the variational parameter θ needs to calculate the gradient of the loss function to the variational parameter. The specific mathematical form is as follows:

Then, according to the gradient calculation method described in the above formula, the variational parameter θ can be updated; the updated variational parameter can be substituted into the loss function, and the updated loss function can be obtained; the variational parameter can be further updated according to the updated loss function , repeating the above process until the loss function satisfies the predetermined accuracy condition, that is, the prediction processing result that makes the loss function satisfy the preset accuracy condition is taken as the target processing result of the differential equation.

This embodiment is different from the traditional numerical calculation method to solve differential equations, because this method does not need to numerically discretize the spatial derivative, especially for the complex shape of the calculation domain, it can avoid the resource consumption caused by grid division; in addition, By adding a regular term to the loss function, the existing prior data can be fully utilized to achieve another way to solve the differential equation.

It can be seen that in this embodiment, the data of the data processing task is first obtained, and then a differentiable subcircuit is constructed according to the data processing task, and then the target processing result of the data processing task is predicted based on the differentiable subcircuit, until the result obtained according to the prediction is When the value of the loss function constructed by the prediction processing result meets the specified accuracy condition, the prediction processing result that makes the value of the loss function meet the specified accuracy condition is set as the target processing result. This embodiment can construct a loss function based on the predicted solution and update the value of the loss function to realize another processing method for solving the differential equation and reduce resource consumption caused by grid division.

Since the Differentiable Quantum Circuits (Differentiable Quantum Circuits, DQC) method has advantages in dealing with the task of solving nonlinear differential equations, one embodiment of this specification combines time discretization techniques with differentiable quantum circuits, and proposes a The method of using quantum computing to solve the task of solving time-dependent partial differential equations, on the one hand, solves the problem of increased line depth due to the existence of time variables; on the other hand, it also reduces the difficulty of variational parameter optimization.

Referring to FIG. 5, FIG. 5 is a schematic flowchart of a solution task of using a quantum circuit to process a time-dependent partial differential equation provided in this embodiment, which may include the following steps:

S201: Acquire data task data, wherein the data processing task is a task of solving a time-dependent partial differential equation; the data processing task data includes the time-dependent partial differential equation.

Partial differential equations are an important branch of modern mathematics. Both in theory and in practical applications, partial differential equations are used to describe problems in the fields of mechanics, control processes, ecological and economic systems, chemical cycle systems, and epidemiology. The use of partial differential equations to describe problems can fully take into account the influence of space, time, etc., so for partial differential equations that contain time items, they can be called time-dependent partial differential equations.

Specifically, the time-dependent partial differential equation is obtained as follows:

Wherein, the u(x,t) is the solution of the time-dependent partial differential equation, x is the space variable, t is the time variable, N'[u(x,t)] is the nonlinear item, Ω is the computational domain, T for time.

S202: Construct a differentiable quantum circuit according to the data processing task data.

For the differential format is the implicit differential format, constructing a differentiable quantum circuit may include the following steps:

S2021: Obtain a group of qubits and set the initial state of the qubits to |0>.

S2022: Using the first type of quantum logic gates, constructing a first sub-quantum circuit module that generates a function space basis set. The process of constructing the first sub-quantum circuit module is the same as the above-mentioned process of constructing the first sub-quantum circuit, and will not be repeated here.

S2023: Obtaining initial variational parameters and using the second type of quantum logic gates to construct a second sub-quantum circuit module for combining function space basis sets into predicted solutions of time-dependent partial differential equations. The process of constructing the second sub-quantum circuit module is the same as the aforementioned process of constructing the second sub-quantum circuit, and will not be repeated here.

S2024: Construct a measurement operation module for obtaining a prediction solution of the time-dependent partial differential equation.

Specifically, a measurement operation module acting on the qubit is constructed to measure the final quantum state of the qubit.

S2025: Combine the first sub-quantum circuit module, the second sub-quantum circuit module, and the measurement operation module in sequence to construct a differentiable quantum circuit.

Specifically, the first sub-quantum circuit module, the second sub-quantum circuit module, and the measurement operation module are combined in turn to construct a schematic diagram of a differentiable quantum circuit as shown in Figure 3. The black dot and ⊕ icon in the figure represent CNOT Quantum logic gates, where the black dots are in the CNOT On the control bit of the quantum logic gate, ⊕ is on the target bit of the CNOT quantum logic gate.

After passing through the first sub-quantum circuit and the second sub-quantum circuit, it is necessary to read the information of the quantum circuit and obtain the final quantum state corresponding to the initial variational parameters.

S203: Predict the target processing result of the time-dependent partial differential equation based on the initial variational parameters of the differentiable quantum circuit, until the value of the loss function at the current moment constructed according to the predicted processing result obtained from the prediction meets the specified accuracy condition In this case, the predicted solution of the time-dependent partial differential equation that makes the value of the loss function meet the specified accuracy condition is used as the target solution of the time-dependent partial differential equation.

Predicting the target processing result of the time-dependent partial differential equation based on the initial variational parameters of the differentiable quantum circuit may include the following steps:

Step 1: Determine the initial conditions and boundary conditions of the time-dependent partial differential equation.

