CN115630701B - System characteristic information determining method, device, electronic equipment and medium - Google Patents
System characteristic information determining method, device, electronic equipment and medium Download PDFInfo
- Publication number
- CN115630701B CN115630701B CN202211058584.9A CN202211058584A CN115630701B CN 115630701 B CN115630701 B CN 115630701B CN 202211058584 A CN202211058584 A CN 202211058584A CN 115630701 B CN115630701 B CN 115630701B
- Authority
- CN
- China
- Prior art keywords
- quantum circuit
- quantum
- parameters
- circuit
- values
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000004590 computer program Methods 0.000 claims abstract description 16
- 238000003860 storage Methods 0.000 claims abstract description 15
- 238000005259 measurement Methods 0.000 claims abstract description 13
- 230000009471 action Effects 0.000 claims abstract description 9
- 230000005283 ground state Effects 0.000 claims description 35
- 238000009826 distribution Methods 0.000 claims description 4
- 238000011478 gradient descent method Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 24
- 239000011159 matrix material Substances 0.000 description 18
- 239000002096 quantum dot Substances 0.000 description 18
- 238000012549 training Methods 0.000 description 10
- 238000004891 communication Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 239000000470 constituent Substances 0.000 description 6
- 238000005457 optimization Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 229910052744 lithium Inorganic materials 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000010365 information processing Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 244000055346 Paulownia Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 101100001674 Emericella variicolor andI gene Proteins 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000010267 cellular communication Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000012885 constant function Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000005305 interferometry Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 229910000103 lithium hydride Inorganic materials 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 229940050561 matrix product Drugs 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000007774 positive electrode material Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000002407 reforming Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
- G06N10/20—Models of quantum computing, e.g. quantum circuits or universal quantum computers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
- G06N10/60—Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mathematical Analysis (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Superconductor Devices And Manufacturing Methods Thereof (AREA)
Abstract
The disclosure provides a characteristic information determining method, a characteristic information determining device, an electronic device, a computer readable storage medium and a computer program product of a system, and relates to the field of computers, in particular to the technical field of quantum computers. The implementation scheme is as follows: determining a preset first quantum circuit for acquiring the initial value of the characteristic information; the following target operations are performed for a preset number of times: the preset second quantum circuit is connected in series to the output end of the first quantum circuit, and the second quantum circuit is close to the unit quantum circuit under the action of the initial value of a group of adjustable parameters; determining initial values of a set of disturbance parameters corresponding to a set of adjustable parameters to form a new set of parameter values; the whole quantum circuit acts on the initial quantum state, and disturbance parameter values are adjusted to minimize the measurement result; and taking the first quantum circuit and the second quantum circuit as new first quantum circuits; and determining characteristic information of the system based on the first quantum circuit obtained after the target operation of the preset times.
Description
Technical Field
The present disclosure relates to the field of computers, and in particular, to the field of quantum computer technology, and more particularly, to a characteristic information method, apparatus, electronic device, computer readable storage medium, and computer program product for a system.
Background
Currently, quantum computers are advancing toward scale and practical use. In the disciplines of physics and chemistry, a very important issue is solving the Ground State of physical systems such as molecules, atoms, crystals, etc. Therefore, how to extract ground state energy of a physical system using a quantum computer has become a research hotspot.
Disclosure of Invention
The present disclosure provides a system characteristic information method, apparatus, electronic device, computer readable storage medium, and computer program product.
According to an aspect of the present disclosure, there is provided a feature information determining method of a system, including: determining a preset first quantum circuit for acquiring the initial value of the characteristic information, wherein the first quantum circuit is determined based on the Hamiltonian amount of the system; the following target operations are performed for a preset number of times: the method comprises the steps of connecting a preset second quantum circuit in series to an output end of the first quantum circuit, wherein the second quantum circuit comprises a group of adjustable parameters, the group of adjustable parameters comprise corresponding initial values, and the second quantum circuit is close to a unit quantum circuit within a preset range under the action of the initial values of the group of adjustable parameters; determining initial values of a set of perturbation parameters corresponding to the set of adjustable parameters, wherein the perturbation parameters are for addition to the respective initial values of the set of adjustable parameters to form a new set of parameter values for the second quantum circuit; applying the first quantum circuit and a second quantum circuit comprising the new set of parameter values to an initial quantum state to measure the obtained quantum state to obtain a measurement result; adjusting values of the set of perturbation parameters to minimize the measurement; and taking the first quantum circuit and the minimized second quantum circuit as a new first quantum circuit; and determining characteristic information of the system based on the first quantum circuit obtained after the target operation of the preset times.
