CN114595801A

CN114595801A - Method, device and server for reducing qubit errors by using quantum neural network

Info

Publication number: CN114595801A
Application number: CN202111547369.0A
Authority: CN
Inventors: 邵伟; 周卓俊; 丘秉宜; 陈柳平; 韩琢; 罗乐
Original assignee: Qike Quantum Technology Zhuhai Co ltd; Guokaike Quantum Technology Beijing Co Ltd
Current assignee: Qike Quantum Technology Zhuhai Co ltd; Guokaike Quantum Technology Beijing Co Ltd
Priority date: 2022-05-07
Filing date: 2022-05-07
Publication date: 2022-06-07

Abstract

The embodiment of the application is suitable for the technical field of quantum, and provides a method, a device and a server for reducing qubit errors by using a quantum neural network, wherein the method comprises the following steps: and counting the polarization vectors of all photons to define a first polarization vector, counting the polarization vectors of all photons to define a second polarization vector at least once again at another time, generating a polarization direction model of the photons based on the second polarization vector and the first polarization vector to serve as the input of a quantum neural network, training the polarization direction model based on the quantum neural network to obtain a trained polarization direction model, and judging the polarization state of the photons according to the trained polarization direction model to determine the quantum bit. Therefore, the quantum bit calculation accuracy can be effectively improved, and the quantum bit can be better put into practical application.

Description

Method, device and server for reducing qubit errors by using quantum neural network

Technical Field

The application belongs to the technical field of quantum, and particularly relates to a method, a device and a server for reducing qubit errors by using a quantum neural network.

Background

In quantum information theory, the basic unit of quantum information is a quantum bit (qubit, complex qubits), called a qubit. A qubit is a two-state quantum system (e.g. the polarization state of a photon, or the spin state of an electron, etc.), i.e. a qubit is a two-dimensional hilbert space (abstract space describing the state vector, which is two-dimensional since the polarization state of a photon and the spin state of an electron are both oriented orthogonally, i.e. with two axes perpendicular to each other). The quantum bit number is the number of the two-state quantum system. If the polarization state of a photon is used, then there are several photons and there are several quanta of bits. The classical bit concept refers to any two-state system (element) that can only take one of the states, generally labeled 0, 1. A classical bit can only be 0 or 1. Researchers who have performed atomic molecular energy level calculations know that Hamiltonian writing is an impossible task even for a simple atomic system (e.g., helium atoms with only two electrons). It is not possible to write a complete non-relativistic hamiltonian starting from the first basic principle if we acknowledge the basic principle of field theory. That is, it is in principle impossible to prepare any one of the defined quantum states. Because quantum state has errors, the qubit is a fluctuation quantity, or a relative quantity or an uncertain quantity, and obviously, the unknown state quantity is difficult to be used in practical application, so how to reduce qubit errors and improve the accuracy of qubit calculation is a problem to be solved urgently at present.

Disclosure of Invention

In view of this, the embodiments of the present application provide a method, an apparatus, and a server for reducing qubit errors by using a quantum neural network, which can effectively improve qubit calculation accuracy, so that the qubit calculation accuracy can be better put into practical use.

A first aspect of embodiments of the present application provides a method for reducing qubit errors using a quantum neural network, comprising:

s101: counting the polarization vectors of all photons to define a first polarization vector;

s102: counting the polarization vectors of all the photons at another time at least once again to define a second polarization vector;

s103: generating a polarization direction model of the photon as an input to a quantum neural network based on the second polarization vector and the first polarization vector;

s104: training the polarization direction model based on the quantum neural network to obtain a trained polarization direction model;

s105: judging the polarization state of the photons according to the trained polarization direction model to determine quantum bits;

s106: repeating S101-S105 for multiple times.

The method and the device have the advantages that the polarization vector of photons is utilized to predict or calculate the polarization state of the photons for the first time, the quantum bit is calculated based on the polarization state of the photons, and then the polarization direction vector mode is repeatedly trained in the quantum neural network, so that the calculation accuracy of the quantum bit is improved.

Further, when the polarization directions of the first polarization vector and the second polarization vector cannot be determined, that is, the first polarization vector and the second polarization vector coincide with each other, a third polarization vector detection is required, and if the third polarization vector coincides with the first polarization vector and the second polarization vector, it is determined that the photon does not have a polarization amount, that is, the polarization state of the photon is a fixed state.

In order to better improve the calculation accuracy of quantum bits, the method and the device perform multiple polarization vector detection to repeatedly measure the polarization state of photons, and further improve the accuracy of quantum bits.