Specifically, there are generally multiple solutions to time-dependent partial differential equations, but when solving specific physical problems, the required solution must be selected from them. Therefore, additional conditions must be known, that is, initial conditions and boundary conditions.

Exemplarily, for the above time-dependent partial differential equation to be solved, the initial condition can be preset as: u(x,0)=g(x), x∈Ω, and the boundary condition is preset as: u(x,t)= h(x,t), x ∈ Γ, where Γ represents the computational domain boundary.

Step 2: Determine the time-dependent partial differential equation in semi-discrete form according to a pre-selected differential scheme.

Specifically, the difference format is a discretization method that uses numerical methods to calculate the derivative of a function, that is, an algorithm that uses the difference between two or more adjacent numerical points to replace the derivative or partial derivative in the partial differential equation. The choice of the difference format is The first step in discretizing or semi-discretizing partial differential equations.

Exemplary, following the above example, the time item in the time-containing partial differential equation is firstly Discrete, get If the pre-selected differential format is an implicit differential format, the determination of the time-dependent partial differential equation in semi-discrete form according to the pre-selected differential format includes:

The time-dependent partial differential equation that determines the semi-discrete form is:
u(x,t+Δt)=u(x,t)+Δt N'[u(x,t+Δt)]

Wherein, the u(x, t+Δt) is the value of the function to be obtained at the time t+Δt.

Step 3: According to the initial conditions and boundary conditions, the solution of the time-dependent partial differential equation is predicted through the differentiable quantum circuit. The forecasting process is the same as the aforementioned forecasting process, and will not be repeated here.

Constructing a loss function at the current moment according to the prediction result obtained from the prediction may include:

If the current moment is the initial moment, the loss function at the initial moment is:

Wherein, the N' is the number of discrete points, M is the number of boundary points, i is the ith discrete point, j' is the j'th boundary point, is the predicted solution at the initial moment, g( _xi ) is the initial condition, h(x _j' ,0) is the boundary condition at the initial moment;

If the current moment is △t time, then the loss function at △t time is:

Wherein, the N'[u(x,t)] is determined according to the differentiable quantum circuits at the previous moment.

The value of the loss function at the current moment constructed according to the prediction processing results meets the preset accuracy conditions, specifically:

According to the predicted solution of the time-dependent partial differential equation, the target solution of the time-dependent partial differential equation is obtained, mainly by using the pre-selected measurement operator acting on the final quantum state , the predicted solution of the time-dependent partial differential equation at the current moment can be obtained And substitute the predicted solution at the current moment into the loss function at the corresponding moment to determine whether the value of the loss function meets the preset accuracy conditions, where the preset accuracy conditions can be set by the user according to the calculation requirements, for example, 10 ^-6 or 0 .

If the value of the loss function at the current moment constructed according to the predicted solution meets the preset accuracy conditions, then the predicted solution that makes the value of the loss function meet the specified accuracy conditions is just the target solution of the above time-dependent partial differential equation; otherwise, through the optimization algorithm Update variational parameters in differentiable quantum circuits.

For example, using the traditional optimization method - gradient descent method, the variational parameter θ is updated by the following formula, namely Among them, n is an integer not less than 1, α is the learning rate, is the gradient of the loss function to θ, and L is the loss function.

Then, the updated variational parameters are passed to the differentiable quantum circuit, and the evolution and measurement of the above steps are continued, and through continuous iterative variation Update the prediction solution and its corresponding loss function by sub-parameters until the prediction solution that makes the value of the loss function meet the preset accuracy conditions is obtained, which is used as the target solution of the above time-dependent partial differential equation.

Exemplarily, obtain the above time-dependent partial differential equation, construct a differentiable quantum circuit and obtain the initial prediction solution of the time-dependent partial differential equation corresponding to the initial variational parameters, obtain the loss function at the initial moment, and calculate the value of the loss function at the initial moment If it does not meet the preset accuracy of 10 ^-6 , update the variational parameters in the differentiable quantum circuit through the optimization algorithm to make it meet the given initial conditions and boundary conditions in the time-dependent partial differential equation to be solved until the initial prediction solution Satisfy the initial conditions and boundary conditions given by the time-dependent partial differential equation above, that is, the loss function at the initial moment satisfies the preset accuracy condition, because is an approximate function of u(x,0), so according to the time-dependent partial differential equation, the loss function at time Δt is obtained, namely:

By optimizing the loss function at time △t given in the formula, the approximate value of the predicted solution at time △t can be obtained in Determine according to the differentiable quantum circuit at the current moment, then update the variational parameters (parameters in the proposed design), and finally repeat the above steps until the predicted solution u of the time-dependent partial differential equation to be solved at a given time T is obtained (x,T).