According to another aspect of the present disclosure, there is provided a feature information determining apparatus of a system, including: a first determining unit configured to determine a preset first quantum circuit for acquiring the initial value of the characteristic information, wherein the first quantum circuit is determined based on the hamiltonian amount of the system; an execution unit configured to execute the following target operations a preset number of times: the method comprises the steps of connecting a preset second quantum circuit in series to an output end of the first quantum circuit, wherein the second quantum circuit comprises a group of adjustable parameters, the group of adjustable parameters comprise corresponding initial values, and the second quantum circuit is close to a unit quantum circuit within a preset range under the action of the initial values of the group of adjustable parameters; determining initial values of a set of perturbation parameters corresponding to the set of adjustable parameters, wherein the perturbation parameters are for addition to the respective initial values of the set of adjustable parameters to form a new set of parameter values for the second quantum circuit; applying the first quantum circuit and a second quantum circuit comprising the new set of parameter values to an initial quantum state to measure the obtained quantum state to obtain a measurement result; adjusting values of the set of perturbation parameters to minimize the measurement; and taking the first quantum circuit and the minimized second quantum circuit as a new first quantum circuit; and a second determination unit configured to determine characteristic information of the system based on the first quantum circuit obtained after the target operation a preset number of times.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods described in the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method described in the present disclosure.
According to one or more embodiments of the present disclosure, first, the first quantum circuit with shallower is used to prepare the approximate feature information, and the second quantum circuit added later can not only keep the advantage of pre-training to the greatest extent, but also continuously increase the precision of the feature information through multiple iterative operations, so that the method has better expandability and trainability.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.
FIG. 1 illustrates a schematic diagram of a conventional parameterized quantum circuit in one embodiment;
FIG. 2 illustrates a flow chart of a method of determining feature information of a system according to an embodiment of the present disclosure;
Fig. 3 shows a schematic diagram of a structure of a second quantum circuit according to an embodiment of the present disclosure;
fig. 4 shows a block diagram of a feature information determination apparatus of a system according to an embodiment of the present disclosure; and
Fig. 5 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.
The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
To date, various types of computers in use are based on classical physics as the theoretical basis for information processing, known as traditional or classical computers. Classical information systems store data or programs using binary data bits that are physically easiest to implement, each binary data bit being represented by a 0 or a1, called a bit or a bit, as the smallest unit of information. Classical computers themselves have the inevitable weakness: first, the most basic limitation of energy consumption in the calculation process. The minimum energy required by the logic element or the memory cell should be more than several times of kT to avoid malfunction under thermal expansion; secondly, information entropy and heating energy consumption; thirdly, when the wiring density of the computer chip is large, the uncertainty of momentum is large when the uncertainty of the electronic position is small according to the uncertainty relation of the Hessenberg. Electrons are no longer bound and there is a quantum interference effect that can even destroy the performance of the chip.
Quantum computers (QWs) are a class of physical devices that perform high-speed mathematical and logical operations, store and process quantum information, following quantum mechanical properties, laws. When a device processes and calculates quantum information and a quantum algorithm is operated, the device is a quantum computer. Quantum computers follow unique quantum dynamics (particularly quantum interferometry) to achieve a new model of information processing. For parallel processing of computational problems, quantum computers have an absolute advantage in speed over classical computers. The transformation implemented by the quantum computer on each superposition component is equivalent to a classical computation, all of which are completed simultaneously and are superimposed according to a certain probability amplitude to give the output result of the quantum computer, and the computation is called quantum parallel computation. Quantum parallel processing greatly improves the efficiency of quantum computers so that they can perform tasks that classical computers cannot do, such as factorization of a large natural number. Quantum coherence is essentially exploited in all quantum ultrafast algorithms. Therefore, quantum parallel computation with quantum state instead of classical state can reach incomparable operation speed and information processing function of classical computer, and save a large amount of operation resources.
Currently, quantum computers are advancing toward scale and practical use. A very important issue in the chemical discipline or industry is the extraction of ground state information of molecular systems. For example, many properties of lithium batteries (e.g., ion mobility, equilibrium voltage, and thermal stability of the positive electrode material, etc.) are closely related to ground state energy. In the industrial production of lithium batteries, the efficient ground state solving can better predict the service life, output power and maximum capacity of the lithium batteries, and the preparation of the corresponding ground state can also help research and develop more new applications related to the lithium batteries. Therefore, how to efficiently calculate the ground state energy of a physical system is an extremely important and widely used problem.