Further, the quantum neural network comprises at least three layers, which are respectively:

a first layer inputting the polarization direction model;

a second layer for inputting the trained polarization direction model;

and a third layer for inputting the qubits obtained based on the trained polarization direction model.

Furthermore, each layer of the quantum neural network comprises a plurality of input nodes, and synchronous input of multidimensional data can be realized.

Further, the method also comprises a vector model establishing process among photons, which comprises the following steps:

determining a first polarization vector of the first photon and a first polarization vector of the second photon;

determining a second polarization vector of the first photon, and a second polarization vector of the second photon;

and obtaining a relation vector between the two photons based on the polarization direction of the first photon and the polarization direction of the second photon.

The use of the multilayer quantum neural network can improve the calculation precision of the quantum bit and reduce errors.

And further, correcting the qubits based on the relation vector, if the relation vector is a phase vector, performing qubit subtraction operation based on the included angle coefficient of the relation vector, and if the relation vector is a backward vector, performing qubit addition operation based on the included angle coefficient of the relation vector.

Further, there may be multiple relationship vectors for the same photon, which are correspondingly added or subtracted multiple times.

A second aspect of embodiments of the present application provides an apparatus for reducing qubit errors using a quantum neural network, the apparatus being configured to implement a method for reducing qubit errors using the quantum neural network, comprising:

the vector analyzer is used for analyzing the polarization vectors of the photons at different times;

the operation module is used for calculating the polarization direction of the photon based on the deflection vector of the photon at different time;

a storage device storing a polarization direction model of photons;

and the processing equipment is used for calculating the quantum bit based on the polarization direction model of the photon and the polarization direction of the photon.

Further, the operation module can synchronously calculate the polarization directions of the photons.

A third aspect of embodiments of the present application provides a server comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, performs a method of reducing qubit errors using a quantum neural network.

Compared with the prior art, the embodiment of the application has the advantages that: utilize quantum neural network to calculate the photon polarization state of deciding the quantum bit in this application, obtain comparatively accurate quantum bit through multilayer neural network and the photon polarization direction vector model of establishing to the quantum bit with the indefinite state can be quantized, makes it can be used for practical application, is favorable to the popularization and application of quantum bit technique.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments or the prior art description will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings may be obtained according to these drawings without inventive labor.

Fig. 1 is a schematic flowchart illustrating a method for reducing qubit errors using a quantum neural network according to an embodiment of the present disclosure;

fig. 2 is a schematic structural diagram of an apparatus for reducing qubit errors using a quantum neural network according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of a server according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.

Referring to fig. 1, a schematic flow chart of a method for reducing qubit errors using a quantum neural network according to an embodiment of the present application is shown, including:

s106: repeating S101-S105 for multiple times.

Quantum Neural Networks (QNNs) are computational neural network models based on the principles of quantum mechanics. Subhash Kak and Ron Chrisley independently published in 1995 an idea about quantum neuro-computing, which was combined with quantum psychology theory that considers quantum effects to play a role in cognitive function. However, a typical study of QNN involves combining the classical artificial neural network model (an important task widely used for pattern recognition in machine learning) with the advantages of quantum information. In order to develop more efficient algorithms. One important motivation for these studies is the difficulty in training classical neural networks, especially in big data applications. It is desirable that quantum computing functions such as quantum parallelism or interference and entanglement effects can be used as resources.

Optionally, in some embodiments, when the polarization directions of the first polarization vector and the second polarization vector cannot be determined, that is, the first polarization vector and the second polarization vector coincide, a third polarization vector detection must be performed, and if the third polarization vector coincides with the first polarization vector and the second polarization vector, it is determined that the photon has no polarization amount, that is, the polarization state of the photon is a fixed state.

Optionally, in some embodiments, the quantum neural network includes at least three layers, which are:

a first layer inputting a polarization direction model;

the second layer inputs the trained polarization direction model;

and the third layer inputs the qubits obtained based on the trained polarization direction model.

Optionally, in some embodiments, each layer of the quantum neural network includes a plurality of input nodes, and the synchronous input of the multidimensional data can be realized.

Optionally, in some embodiments, the method further includes a vector model building process between photons, including:

Furthermore, the qubits are corrected based on the relation vector, if the relation vector is a relative vector, qubit subtraction operation is performed based on the included angle coefficient, if the relation vector is a backward vector, qubit addition operation is performed based on the included angle coefficient, and at least one layer of the qubits is correspondingly added in the quantum neural network and used for inputting the relation vector.