In this embodiment, the time-dependent partial differential equation is semi-discretized and transformed into a semi-discrete time-dependent partial differential equation, and by constructing a differentiable quantum circuit and obtaining the predicted solution of the time-dependent partial differential equation corresponding to the initial variational parameters, the The classical data structure is connected with the quantum state in the quantum field, and the evolution operation of the classical data structure encoding to the quantum state is performed, and the quantum state of the evolved quantum circuit is obtained, which can use the superposition characteristics of the quantum to speed up processing. The high processing speed of solving tasks of time-dependent partial differential equations expands the simulation application scenarios of quantum computing.

The DQC method has advantages in solving ordinary differential equations and ordinary differential equations, but because the differentiable quantum circuit model needs to use the parameter shift method to calculate the values of the derivatives in the differential equation many times, the calculation efficiency may be reduced.

In related technologies, when differentiable quantum circuits are used to solve the task of solving differential equations, the solution of the differential equation in the entire computational domain is directly solved. This approach has two disadvantages. One is that the nature of the solution is more complicated As far as differential equations are concerned, the method of directly processing the solving tasks in the entire computational domain may lead to low accuracy of solutions in some areas. The expressive ability of the differentiable quantum circuit algorithm under the condition of differentiable quantum circuit algorithm, that is, the ability to approximate the solution of the differential equation is limited; the second is that even under the condition that the expressive ability of the differentiable quantum circuit algorithm is sufficient, when the properties of the solution are relatively When it is complex, directly dealing with the task of solving differential equations in the entire calculation domain will also cause optimization difficulties. This is mainly caused by two reasons. Variational parameters cause difficulties. On the other hand, under the condition of a certain distribution density of discrete points, the larger the area, the more the number of discrete points, and the more constraints on the predicted solution (the predicted solution at each discrete point must be satisfies the differential equation and boundary conditions), which also leads to low optimization efficiency.

Therefore, an embodiment of this specification combines domain decomposition technology with differentiable quantum circuits, and proposes a method for solving differential equations using quantum computing. The differential quantum circuit is solved on the entire computational domain, resulting in a waste of computational resources; on the other hand, it also effectively improves the computational efficiency of optimizing variational parameters.

Referring to FIG. 6, FIG. 6 is a schematic flowchart of a method for solving differential equations based on computational domain decomposition provided in this embodiment, which may include the following steps:

S301: Acquire data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes the differential equation.

Specifically, the differential equation to be solved is:

Wherein, the F is a functional function, the d _x u is a derivative term, the u is the solution of the differential equation to be solved, the x is a variable, and u(x ₀ )=u ₀ can be an initial condition or a boundary condition .

S302: Obtain the computational domain of the differential equation and divide the computational domain into several non-overlapping sub-calculation domains.

Specifically, the computational domain Ω of the differential equation is decomposed into non-overlapping sub-regions Ω ₁ , Ω ₂ , ..., Ω _n , that is, Ω=Ω ₁ ∪Ω ₂ ∪...∪Ω _n , where Ω _i ∈ Ω, i ∈ 1, 2, ..., n.

Exemplarily, for the following differential equation:

Wherein, the u(x,t) is the solution of the differential equation, x is the space variable, t is the time variable, N'[u(x,t)] is the non-linear item, Ω is the computational domain, T is the time, Divide the computational domain Ω into four non-overlapping sub-computational domains as shown in Figure 7. It should be noted that there are generally multiple solutions to differential equations, but when solving specific physical problems, one must select the required The solution of , therefore, must also know the additional conditions, that is, the boundary conditions.

S303: Construct differentiable quantum circuits corresponding to sub-regions, wherein one sub-computational domain corresponds to one predicted solution of the differential equation. Specifically, the process of constructing a differentiable quantum circuit corresponding to a sub-region is the same as the process of constructing a differentiable quantum circuit in the previous two embodiments, and will not be repeated here.

S304: Predict the target solution of the differential equation based on the differentiable quantum circuit, until the value of the joint loss function of the sub-calculation domain constructed according to the predicted solution of the differential equation meets the specified accuracy condition, the value of the loss function will meet The predicted solution of the differential equation for the specified accuracy condition serves as the target solution of the differential equation. The prediction process is basically the same as the prediction process in the foregoing embodiments, and will not be repeated here.

It should be noted that, according to the above method, construct a differentiable quantum circuit corresponding to a sub-region and obtain a predicted solution of the differential equation, determine the number of sub-computational domains that do not overlap with each other in the calculation domain division of the differential equation, and construct the sub-computational domain as shown in Figure 8. The corresponding number of differentiable quantum circuit schematic diagrams are shown, where f _i represents the predicted solution of the sub-computational domain Ω _i , and the predicted solutions f _i of each sub-computational domain are independent of each other. Among them, for solving practical problems, the flow is more complicated or differential In areas where the nature of the equation solution is relatively complex, a differentiable quantum circuit with a large number of qubits and a deep circuit can be used.

The joint loss function constructed according to the predicted solution of the differential equation may include:

Wherein, the n is the number of sub-regions, the n _b is the number of interfaces, and the L _i ^(diff) [d _x f _i , f _i , x] is the predicted solution of the i-th sub-region does not satisfy the differential equation but The error caused by L _i ^(boundary) [f _i , x] is the error caused by the prediction solution of the i-th sub-region not satisfying the boundary conditions, and the L _i ^(interface) [f, x] is the error caused by the i-th sub-region The error caused by the prediction solution of the adjacent areas on both sides of the interface does not meet the continuity condition of the interface and The n _i is the number of discrete points on the i-th interface, and f ⁺ (x _j ) and f ^- (x _j ) are the predicted solutions corresponding to the sub-regions on both sides of the interface.