The ground state (energy) of a physical system is typically determined by the Hamiltonian (Hamiltonian) of the physical system, and assuming that a system is composed of n qubits, the Hamiltonian H of the system can be written as a Hermitian matrix of 2 n×2n. In practice, the hamiltonian of a system is usually obtained in many ways. The ground state energy of the system is mathematically equivalent to the minimum eigenvalue of Ha Midu quantities H. Thus, the task of determining the hamiltonian ground state energy may be described as: given a hamiltonian H containing n qubits, assuming that its ground state is |v 0>(|v0 > can be considered as a column vector), and h|v 0>＝λ0|v0 >, where lambda 0 is the minimum eigenvalue, the goal is to find the minimum eigenvalue lambda 0 and prepare the corresponding ground state |v 0 >.
Once the ground state of a physical system is known, many of the system's properties can be revealed. Therefore, the Hamiltonian eigenvalue solution has been an important technical problem of widespread attention. In recent years, with the development of scientific technology, a plurality of new schemes for intrinsically solving problems of the hamiltonian of a physical system are proposed.
For small-scale problems, the information of the ground state can be extracted by using a classical computer, but when the problem scale reaches a certain degree, the exponential complexity makes us consume a large amount of computing resources when computing the ground state energy by using the classical computer. This results in that the large-scale solution ground state energy problem cannot be effectively solved.
In order to solve the difficulties faced by classical computing forces in dealing with ground state energy solving problems, some new schemes based on quantum computing, such as variable component sub-eigensolvers (Variational Quantum Eigensolver, VQE), have been proposed and tested. This approach takes into account the capabilities and limitations of current quantum computing devices and uses primarily parameterized quantum circuits (Parameterized Quantum Circuits, PQCs) to compute on recent noisy mid-scale quantum computers (Noisy Intermediate-Scale Quantum Computer, NISQ). Such schemes typically use parameterized quantum circuits, the circuit structure of which may be as shown in fig. 1, for example, to optimize a particular loss function. The loss function values and their corresponding gradients are then calculated using a quantum computer and the parameters are updated on a classical computer so that the trained circuit can prepare the ground state or estimate the ground state energy.
The quantum computing-based scheme reduces the dependence on classical computing resources by virtue of the nature of quantum computing. However, as the problem scale increases, the depth of the quantum circuit needs to be increased to obtain better training effect, the parameter amount required to be trained is increased significantly, and the high fidelity and longer decoherence time of the quantum bit are relied on. The large number of quantum circuit parameters makes classical computers difficult to optimize. In addition, there is a clear lean plateau (Barren Plateaus, BP) phenomenon in multi-bit and deeper parameterized quantum circuits, i.e., the gradient of the loss function decreases exponentially with increasing qubit size, thereby greatly reducing the trainability and expansibility of complex PQCs. This phenomenon results in that the parameterized quantum circuit cannot converge in an acceptable time during training, and also cannot achieve a good accuracy. Therefore, such conventional parameterized quantum circuit training schemes have limited effectiveness, have no good scalability and trainability, and require large resource consumption.
It can be seen that as the complexity of the problem increases, both classical and quantum schemes, new, difficult to solve problems arise. Thus, how to combine classical and quantum computing power to determine the ground state energy of hamiltonian of a physical system with fewer classical, quantum computing resources becomes critical, and further advances the scale, industrialization, and practicality of quantum computing.
Thus, according to an embodiment of the present disclosure, there is provided a feature information determining method of a system. Fig. 2 shows a flowchart 200 of a method of determining feature information of a system according to an embodiment of the present disclosure, as shown in fig. 2, the method 200 includes: determining a preset first quantum circuit for acquiring a characteristic information initial value, wherein the first quantum circuit is determined based on the Hamiltonian amount of the system (step 210); the following target operations are performed a preset number of times (step 220): the method comprises the steps of connecting a preset second quantum circuit in series to the output end of the first quantum circuit, wherein the second quantum circuit comprises a group of adjustable parameters, the group of adjustable parameters comprise corresponding initial values, and the second quantum circuit is adjacent to a unit quantum circuit within a preset range under the action of the initial values (step 2201); determining initial values of a set of perturbation parameters corresponding to the set of adjustable parameters for addition to the corresponding initial values of the set of adjustable parameters to form a new set of parameter values for the second quantum circuit (step 2202); applying the first quantum circuit and the second quantum circuit comprising a new set of parameter values to the initial quantum state to measure the obtained quantum state to obtain a measurement (step 2203); adjusting values of a set of perturbation parameters to minimize the measurement (step 2204); and taking the first quantum circuit and the minimized second quantum circuit as a new first quantum circuit (step 2205); and determining characteristic information of the system based on the first quantum circuit obtained after the target operation for a preset number of times (step 230).