Optionally, in some embodiments, there may be multiple relationship vectors for the same photon, and the multiple relationship vectors are correspondingly added or subtracted multiple times. For example, for a certain photon, there are several adjacent photons, so there are several corresponding relationship vectors, and therefore each relationship vector is to be modified as a modification parameter.

Referring to fig. 2, a schematic structural diagram of an apparatus for reducing qubit errors by using a quantum neural network according to an embodiment of the present application is provided, where the apparatus is used to implement a method for reducing qubit errors by using the quantum neural network, and the method includes:

the vector analyzer 11 is used for analyzing polarization vectors of the photons at different times;

the operation module 12 is used for calculating the polarization direction of the photons based on the deflection vectors of the photons at different times;

a storage device 13 storing a polarization direction model of photons; the storage device 13 stores data in a local storage mode, so that the data can be quickly and effectively called, and a cloud storage mode is avoided, so that the stability of the data is improved.

The processing device 14 computes a qubit based on the polarization direction model of the photons and the polarization direction of the photons.

Alternatively, in some embodiments, the calculation module 12 may calculate the polarization directions of multiple photons simultaneously.

A third aspect of embodiments of the present application provides a server comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, performs a method of reducing qubit errors using a quantum neural network.

Fig. 3 is a schematic structural diagram of a server according to an embodiment of the present application. As shown in fig. 3, the server of this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and operable on the processor 60. The steps in the above-described method embodiments are implemented when the processor 60 executes the computer program 62. Alternatively, the processor 60, when executing the computer program 62, implements the functions of the respective modules/units in the above-described respective apparatus embodiments.

Illustratively, the computer program 62 may be divided into one or more modules/units, which are stored in the memory 61 and executed by the processor 60 to accomplish the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions that describe the execution of the computer program 62 in the server.

The server may be a computing device such as a cloud server. The server may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that FIG. 3 is merely an example of a server and is not intended to be limiting and may include more or fewer components than those shown, or some of the components may be combined, or different components.

The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 61 may be an internal storage unit of the server, such as a hard disk or a memory of the server. The memory 61 may also be an external storage device of the server, such as a plug-in hard disk provided on the server, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 61 may also include both an internal storage unit of the server and an external storage device. The memory 61 is used for storing computer programs and other programs and data required by the server. The memory 61 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed server and method may be implemented in other ways. For example, the above-described server embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above may be implemented by instructing the relevant hardware by a computer program, where the computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the methods described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.

Claims

1. A method for reducing qubit errors using a quantum neural network, comprising:

s106: repeating S101-S105 for multiple times.

2. The method of claim 1 for reducing qubit errors using a quantum neural network, wherein: when the polarization directions of the first polarization vector and the second polarization vector cannot be determined, that is, the first polarization vector and the second polarization vector are coincident, third-time polarization vector detection is required, and if the third-time polarization vector is coincident with the first polarization vector and the second polarization vector, it is determined that the photon has no polarization quantity, that is, the polarization state of the photon is a fixed state.

3. The method of claim 1 for reducing qubit errors using a quantum neural network, wherein: the quantum neural network at least comprises three layers which are respectively:

a first layer inputting the polarization direction model;

a second layer for inputting the trained polarization direction model;

4. The method of claim 3 for reducing qubit error using a quantum neural network, wherein: each layer of the quantum neural network comprises a plurality of input nodes, and multi-dimensional data can be synchronously input.

5. The method of claim 1 for reducing qubit errors using a quantum neural network, wherein: the method also comprises a vector model establishing process among photons, which comprises the following steps:

6. The method of claim 5 for reducing qubit error using a quantum neural network, wherein: and correcting the quantum bit based on the relation vector, if the relation vector is a phase vector, performing quantum bit subtraction operation based on the included angle coefficient of the relation vector, and if the relation vector is a backward vector, performing quantum bit addition operation based on the included angle coefficient of the relation vector.

7. The method of claim 6, wherein the qubit error reduction using a quantum neural network comprises: multiple relationship vectors may exist for the same photon, which may be correspondingly added or subtracted multiple times.

8. An apparatus for reducing qubit errors using a quantum neural network, the apparatus being configured to implement the method of any one of claims 1-7 for reducing qubit errors using the quantum neural network, comprising:

a storage device storing a polarization direction model of photons;

9. The apparatus of claim 8, wherein the computing module is capable of computing polarization directions of the plurality of photons simultaneously.

10. A server comprising a memory and a processor, the memory storing a computer program, wherein the computer program when executed by the processor performs the method of any of claims 1-7 for reducing qubit errors using a quantum neural network.