Whether the value of the joint loss function constructed according to the predicted solution of the differential equation meets the accuracy, specifically:

According to the predicted solution f _i of the differential equation in each sub-computation domain, the target solution of the differential equation is obtained, which is mainly by using the pre-selected measurement operator When acting on each final quantum state |f _i (x)>, each predicted solution f _i (x) of the differential equation is obtained, and the predicted solution is substituted into the joint loss function to determine whether the value of the joint loss function meets the preset accuracy conditions , where the preset accuracy condition can be set by the user according to the calculation requirements, for example, 10 ^-6 or 0.

If the value of the joint loss function constructed according to the predicted solution meets the preset accuracy condition, then the predicted solution that makes the value of the loss function meet the specified accuracy condition is just the target solution of the above differential equation; otherwise, the differentiable quantum is updated by the optimization algorithm Variational parameters in the circuit. The optimization process of variational parameters is basically the same as the optimization process of variational parameters in the foregoing embodiments, and will not be repeated here.

In this embodiment, by constructing a differentiable quantum circuit and predicting the solution of the differential equation according to the differentiable quantum circuit, the classical data structure is connected with the quantum state in the quantum field, and the evolution operation of encoding the classical data structure into the quantum state is performed. , to obtain the quantum state of the evolved quantum circuit, which can use the quantum superposition property to speed up the processing speed of the task of solving the differential equation with high complexity, and expand the simulation application scenarios of quantum computing.

For the situation that the parameter shift method of DQC cannot be used to calculate the derivative, the numerical method is used to calculate the derivative, which will bring numerical errors.

Referring to FIG. 9, FIG. 9 is a schematic flowchart of a method for using a quantum circuit to solve a differential equation solution task provided by an embodiment of this specification, which may include the following steps:

S401: Acquire data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes the differential equation and a derivative term of the differential equation.

It should be noted that, in this embodiment, the solution of the predicted value of the derivative term of the differential equation in the task of solving the differential equation by using the differentiable quantum circuit described below, wherein the derivative term of the differential equation can be divided into different categories, such as the derivative term and can be classified into one class, the derivative term and Belonging to one class, etc., the key to its classification is whether the same section of differentiable quantum circuit can be used to solve the predicted value of the derivative term.

Specifically, determine the differential equation to be solved as:

Wherein, the F is a functional function, the d _x u is a derivative term, the u is the predicted solution of the differential equation to be solved, the x is a variable, and u(x ₀ )=u ₀ can be an initial condition or a boundary condition.

Exemplarily, for a specific differential equation to be solved:

in, is the derivative term, u is the solution of the differential equation to be solved, and x is the variable.

S402: Construct differentiable quantum circuits respectively according to the differential equation and derivative terms of the differential equation.

Specifically, constructing a differentiable quantum circuit according to the differential equation and the derivative term of the differential equation may include:

The first differentiable quantum circuit is constructed according to the above differential equation, the second differentiable quantum circuit is constructed according to the derivative term of the above differential equation, and the predicted solution and the second differential equation corresponding to the current variational parameters in the first differentiable quantum circuit are respectively determined. The current variational parameter in the differentiable quantum circuit corresponds to the predicted value of the derivative term of the differential equation. The construction process of respectively constructing the first and second differentiable quantum circuits according to the above differential equation and the derivative terms of the above differential equation is basically the same as that of the foregoing embodiment, and will not be repeated here.

S403: Predict the target solution of the differential equation and the value of the derivative term of the differential equation based on the differentiable quantum circuit, until the value of the loss function constructed based on the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation meets the specified accuracy In the case of the condition, the predicted solution of the differential equation that makes the value of the loss function meet the specified accuracy condition is taken as the target solution of the differential equation. The prediction process is basically the same as the prediction process in the foregoing embodiments, and will not be repeated here.

It should be noted that a differentiable subcircuit corresponding to each type of derivative term is constructed according to the above method and the predicted value of the derivative term is obtained, wherein the predicted value of each type of derivative term is represented separately, and the predicted values of each derivative term are mutually correlated independent.

According to the predicted solution of the differential equation obtained based on the differentiable quantum circuit and the predicted value of the derivative term of the differential equation, the loss function constructed is:
L[f ⁱ , f, x]=L ^(diff) [f ⁱ , f, x]+L ^(boundary) [f, x]

Wherein, said f ⁱ represents the i-th order derivative term in the differential equation, said L ^(diff) represents that said predicted solution does not satisfy the error of the differential equation, said f is said differential equation, and said x represents a variable, The L ^(boundary) represents the error that the predicted solution does not satisfy the boundary conditions and initial conditions.