According to the embodiment of the disclosure, the first quantum circuit with shallower is used for preparing the approximate characteristic information, and the second quantum circuit added later can not only keep the pre-training advantage to the greatest extent, but also continuously increase the precision of the characteristic information through repeated iterative operation, so that the method has better expandability and trainability.
In the present disclosure, a preset first quantum circuit for acquiring the initial value of the characteristic information is first determined, and the first quantum circuit is determined based on the hamiltonian amount H of the system. For example, the particular variable sub-circuit U 0, i.e., the first sub-circuit, required to prepare the initial state may be determined from the hamiltonian H. These circuits generally enable efficient approximate solutions to the system characteristic information after classical optimization. According to some embodiments, the characteristic information includes at least one of ground state and ground state energy.
The first quantum circuit may be a circuit designed for a specific H, a circuit designed for a reference density matrix reforming group (Matrix Product State, MPS), or the like, and is not limited thereto.
In particular, in some examples, for a K-local hamiltonian corresponding to an n-qubit system, the type of hamiltonian may be expanded into a summation form of m sub-hamiltonians, i.e., Wherein, H l and c l respectively represent the first sub Ha Midu amount and the corresponding decomposition coefficient thereof. Each child Ha Midu quantity H l can be represented by at most the tensor product of K single-quantum bit brix matrices, in particular, the first child hamiltonian quantity can be represented as:
wherein K is not less than K and not more than n, And (3) representing a bubble matrix of the j type corresponding to the quantum bit q k, wherein j epsilon { X, Y, Z }, and m is a positive integer. The three different values of j correspond to three single-quantum-bit bubble-benefit matrices.
In some embodiments, to ensure the efficiency of the scheme, a value of K may be considered to be equal to or less than a threshold, which may be set to n/2, for example.
As described above, the hamiltonian H can be expanded into a summation form of m sub-hamiltonian, and each sub Ha Midu quantity can be represented by at most the tensor product of K single-qubit brew matrices. Therefore, by counting the single-qubit poultices matrix corresponding to each of the sub Ha Midu amounts, a set of poultices matrices corresponding to each of the qubits can be determined, and a poultice matrix corresponding to each of the qubits in the largest number can be further determined. The one of the most numerous brix matrices may be any of the brix, briy, and briz matrices described above. When the number of the bubble matrix with the largest number corresponding to the qubit is two or more, any one of the bubble matrix can be used as the bubble matrix with the largest number.
According to some embodiments, the brix matrix comprises at least one of a brix matrix, a briy matrix, and a briz matrix. When the one of the most numerous Paulownia matrices is a Paulownia X matrix, the constituent function is sin (θ); when the one of the most numerous Paully matrices is a Paully Y matrix, the constituent function is-sin (θ); and when the maximum number of one type of the brix matrices is a brix Z matrix, the constituent function is cos (θ), where θ is the adjustable parameter. According to some embodiments, when the number of the brix matrices corresponding to the equivalent sub-bits is 0, the constituent function may be set to 1, i.e., a constant function.
According to some embodiments, the decomposition into HamiltonianWhen, as described above, the loss function may be set to: < H l > is the first sub hamiltonian amount, < H l > is expressed as:
Wherein, AndRespectively representing the corresponding constituent functions of the qubit q k and the corresponding adjustable parameters thereof. Multiplying < H l > corresponding to each sub Ha Midu by a corresponding coefficient C l and summing to obtain the final analytical value of the loss function C (θ).
After determining the loss function, the value of the adjustable parameter in the corresponding constituent function of each qubit is adjusted to minimize the loss function, thereby obtaining a set of optimized parameter values And stored in a classical computer. The quantum circuit is generated based on the adjusted value of the adjustable parameter and a quantum gate corresponding to a preset composition function, and the quantum circuit can be used as a first quantum circuit.
According to some embodiments, the first quantum circuit is generated based on at least one of a single bit rotation gate R Y (θ) in the Y direction and a single bit rotation gate R X (θ) in the X direction. When one of the Baoli matrices corresponding to the qubit with the largest number is the Baoli X matrix, R Y (theta) acts on the qubit; when one of the most number of the Baoli matrices corresponding to the qubit is a Baoli Y matrix, acting R X (theta) on the qubit; and when the one of the most number of the brix matrices corresponding to the qubit is a brix Z matrix, acting R Y (θ) or R X (θ) on the qubit, wherein a corresponding parameter θ of at least one of the single-bit rotation gates R Y (θ) and R X (θ) is configured based on the adjusted value of the adjustable parameter.
It should be noted that for the K-local hamiltonian, the loss function C (θ) is not necessarily a function of all n rotation angles. That is, there are cases where the number of the brix matrices corresponding to one or more qubits is 0. Therefore, the quantum gate corresponding to the one or more qubits may be any single-qubit gate, for example, any one of R Y (θ) and R X (θ), and the rotation angle may take any value as the optimized parameter value, for example, 0.
According to some embodiments, the quantum circuit may be generated based on a U3 quantum gate, wherein a parameter of the U3 quantum gate is configured to be a value of the adjustable parameter after adjustment. Compared with R Y (theta) and R X (theta) quantum gates, the U3 quantum gate has better quantum state expression capability, so that the trained approximate quantum ground state is more similar to a real ground state, and the effect of subsequent further optimization is improved.
In the present disclosure, one or more second quantum circuits for concatenating at the determined first quantum circuit output may be preset. The second quantum circuit comprises a group of adjustable parameters, the group of adjustable parameters comprise corresponding initial values, and the second quantum circuit is close to the unit quantum circuit within a preset range under the action of the initial values.
According to some embodiments, the second quantum circuit comprises a third quantum circuit and a fourth quantum circuit in series, the third quantum circuit and the fourth quantum circuit comprising respective parameters to form the set of adjustable parameters. The third quantum circuit is close to an inverse mapping of the fourth quantum circuit within the preset range under the influence of the initial values of the set of adjustable parameters.
By way of example only, the process may be performed,From the following componentsAndThe two-part parameterized circuit is formed, At this timeFIG. 3 shows V L andIs a schematic diagram of the circuit structure. It will be appreciated that the second quantum circuit, V L andAny suitable circuit configuration is possible and is not limited herein.
According to some embodiments, the values of the determined set of perturbation parameters satisfy a normal distribution in performing the target operation. It will be appreciated that it is also possible to determine the values of a set of disturbance parameters in other ways, without limitation.
It can be appreciated that in the present disclosure, the output result of the first quantum circuit is fine-tuned by the second quantum circuit, so as to further improve the calculation accuracy of the ground state energy, so that the trained second quantum circuit does not deviate too much from the unit quantum circuit. Thus, the values of the set of disturbance parameters may be determined within a predetermined small range of values, e.g., [ -0.1,0.1].
According to some embodiments, the second quantum circuit comprises: at least one of a single bit rotation gate R Y and a controlled not gate in the Y direction. Fig. 3 illustrates a second quantum circuit according to an embodiment of the present disclosureIs a schematic structural diagram of the (c). As shown in the figure 3 of the drawings,From the following componentsAndTwo parts of parameterized circuits.
It will be appreciated that other configurations of the second quantum circuit are possible, as long as it is guaranteed that it is close to the unit quantum circuit under the influence of the initial parameter values.
According to some embodiments, any one of a set of preset second quantum circuits is connected in series to an output of the first quantum circuit during execution of the target operation.
In particular, during an iteration of the operation, the circuits that are concatenated for each iteration may all be the same parameterized quantum circuit. Alternatively, a series of combinations of parameterized circuits of different designs { U k}k may be prepared, one circuit design being randomly selected from the set as the second quantum circuit in series for this operation in each iteration.
In one embodiment according to the present disclosure, first, the hamiltonian H corresponding to the system, i.e., the decomposed hamiltonian, and the maximum number of iterations L max are input in the form of a brix string on a classical computer. The first quantum circuit is determined, the quantum state output by the circuit is marked as |E 'L-1 >, and a layer of second quantum circuit is connected in series after the quantum state is marked as |E' L-1 >Wherein the method comprises the steps ofVectors formed by all rotation angles which are randomly generated, fixed and independent of each other when the second quantum circuit of the layer is initialized. L starts with 1 and represents the number of second quantum circuit layers that are added iteratively at present.From the following componentsAndThe two-part parameterized circuit is formed,Then, adding disturbance parameters to U L, namely V L and V L respectivelyRandomly generating a set of mutually independent disturbance parameters, e.g. according to a normal distribution N (μ, σ 2)AndIs recorded asAndThus, the layer of second quantum circuit can be expressed as Wherein the method comprises the steps ofFor indicating all circuit parameters after the disturbance has been added.