Whether the value of the loss function constructed based on the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation meets the preset accuracy conditions, specifically:

According to the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation obtained in each differentiable quantum circuit, the target solution of the differential equation is obtained, which is mainly by using the pre-selected measurement operator When acting on each final quantum state, each predicted solution f _i (x) of the differential equation and each predicted value f′ _i (x) of the derivative term are obtained, and the predicted solution and predicted value are substituted into the loss function to determine the value of the loss function Whether it meets the preset accuracy condition, where the preset accuracy condition can be set by the user according to the calculation requirements, for example, 10 ^-6 or 0.

If the value of the loss function constructed according to the predicted solution and the predicted value meets the preset accuracy condition, then the predicted solution that makes the value of the loss function meet the specified accuracy condition is just the target solution of the differential equation; Variational parameters in quantum circuits. The optimization process of variational parameters is basically the same as the optimization process of variational parameters in the foregoing embodiments, and will not be repeated here.

In this embodiment, by constructing a differentiable quantum circuit and predicting the solution of the differential equation and the value of the derivative term of the differential equation according to the differentiable quantum circuit, the classical data structure is connected with the quantum state in the quantum field, and the classical data structure is executed. The evolution operation of structure encoding to quantum state obtains the quantum state of the evolved quantum circuit, which can take advantage of the superposition characteristics of quantum to speed up the processing speed of solving tasks of highly complex differential equations and expand the simulation application scenarios of quantum computing.

Referring to FIG. 10, FIG. 10 is a schematic structural diagram of a data processing task processing device provided in an embodiment of this specification, corresponding to the process shown in FIG. 2, and the device may include:

An acquisition module 501, configured to acquire data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes the differential equation;

A construction module 502, configured to construct a differentiable quantum circuit according to the data processing task data;

The prediction module 503 is configured to predict the target processing result of the data processing task based on the differentiable quantum circuit, and when the value of the loss function constructed according to the predicted prediction processing result meets the specified accuracy condition, the loss The predicted processing result whose value of the function meets the specified accuracy condition is used as the target processing result.

Specifically, the acquisition module 501 includes:

The acquisition unit is used to acquire the information of the differential equation and the initial conditions and boundary conditions satisfied by the solution of the differential equation.

The building block 502 includes:

a construction unit for constructing a differentiable quantum circuit and determining variational parameters in the differentiable quantum circuit;

Specifically, the building units include:

The first acquisition subunit is used to acquire a group of qubits and set the initial state of the qubits to |0>;

The first construction sub-unit is used to construct the first sub-quantum circuit module of the generating function space basis set by using the first type of quantum logic gate;

The second construction subunit is used to construct a second sub-quantum circuit module for combining function space basis sets into predicted solutions of differential equations by using the second type of quantum logic gates;

The third construction subunit is used to construct the measurement operation module for obtaining the prediction solution of the differential equation;

The combination subunit is used to sequentially combine the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module to obtain a differentiable quantum circuit.

The prediction module 503 includes:

a prediction unit, configured to obtain a predicted solution of the differential equation through the differentiable quantum circuit according to the initial condition and the boundary condition;

A determining unit, configured to determine that the loss function constructed according to the predicted solution of the differential equation meets a preset accuracy condition;

The update unit is used to update the variational parameters through an optimization algorithm for the predicted solution of the differential equation whose loss function does not meet the preset accuracy condition, and obtain the predicted solution of the differential equation corresponding to the updated variational parameter.

Specifically, the update unit includes:

The update subunit is used to update the variational parameter θ through the following formula:

Wherein, the n is an integer not less than 1, α is the learning rate, is the gradient of the loss function to θ, and L is the loss function.

The embodiments of this specification also provide a storage medium, where a computer program is stored in the storage medium, wherein the computer program is configured to execute the steps in any one of the above embodiments when running.

Specifically, in this embodiment, the above-mentioned storage medium may include but not limited to: U disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), mobile hard disk, magnetic disk Or various media such as optical discs that can store computer programs.

The implementation mode of this specification also provides an electronic device, including a memory and a processor, the computer program is stored in the memory, and the processor is configured to run the computer program to perform any one of the above method embodiments A step of.

Specifically, the electronic device may further include a transmission device and an input and output device, wherein the transmission device is connected to the processor, and the input and output device is connected to the processor.

The structure, features and effects of the present invention have been described in detail above based on the embodiments shown in the drawings. The above descriptions are only preferred embodiments of the present invention, but the present invention does not limit the scope of implementation as shown in the drawings. Changes made to the idea of the present invention, or modifications to equivalent embodiments that are equivalent changes, and still within the spirit covered by the description and illustrations, shall be within the protection scope of the present invention.

Claims (36)

1. A processing method for a data processing task, characterized in that the method comprises:

   Acquiring data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes the differential equation;

   Constructing a differentiable quantum circuit according to the data processing task data;

   Predict the target processing result of the data processing task based on the differentiable quantum circuit, and when the value of the loss function constructed according to the predicted processing result meets the specified accuracy condition, the loss The predicted processing result whose value of the function meets the specified accuracy condition is used as the target processing result.
2. The method according to claim 1, wherein predicting the target processing result of the data processing task based on the differentiable quantum circuit comprises:

   A target solution to the differential equation is predicted based on variational parameters in the differentiable quantum circuit.
3. The method according to claim 1, wherein the data processing task data also includes initial conditions and boundary conditions satisfied by the solution of the differential equation; the step of constructing a differentiable quantum circuit according to the data processing task data ,include:

   The differentiable quantum circuit is constructed according to the differential equation and the initial conditions and boundary conditions satisfied by the solution of the differential equation, and the variational parameters in the differentiable quantum circuit are determined.
4. The method according to claim 3, wherein constructing the differentiable quantum circuit according to the differential equation and the initial conditions and boundary conditions satisfied by the solution of the differential equation includes:

   Obtain a group of qubits and set the initial state of the qubits to |0>;

   Using the first type of quantum logic gates, constructing the first sub-quantum circuit module that generates the basis set of the function space;

   Constructing a second sub-quantum circuit module for combining functional space basis sets into predicted solutions of differential equations using a second class of quantum logic gates;

   constructing a measurement operation module for obtaining a predicted solution of said differential equation;

   Combining the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module in sequence to obtain the differentiable quantum circuit.
5. The method according to claim 2, wherein the step of predicting the target solution of the differential equation based on the variational parameters in the differentiable quantum circuit comprises:

   The variational parameters are updated by an optimization algorithm, and the target solution of the differential equation is predicted based on the updated variational parameters.
6. The method according to claim 5, wherein the updating of the variational parameters by an optimization algorithm comprises:

   The variational parameter θ is updated by the following formula:
   

   Wherein, the n is an integer not less than 1, α is the learning rate, and L is the loss function, is the gradient of the loss function to θ.
7. The method according to claim 6, wherein the loss function is:
   

   Wherein, the L _θ ^(diff) represents that the predicted solution of the differential equation does not satisfy the error of the differential equation, the d _x f represents the derivative term in the differential equation, and the f represents the difference between the differential equation and the differential equation Corresponding function, said x represents a variable, said L _θ ^(boundary) represents the error that the predicted solution of said differential equation does not satisfy said boundary condition and said initial condition, said Indicates the regularization term error.
8. The method according to claim 1, characterized in that, the differential equation is a time-dependent partial differential equation; the target processing result of the data processing task is predicted based on the differentiable quantum circuit, until the result is obtained according to the prediction When the value of the loss function constructed by the predicted processing result meets the specified accuracy condition, the steps of making the predicted processing result whose value of the loss function meets the specified accuracy condition are taken as the target processing result include:

   Predict the target solution of the time-dependent partial differential equation based on the initial variational parameters of the differentiable quantum circuit, until the value of the loss function at the current moment constructed according to the prediction processing result obtained from the prediction meets the specified accuracy condition In the case of , the predicted solution of the time-dependent partial differential equation that makes the value of the loss function meet the specified accuracy condition is used as the target solution of the time-dependent partial differential equation.
9. The method according to claim 8, wherein the time-dependent partial differential equation comprises:
   

   Wherein, the u(x, t) is the solution of the time-dependent partial differential equation, x is a space variable, t is a time variable, N'[u(x, t)] is a nonlinear term, and Ω is a computational domain , T is time.
10. The method according to claim 9, wherein the data processing task data also includes initial conditions and boundary conditions of the time-dependent partial differential equation;

    After the step of obtaining the data of the data processing task, also include:

    Said time-dependent partial differential equations are determined in semi-discrete form according to a preselected difference scheme.
11. The method according to claim 10, wherein the pre-selected differential scheme is an implicit differential scheme; the step of determining the time-dependent partial differential equation in semi-discrete form according to the pre-selected differential scheme comprises: :

    The time-dependent partial differential equation that determines the semi-discrete form is:

    u(x,t+Δt)=u(x,t)+Δt N'[u(x,t+Δt)]

    Wherein, the u(x,t+Δt) is the solution of the time-dependent partial differential equation at time t+Δt.
12. The method according to claim 8, wherein the prediction of the target solution of the time-dependent partial differential equation based on the initial variational parameters of the differentiable quantum circuit comprises:

    Obtain a group of qubits and set the initial state of the qubits to |0>;

    Using the first type of quantum logic gates, constructing the first sub-quantum circuit module that generates the basis set of the function space;

    Obtaining initial variational parameters and using quantum logic gates of the second type to construct a second sub-quantum circuit module for combining function-space basis sets into predicted solutions of time-dependent partial differential equations;

    Constructing a measurement operation module for obtaining a predicted solution of the time-dependent partial differential equation;

    Combining the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module in turn to construct the differentiable quantum circuit, and determine according to the final quantum state corresponding to the obtained initial variational parameters A predicted solution of the time-dependent partial differential equation.
13. The method according to claim 12, wherein said determining the predicted solution of said time-dependent partial differential equation comprises:

    Get the pre-selected measurement operator;

    determining an expected value corresponding to the measurement operator according to the final quantum state;

    A predicted solution to the time-dependent partial differential equation is determined based on the expected value.
14. The method according to claim 12, wherein the first type of quantum logic gates comprises: Rx quantum logic gates, Ry quantum logic gates and Rz quantum logic gates;

    The second type of quantum logic gates includes: Rx quantum logic gates, Ry quantum logic gates, Rz quantum logic gates and CNOT quantum logic gates.
15. The method according to claim 8, wherein the loss function at the current moment constructed according to the prediction processing result obtained by the prediction includes:

    If the current moment is the initial moment, the loss function at the initial moment is:
    

    Wherein, the N' is the number of discrete points, M is the number of boundary points, i is the ith discrete point, j' is the j'th boundary point, is the prediction processing result at the initial moment, g( _xi ) is the initial condition, h(x _j' ,0) is the boundary condition at the initial moment;

    If the current moment is △t time, then the loss function at △t time is:
    

    Wherein, the N'[u(x,t)] is determined according to the differentiable quantum circuits at the previous moment.
16. The method according to claim 8, characterized in that the method further comprises:

    The variational parameter θ is updated by the following formula:
    

    Wherein, the n is an integer not less than 1, α is the learning rate, and L is the loss function, is the gradient of the loss function to θ.
17. The method according to claim 1, characterized in that, after the step of acquiring data processing task data, the method further comprises:

    Obtaining the computational domain of the differential equation and dividing the computational domain into several non-overlapping sub-computational domains;

    The step of constructing a differentiable quantum circuit according to the data processing task data includes:

    Constructing a differentiable quantum circuit corresponding to the sub-computation domain; wherein, one sub-computation domain corresponds to one predicted solution of the differential equation;

    Predict the target processing result of the data processing task based on the differentiable quantum circuit, and when the value of the loss function constructed according to the predicted processing result obtained from the prediction meets the specified accuracy condition, the loss function will be The steps of using the predicted processing result whose value meets the specified accuracy condition as the target processing result include:

    Predict the target solution of the differential equation based on the differentiable quantum circuit, and when the value of the joint loss function of the sub-calculation domain constructed according to the predicted solution of the differential equation meets the specified accuracy condition, The predicted solution of the differential equation that makes the value of the loss function meet the specified accuracy condition is used as the target solution of the differential equation.
18. The method according to claim 17, wherein the differential equation is:
    

    Wherein, the F is a functional function, the d _x u is a derivative term, the u is the solution of the differential equation, and the x is a variable.
19. The method according to claim 17, wherein predicting the target processing result of the data processing task based on the differentiable quantum circuit comprises:

    A prediction is made based on a target solution of the differential equation for variational parameters in the differentiable quantum circuit.
20. The method according to claim 19, wherein the step of constructing a differentiable quantum circuit corresponding to the sub-computational domain includes:

    Obtain a group of qubits and set the initial state of the qubits to |0>;

    Using the first type of quantum logic gates, constructing the first sub-quantum circuit module that generates the basis set of the function space;

    Constructing a second sub-quantum circuit module for combining functional space basis sets into predicted solutions of differential equations using a second class of quantum logic gates;

    constructing a measurement operation module for obtaining a predicted solution of said differential equation;

    Combining the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module in sequence to construct the differentiable quantum circuit.
21. The method according to claim 20, wherein the step of predicting the target solution of the differential equation based on the variational parameters in the differentiable quantum circuit comprises:

    Get the pre-selected measurement operator;

    determining the expected value corresponding to the measurement operator according to the final quantum state corresponding to the variational parameter in the differentiable quantum circuit;

    A predicted solution to the differential equation is determined based on the expected value.
22. The method according to claim 20, wherein the first type of quantum logic gates comprises: Rx quantum logic gates, Ry quantum logic gates and Rz quantum logic gates;

    The second type of quantum logic gates includes: Rx quantum logic gates, Ry quantum logic gates, Rz quantum logic gates and CNOT quantum logic gates.
23. The method according to claim 17, wherein the joint loss function is:
    

    Wherein, the n is the number of sub-computational domains, the n _b is the number of interfaces, and the L _i ^(diff) [d _x f _i , f _i , x] is the differential equation corresponding to the i-th sub-computational domain The error caused by the prediction solution of the differential equation does not satisfy the differential equation, the L _i ^(boundary) [f _i , x] is the prediction solution of the differential equation corresponding to the i-th sub-calculation domain does not satisfy the differential equation The error caused by solving the boundary conditions satisfied, the L _i ^(interface) [f, x] is the predicted solution of the differential equation corresponding to the adjacent areas on both sides of the i-th interface does not satisfy the interface continuity condition errors caused by The n _i is the i-th junction The number of discrete points on the surface, f ⁺ (x _j ) and f ^- (x _j ) are the predicted solutions of the differential equations corresponding to the sub-computational domains on both sides of the interface.
24. The method according to claim 19, wherein the step of predicting the target solution of the differential equation based on the variational parameters in the differentiable quantum circuit comprises:

    updating variational parameters in the differentiable quantum circuit using an optimization algorithm;

    An updated predicted solution of the differential equation is obtained according to the updated variational parameters in the differentiable quantum circuit.
25. The method according to claim 24, wherein the step of using an optimization algorithm to update the variational parameters in the differentiable quantum circuit comprises:

    The variational parameter θ is updated by the following formula:
    

    Wherein, the n is an integer not less than 1, α is the learning rate, and L is the joint loss function, is the gradient of the joint loss function to θ.
26. The method according to claim 1, wherein the data processing task data also includes a derivative term of the differential equation;

    The step of constructing a differentiable quantum circuit according to the data processing task data includes:

    constructing the differentiable quantum circuits respectively according to the differential equation and derivative terms of the differential equation;

    Predict the target processing result of the data processing task based on the differentiable quantum circuit, and when the value of the loss function constructed according to the predicted processing result meets the specified accuracy condition, the loss The steps of using the predicted processing result whose value of the function meets the specified precision condition as the target processing result include:

    Predict the target solution of the differential equation and the value of the derivative term of the differential equation based on the differentiable quantum circuit, and construct according to the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation When the value of the loss function meets the specified accuracy condition, the predicted solution of the differential equation that makes the value of the loss function meet the specified accuracy condition is used as the target solution of the differential equation.
27. The method of claim 26, wherein

    The differential equation is:
    

    Wherein, the F is a functional function, the d _x u is a derivative term of the differential equation, the u is a predicted solution of the differential equation, and the x is a variable.
28. The method according to claim 26, wherein the step of constructing the differentiable quantum circuit according to the differential equation and the derivative term of the differential equation respectively comprises:

    Constructing the differentiable quantum circuit for solving the predicted solution of the differential equation and the predicted value of the derivative term for solving the differential equation, respectively, and determining the corresponding to the variational parameters in the differentiable quantum circuit A predicted solution to the differential equation and predicted values of derivative terms of the differential equation.
29. The method according to claim 28, wherein said constructing respectively a differentiable quantum circuit for solving the predicted solution of the differential equation and for solving the predicted value of the derivative term comprises:

    Obtain a group of qubits respectively and set the initial state of the qubits to |0>;

    Using the first type of quantum logic gates, respectively constructing the first sub-quantum circuit modules that generate function space basis sets;

    Using the second type of quantum logic gates, respectively constructing a second sub-quantum circuit module for combining the function space basis set into the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation;

    respectively constructing measurement operation modules for obtaining the predicted solution of the differential equation and the predicted value of the derivative term of the differential equation;

    Combining the first sub-quantum circuit module, the second sub-quantum circuit module and the measurement operation module respectively to construct the differentiable quantum circuit.
30. The method according to claim 29, wherein said respectively determining the predicted solution of the differential equation corresponding to the variational parameter in the differentiable quantum circuit and the predicted value of the derivative term of the differential equation comprises:

    Get the pre-selected measurement operator;

    determining the expected value corresponding to the measurement operator according to the final quantum state corresponding to the variational parameter in the differentiable quantum circuit;

    A predicted solution of the differential equation and a predicted value of a derivative term of the differential equation are determined based on the expected value.
31. The method according to claim 26, wherein the loss function is:

    L[f ⁱ , f, x]=L ^(diff) [f ⁱ , f, x]+L ^(boundary) [f, x]

    Wherein, the f ⁱ represents the i-th order derivative term in the differential equation, the L ^(diff) represents that the predicted solution of the differential equation does not satisfy the error of the differential equation, and the f is the differential equation , the x represents a variable, and the L ^(boundary) represents an error that the predicted solution of the differential equation does not satisfy the boundary conditions and initial conditions of the solution of the differential equation.
32. The method according to claim 28, wherein determining the predicted solution of the differential equation corresponding to the variational parameter in the differentiable quantum circuit and the predicted value of the derivative term of the differential equation respectively includes:

    updating variational parameters in the differentiable quantum circuit using an optimization algorithm;

    According to the updated variational parameters in the differentiable quantum circuit, the updated predicted solution of the differential equation and the predicted value of the derivative term of the differential equation are obtained.
33. The method according to claim 32, wherein the step of using an optimization algorithm to update the variational parameters in the differentiable quantum circuit comprises:

    The variational parameter θ is updated by the following formula:
    

    Wherein, the n is an integer not less than 1, α is the learning rate, and L is the loss function, is the gradient of the loss function to θ.
34. A processing device for data processing tasks, characterized in that the device includes:

    An acquisition module, configured to acquire data processing task data; wherein, the data processing task is a task of solving a differential equation; the data processing task data includes the differential equation;

    a building block, configured to build a differentiable quantum circuit according to the data processing task data;

    A prediction module, configured to predict the target processing result of the data processing task based on the differentiable quantum circuit, until the value of the loss function constructed according to the predicted processing result obtained from the prediction meets the specified accuracy condition; The prediction processing result that makes the value of the loss function conform to the specified accuracy condition is taken as the target processing result.
35. A storage medium, characterized in that a computer program is stored in the storage medium, wherein the computer program is configured to execute the method described in any one of claims 1 to 33 when running.
36. An electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to perform the process described in any one of claims 1 to 33. described method.