By running the whole quantum circuit and measuring the output quantum state and obtaining the desired value as a loss function value based on the hamiltonian HThen, a common classical optimization algorithm is used for minimizing the loss function value and optimizing and updating the disturbance parameters in the second quantum circuit of the layerAndAccording to some embodiments, the values of the set of perturbation parameters are adjusted based on a gradient descent method or a non-gradient descent method of the L-BFGS.
Repeatedly adjusting disturbance parametersAndUntil the loss function value converges. Storing all optimized total parameters on a classical computer, and recording asAt this time, the whole circuit outputs quantum state Then, the first quantum circuit and the layer of second quantum circuit are used as new first quantum circuits, a layer of second quantum circuits are connected in series after the new first quantum circuits, and disturbance parameters of the connected layer of second quantum circuits are adjusted … … until the preset maximum iteration number L max is reached. By using the circuit parameters after each step of iterative optimizationAnd all iteratively optimized U L, obtaining a circuit:
by operating the obtained circuit, the lowest eigenstate of Hamiltonian H can be prepared And estimating the ground state energy thereof
In the present disclosure, when the second quantum circuit layer is added in a stepwise iteration, parameters in the second quantum circuit that has completed training will be fixed and no longer participate in optimization iterations in subsequent operations. To further enhance deployment effects on quantum computers, in some examples, circuit optimization methods such as VAns may be used to reduce the number of quantum gates required to lay down the circuit, thereby providing the possibility for near-term real machine training.
In one application according to embodiments of the present disclosure, to embody the advantages of the scheme according to the present disclosure in estimating the ground state energy, the chemical molecular ground state energy process is solved. First, the difference in the hamiltonian ground state energy solutions of lithium hydride at different scales using the schemes of the embodiments of the present disclosure and the conventional training schemes is compared. Here, the second quantum circuit is designed with reference to the circuit in fig. 3. To ensure fairness, the circuit used in the conventional scheme is also designed with reference to fig. 3. When the parameterized circuit is set, in the scheme according to the disclosure, the depth of the whole circuit finally optimized is 15 layers so as to ensure the equivalent circuit parameter quantity as in the traditional training scheme, thereby ensuring fairness. The obtained hamiltonian ground state energy is shown in table 1.
TABLE 1
It can be seen that the scheme of the embodiment of the disclosure can improve the accuracy of the estimated hamiltonian ground state energy to a certain extent, and the scheme of the embodiment of the disclosure is used for iterative training to obtain a shallower circuit, so that the scheme of the embodiment of the disclosure has more practicability and effectiveness.
There is also provided, as shown in fig. 4, a feature information determining apparatus 400 of a system, including: a first determining unit 410 configured to determine a first quantum circuit preset for acquiring the initial value of the characteristic information, wherein the first quantum circuit is determined based on a hamiltonian amount of the system; an execution unit 420 configured to execute the following target operations a preset number of times: the method comprises the steps of connecting a preset second quantum circuit in series to an output end of the first quantum circuit, wherein the second quantum circuit comprises a group of adjustable parameters, the group of adjustable parameters comprise corresponding initial values, and the second quantum circuit is close to a unit quantum circuit within a preset range under the action of the initial values of the group of adjustable parameters; determining initial values of a set of perturbation parameters corresponding to the set of adjustable parameters, wherein the perturbation parameters are for addition to the respective initial values of the set of adjustable parameters to form a new set of parameter values for the second quantum circuit; applying the first quantum circuit and a second quantum circuit comprising the new set of parameter values to an initial quantum state to measure the obtained quantum state to obtain a measurement result; adjusting values of the set of perturbation parameters to minimize the measurement; and taking the first quantum circuit and the minimized second quantum circuit as a new first quantum circuit; and a second determining unit 430 configured to determine characteristic information of the system based on the first quantum circuit obtained after the target operation a preset number of times.
Here, the operations of the above units 410 to 430 of the system characteristic information determining apparatus 400 are similar to those of the steps 210 to 230 described above, respectively, and are not repeated here.
According to embodiments of the present disclosure, there is also provided an electronic device, a readable storage medium and a computer program product.
Referring to fig. 5, a block diagram of an electronic device 500 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the electronic device 500 includes a computing unit 501 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 may also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in electronic device 500 are connected to I/O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the electronic device 500, the input unit 506 may receive input numeric or character information and generate key signal inputs related to user settings and/or function control of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and/or a remote control. The output unit 507 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 508 may include, but is not limited to, magnetic disks, optical disks. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices over a computer network such as the internet and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 200 by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.
Claims (15)
1. A characteristic information determining method of a system, wherein the characteristic information includes at least one of a ground state and a ground state energy of the system, the method comprising:
determining a preset first quantum circuit for acquiring the initial value of the characteristic information, wherein the first quantum circuit is determined based on the Hamiltonian amount of the system;
The following target operations are performed for a preset number of times:
The method comprises the steps of connecting a preset second quantum circuit in series to an output end of the first quantum circuit, wherein the second quantum circuit comprises a group of adjustable parameters, the group of adjustable parameters comprise corresponding initial values, and the second quantum circuit is close to a unit quantum circuit within a preset range under the action of the initial values of the group of adjustable parameters;
Determining initial values of a set of perturbation parameters corresponding to the set of adjustable parameters, wherein the perturbation parameters are for addition to the respective initial values of the set of adjustable parameters to form a new set of parameter values for the second quantum circuit;
applying the first quantum circuit and a second quantum circuit comprising the new set of parameter values to an initial quantum state to measure the obtained quantum state to obtain a measurement result;
adjusting values of the set of perturbation parameters to minimize the measurement; and
Taking the first quantum circuit and the minimized second quantum circuit as a new first quantum circuit; and
And determining the characteristic information of the system based on the first quantum circuit obtained after the target operation of the preset times.
2. The method of claim 1, wherein the second quantum circuit comprises third and fourth quantum circuits in series, the third and fourth quantum circuits comprising respective parameters to form the set of adjustable parameters, and wherein,
The third quantum circuit is close to an inverse mapping of the fourth quantum circuit within the preset range under the influence of the initial values of the set of adjustable parameters.
3. The method of claim 1, wherein the determined values of the set of perturbation parameters satisfy a normal distribution in performing the target operation.
4. The method of claim 1 or 2, wherein the second quantum circuit comprises: direction single bit revolving door And at least one of a controlled NOT gate.
5. The method of claim 1, wherein any one of a set of a predetermined set of second quantum circuits is concatenated at an output of the first quantum circuit during execution of the target operation.
6. A method according to claim 1 or 3, wherein the values of the set of perturbation parameters are adjusted based on a gradient descent method or an L-BFGS method.
7. A characteristic information determining apparatus of a system, wherein the characteristic information includes at least one of a ground state and a ground state energy of the system, the apparatus comprising:
A first determining unit configured to determine a preset first quantum circuit for acquiring the initial value of the characteristic information, wherein the first quantum circuit is determined based on the hamiltonian amount of the system;
an execution unit configured to execute the following target operations a preset number of times:
The method comprises the steps of connecting a preset second quantum circuit in series to an output end of the first quantum circuit, wherein the second quantum circuit comprises a group of adjustable parameters, the group of adjustable parameters comprise corresponding initial values, and the second quantum circuit is close to a unit quantum circuit within a preset range under the action of the initial values of the group of adjustable parameters;
Determining initial values of a set of perturbation parameters corresponding to the set of adjustable parameters, wherein the perturbation parameters are for addition to the respective initial values of the set of adjustable parameters to form a new set of parameter values for the second quantum circuit;
applying the first quantum circuit and a second quantum circuit comprising the new set of parameter values to an initial quantum state to measure the obtained quantum state to obtain a measurement result;
adjusting values of the set of perturbation parameters to minimize the measurement; and
Taking the first quantum circuit and the minimized second quantum circuit as a new first quantum circuit; and
And a second determination unit configured to determine characteristic information of the system based on the first quantum circuit obtained after the target operation for a preset number of times.
8. The apparatus of claim 7, wherein the second quantum circuit comprises third and fourth quantum circuits in series, the third and fourth quantum circuits comprising respective parameters to form the set of adjustable parameters, and wherein,
The third quantum circuit is close to an inverse mapping of the fourth quantum circuit within the preset range under the influence of the initial values of the set of adjustable parameters.
9. The apparatus of claim 7, wherein the determined values of the set of perturbation parameters satisfy a normal distribution in performing the target operation.
10. The apparatus of claim 7 or 8, wherein the second quantum circuit comprises: direction single bit revolving door And at least one of a controlled NOT gate.
11. The apparatus of claim 7, wherein any one of a set of a predetermined set of second quantum circuits is concatenated at an output of the first quantum circuit during execution of the target operation.
12. The apparatus of claim 7 or 9, wherein values of the set of perturbation parameters are adjusted based on a gradient descent method or an L-BFGS method.
13. An electronic device, comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein the method comprises the steps of
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211058584.9A CN115630701B (en) | 2022-08-31 | 2022-08-31 | System characteristic information determining method, device, electronic equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211058584.9A CN115630701B (en) | 2022-08-31 | 2022-08-31 | System characteristic information determining method, device, electronic equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115630701A CN115630701A (en) | 2023-01-20 |
| CN115630701B true CN115630701B (en) | 2024-07-12 |
Family
ID=84902533
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211058584.9A Active CN115630701B (en) | 2022-08-31 | 2022-08-31 | System characteristic information determining method, device, electronic equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115630701B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109478258A (en) * | 2016-06-02 | 2019-03-15 | 谷歌有限责任公司 | Use sub- logic control training quantum evolution |
| CN110023966A (en) * | 2016-10-24 | 2019-07-16 | 谷歌有限责任公司 | Use quantum computing simulation material |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10332023B2 (en) * | 2017-09-22 | 2019-06-25 | International Business Machines Corporation | Hardware-efficient variational quantum eigenvalue solver for quantum computing machines |
| US20200104740A1 (en) * | 2018-10-02 | 2020-04-02 | Zapata Computing, Inc. | Hybrid Quantum-Classical Computer for Solving Linear Systems |
-
2022
- 2022-08-31 CN CN202211058584.9A patent/CN115630701B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109478258A (en) * | 2016-06-02 | 2019-03-15 | 谷歌有限责任公司 | Use sub- logic control training quantum evolution |
| CN110023966A (en) * | 2016-10-24 | 2019-07-16 | 谷歌有限责任公司 | Use quantum computing simulation material |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115630701A (en) | 2023-01-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113496285B (en) | Quantum circuit-based data processing method and device, electronic equipment and medium | |
| CN114219076B (en) | Quantum neural network training method and device, electronic equipment and medium | |
| CN113011593B (en) | Method and system for eliminating quantum measurement noise, electronic device and medium | |
| CN113298262B (en) | Quantum device denoising method and device, electronic device and computer readable medium | |
| CN112541590B (en) | Quantum entanglement detection method and device, electronic device and storage medium | |
| CN113807525B (en) | Quantum circuit operation method and device, electronic device and medium | |
| CN115345309A (en) | Method and device for determining system characteristic information, electronic equipment and medium | |
| CN113705793B (en) | Decision variable determination method and device, electronic equipment and medium | |
| CN114021728B (en) | Quantum data measuring method and system, electronic device, and medium | |
| CN113255922B (en) | Quantum entanglement quantization method and device, electronic device and computer readable medium | |
| CN114548413A (en) | Quantum circuit operation method and device, electronic device and medium | |
| CN114580649A (en) | Method and device for eliminating quantum Pagli noise, electronic equipment and medium | |
| CN113098802B (en) | Inverse mapping decomposition method and device for quantum noise channel, electronic device, and medium | |
| CN116502721A (en) | Quantum system-based information processing method and device, electronic equipment and medium | |
| CN112529195A (en) | Quantum entanglement detection method and device, electronic device and storage medium | |
| CN115630701B (en) | System characteristic information determining method, device, electronic equipment and medium | |
| CN115329971B (en) | Method and device for eliminating amplitude damping noise, electronic equipment and medium | |
| CN116523065B (en) | Method and device for determining quantum equipment evolution unitary matrix, electronic equipment and medium | |
| CN116362341B (en) | Quantum device unitary transformation degree determining method and device, electronic device and medium | |
| CN114021729B (en) | Quantum circuit operation method and system, electronic device and medium | |
| CN116739098B (en) | Quantum measurement device performance estimation method and device, electronic device and medium | |
| CN114580648B (en) | Method and device for eliminating generalized amplitude damping noise, electronic equipment and medium | |
| CN116739097B (en) | Quantum measurement device performance estimation method and device, electronic device and medium | |
| CN116306956B (en) | Method and device for eliminating quantum computer noise, electronic equipment and medium | |
| CN116629368A (en) | Quantum device unitary transformation degree determining method and device, electronic device and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |