A quantum phase estimation method and apparatus

Abstract

The application discloses a quantum phase estimation method and device. The method comprises the following steps: obtaining a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit respectively, wherein the first quantum circuit and the second quantum circuit are circuits for phase estimation constructed by using a statistical inference principle; in response to the first quality factor and the second quality factor satisfying a target condition, iteratively running the first quantum circuit to obtain measurement results until a total number of actions of a target module reaches a preset iteration termination condition, wherein the target module is a module with a phase to be estimated in the quantum circuit; and determining a target estimation value of the phase to be estimated based on all the measurement results. By using the embodiment of the application, phase estimation of a large-scale problem is realized.

Description

Technical Field

[0001] The present application relates to the field of quantum computing technology, and in particular to a quantum phase estimation method and device. Background Art

[0002] Quantum Phase Estimation (QPE) is a core step in many quantum algorithms, such as Shor's algorithm and the HHL algorithm (proposed by Harrow, Hassidim, and Lloyd). It is also a very useful step in accelerating computations in many other quantum computing applications. QPE is used to quickly estimate the eigenvalues ​​of a unitary transformation. Since the eigenvalues ​​of a unitary matrix are all complex numbers modulo 1, their eigenvalues ​​and phase are essentially equivalent, so only the phase of the eigenvalues ​​needs to be estimated.

[0003] Eigenvalues ​​carry information about physical parameters and represent the characteristics of a physical process. Solving for these eigenvalues ​​can be used to study the ground state and excited state energies of quantum systems, which is of great significance to scientific research and industrial development. For example, in lithium-ion battery technology, efficiently extracting ground state properties can promote the development of lithium-ion battery technology. Existing quantum phase estimation methods are based on quantum Fourier transforms. These methods require the introduction of auxiliary qubits, which number approximately twice as many as data qubits. Given the current scale of quantum computers, phase estimation for larger-scale problems is difficult, thus limiting scientific research and industrial development. Summary of the Invention

[0004] The purpose of this application is to provide a quantum phase estimation method and device, aiming to achieve phase estimation for larger-scale problems.

[0005] An embodiment of the present application provides a quantum phase estimation method, the method comprising:

[0006] Obtaining a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit, respectively, wherein both the first quantum circuit and the second quantum circuit are circuits for phase estimation constructed using statistical inference principles;

[0007] In response to the first quality factor and the second quality factor satisfying a target condition, iteratively running the first quantum circuit to obtain measurement results until the total number of actions of a target module reaches a preset iteration termination condition, wherein the target module is a module in the quantum circuit with a phase to be estimated;

[0008] Based on all the measurement results, a target estimated value of the phase to be estimated is determined.

[0009] Optionally, determining a target estimated value of the phase to be estimated based on all measurement results includes:

[0010] A target estimated value of the phase to be estimated is determined using a posterior distribution of the phase to be estimated, wherein the posterior distribution is generated based on all measurement results.

[0011] Optionally, the method further includes:

[0012] Using the first quantum circuit and the second quantum circuit respectively, evolving and measuring the initial quantum state to obtain a probability distribution of the current phase to be estimated;

[0013] When it is determined based on the probability distribution that the phase to be estimated does not satisfy the specified condition, returning to the step of respectively evolving and measuring the initial quantum state using the first quantum circuit and the second quantum circuit to obtain the probability distribution of the current phase to be estimated, until the phase to be estimated satisfies the specified condition.

[0014] Optionally, the first quantum circuit and the second quantum circuit both include a first parameter and a second parameter, wherein the first parameter is a pre-set phase parameter in the two quantum circuits, and the second parameter is the number of times the target module acts in one quantum circuit;

[0015] The method further comprises:

[0016] In response to the first quality factor and the second quality factor not satisfying the target condition, the first parameters and the second parameters of the two quantum circuits are updated respectively to obtain new first quantum circuits and second quantum circuits, and the process returns to the step of evolving the initial quantum state using the first quantum circuit and the second quantum circuit respectively to obtain the probability distribution of the current phase to be estimated.

[0017] Optionally, the specified condition is determined based on a probability that the phase to be estimated is within a set phase range and a set value, and the set phase range and the set value are updated according to the number of iterations.

[0018] Optionally, the specified condition is:

[0019] Pr[θ∈Θ i ]≥1-∈ i

[0020] Where i is the number of iterations, θ is the phase to be estimated, Θ i is the phase range set, 1-∈ i is the set value;

[0021]

[0022]

[0023] n i =min(2 i-1 ,n opt ,n lim )

[0024]

[0025] n i is the number of times the target module acts in a quantum circuit, N tot is the threshold of the number of times the target module acts during the quantum phase estimation process; β is the noise level parameter, n lim is the threshold of the number of times a quantum circuit can carry the action of the target module, θ min for is the estimated value of the phase to be estimated.

[0026] Optionally, the quality factor function is:

[0027]

[0028] in, is the quality factor function, is the loss function, n represents the second parameter in vector form, φ represents the first parameter in vector form, v represents the number of times a quantum circuit in vector form is run, x represents the measurement result in vector form, and p(θ|n,φ,v,x) is the posterior distribution of the phase to be estimated.

[0029] Optionally, include or

[0030] The number of measurements obtained in each iteration is Among them, N left The number of remaining target module actions.

[0031] Another embodiment of the present application provides a quantum phase estimation device, comprising:

[0032] A first obtaining module is configured to obtain a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit, respectively, wherein both the first quantum circuit and the second quantum circuit are circuits for phase estimation constructed using statistical inference principles;

[0033] a second obtaining module, configured to, in response to the first quality factor and the second quality factor satisfying a target condition, iteratively run the first quantum circuit to obtain a measurement result until the total number of actions of a target module reaches a preset iteration termination condition, wherein the target module is a module in the quantum circuit with a phase to be estimated;

[0034] A determination module is used to determine a target estimated value of the phase to be estimated based on all the measurement results.

[0035] An embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to implement any of the above methods when running.

[0036] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement any of the above methods.

[0037] Compared to the prior art, this application first obtains a first quality factor for a first quantum circuit and a second quality factor for a second quantum circuit. Then, in response to the first and second quality factors satisfying target conditions, the first quantum circuit is iteratively run to obtain measurement results until the total number of target module actions reaches a preset iteration termination condition. Based on all these measurement results, a target estimate of the phase to be estimated is determined. The quality factors are used to select a superior quantum circuit from the two quantum circuits. The selected quantum circuit is then used to obtain the data required for phase estimation, and the data is processed to obtain the final result. The quantum circuits used are constructed using statistical inference principles, eliminating the need for quantum Fourier transforms, reducing the number of auxiliary qubits, and significantly reducing the use of qubit resources. This allows for phase estimation of larger-scale problems, thereby promoting scientific research and industrial development. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a network block diagram of a quantum phase estimation system provided by an embodiment of the present application;

[0039] Figure 2 A schematic diagram of a flow chart of a quantum phase estimation method provided in an embodiment of the present application;

[0040] Figure 3 A schematic diagram of the structure of a quantum phase estimation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and should not be construed as limiting the present application.

[0042] Figure 1 This is a network block diagram of a quantum phase estimation system provided by an embodiment of the present application. The quantum phase estimation system may include a network 110, a server 120, a wireless device 130, a client 140, a storage unit 150, a classical processing system 160, a quantum processing system 170, and may also include additional memory, classical processors, quantum processors, and other devices not shown.

[0043] The network 110 is a medium for providing communication links between various devices and computers connected together in the quantum phase estimation system, including but not limited to the Internet, corporate intranet, local area network, mobile communication network and their combinations. The connection method can be wired, wireless communication links or optical fiber cables.

[0044] Server 120 and client 140 are conventional data processing systems that may contain data and applications or software tools that perform conventional computing processes. Client 140 may be a personal computer or a network computer, so the data may also be provided by server 120. Wireless device 130 may be a smartphone, tablet, laptop, smart wearable device, etc. Storage unit 150 may include database 151, which may be configured to store data such as qubit parameters, quantum logic gate parameters, quantum circuits, and quantum programs.

[0045] The classical processing system 160 (quantum processing system 170) may include a classical processor 161 (quantum processor 171) for processing classical data (quantum data) and a memory 163 (memory 172) for storing classical data (quantum data). The classical data (quantum data) may be a boot file, an operating system image, and an application 162 (application 173). The application 162 (application 173) may be used to implement a quantum algorithm compiled according to the quantum phase estimation method provided in an embodiment of the present application.

[0046] Any data or information stored or generated in classical processing system 160 (quantum processing system 170) can also be configured to be stored or generated in another classical (quantum) processing system in a similar manner, and similarly, any application program executed therein can also be configured to be executed in another classical (quantum) processing system in a similar manner.

[0047] It should be noted that a true quantum computer is a hybrid structure, which includes at least Figure 1 The system consists of two parts: the classical processing system 160, which is responsible for performing classical calculations and control; and the quantum processing system 170, which is responsible for running quantum programs and thus realizing quantum computing.

[0048] The classical processing system 160 and the quantum processing system 170 can be integrated in one device or distributed in two different devices. For example, a first device including the classical processing system 160 runs a classical computer operating system, on which quantum application development tools and services are provided, and storage and network services required by quantum applications are also provided. A user develops a quantum application through the quantum application development tools and services thereon, and sends the quantum program to a second device including the quantum processing system 170 through the network services thereon. The second device runs a quantum computer operating system, parses the code of the quantum program through the quantum computer operating system, and compiles it into instructions that can be recognized and executed by the quantum computer control system, and the quantum processor 170 implements the quantum algorithm corresponding to the quantum program according to the instructions.

[0049] In the classical processing system 160 based on a silicon chip, the units of the classical processor 161 are CMOS tubes, and such computing units are not limited by time and coherence, that is, such computing units are not limited by the use time and are available at any time. In addition, in the silicon chip, the number of such computing units is also sufficient, and the number of computing units in a classical processor is currently thousands or even tens of thousands. The number of computing units is sufficient and the computing logic of the CMOS tube is fixed, for example: AND logic. When operating with CMOS tubes, a large number of CMOS tubes are combined with limited logic functions to achieve the effect of operation.

[0050] Unlike such logic units in the classical processing system 160, the basic computing unit of the quantum processor 171 in the quantum processing system 170 is a quantum bit, and the input of the quantum bit is limited by coherence and coherence time, that is, the quantum bit is limited by the use time and is not available at any time. It is a key problem of quantum computing to fully use the quantum bit within the available use time of the quantum bit. In addition, the number of quantum bits in a quantum computer is one of the representative indicators of the performance of the quantum computer, and each quantum bit realizes computing functions through on-demand configured logic functions. Given the limited number of quantum bits and the diversified logic functions in the field of quantum computing, such as: Hadamard gate (H gate), Pauli-X gate (X gate), Pauli-Y gate (Y gate), Pauli-Z gate (Z gate), X gate, RY gate, RZ gate, CNOT gate, CR gate, iSWAP gate, Toffoli gate, etc. When quantum computing, a limited number of quantum bits are combined with a variety of logic functions to achieve the effect of operation.

[0051] Based on these differences, the design of logical functions acting on qubits (including the design of whether to use qubits and the design of the efficiency of each qubit) is the key to improving the operation performance of quantum computers, and special design is needed. The above design for qubits is a technical problem that ordinary computing devices do not need to consider and face. Based on this, for the phase estimation of larger-scale problems in quantum computing, the present application proposes a quantum phase estimation method and device, aiming to realize the phase estimation of larger-scale problems.

[0052] Reference Figure 2 , Figure 2 A flowchart of a quantum phase estimation method provided by an embodiment of the present application can include the following steps:

[0053] S201: Obtain a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit, respectively, wherein the first quantum circuit and the second quantum circuit are circuits for phase estimation constructed using statistical inference principles.

[0054] The first quantum circuit and the second quantum circuit are circuits for quantum phase estimation, but quantum phase estimation does not need to perform inverse quantum Fourier transform and can be constructed using statistical inference principles, specifically using Bayesian statistical inference principles. It should be noted that the basic circuit units included in the first quantum circuit and the second quantum circuit can be the same, but at least one basic circuit unit has different parameters, and of course, the number of the same basic circuit unit can be different in different quantum circuits.

[0055] The quality factor is calculated based on a quality factor function using the measurement results obtained by the current operation of the quantum circuit. Specifically, the quality factor function is constructed based on a loss function. The quality factor function is mainly used to judge the effect of a quantum circuit on phase estimation. The quality factor is used to evaluate the performance of phase estimation, thereby excluding the influence of other peak values on θ estimation and excluding the ambiguity of the quantum phase estimation algorithm.

[0056] In the present application, the first quantum circuit and the second quantum circuit each include a first parameter and a second parameter, wherein the first parameter is a phase parameter pre-set in the two quantum circuits, and the second parameter is the number of times the target module acts in one quantum circuit. Changing the first parameter and / or the second parameter can change the measurement results of the quantum circuit, thereby possibly obtaining different quality factors, and then a better quantum circuit can be selected for phase estimation.

[0057] The basic circuit unit of the quantum circuit includes a unitary operation and a unitary operation with a phase of φ The U operator is a unitary operator, which realizes U|ψ> = e2πiθ |ψ>. is a target module, θ is a phase to be estimated, |ψ> is a quantum state, the first parameter is a phase φ in a quantum circuit, and the second parameter is a number of times of action of a quantum circuit .

[0058] In some possible embodiments of the present application, the method can further include:

[0059] evolving and measuring the initial quantum state by using the first quantum circuit and the second quantum circuit respectively to obtain a probability distribution of the current phase to be estimated;

[0060] when it is determined based on the probability distribution that the phase to be estimated does not satisfy a specified condition, returning to the step of evolving and measuring the initial quantum state by using the first quantum circuit and the second quantum circuit respectively to obtain a probability distribution of the current phase to be estimated until the phase to be estimated satisfies the specified condition.

[0061] When the first quantum circuit and the second quantum circuit satisfy certain conditions, the two quantum circuits are evaluated again, which can improve the accuracy of phase estimation. When determining whether the first quantum circuit and the second quantum circuit satisfy certain conditions, the same initial quantum state is evolved and measured by using the first quantum circuit and the second quantum circuit respectively to obtain a probability distribution of θ, and then a phase value of the current phase to be estimated is obtained by using the probability distribution, and it is determined whether the specified condition is satisfied by using the phase value. If the specified condition is satisfied, the corresponding quality factor is calculated by using the measurement results of the two quantum circuits, and if the specified condition is not satisfied, the two quantum circuits are iteratively run until the specified condition is satisfied. It should be noted that when the two quantum circuits are iteratively run, the parameters in the two quantum circuits do not change, and the evolved initial quantum state is also the same quantum state.

[0062] In some possible embodiments of the present application, the specified condition is determined based on a probability that the phase to be estimated is within a set phase range and a set value, and the set phase range and the set value are updated according to the number of iterations.

[0063] The specified condition can be that the probability that the phase to be estimated is within the set phase range is greater than or equal to the set value, or that a difference between the probability and the set value is within a certain range, and the like. The set phase range and the set value can be updated synchronously in a preset manner, or asynchronously.

[0064] In some embodiments of the present application, the specified condition can be:

[0065] Pr[θ∈Θ i ]≥1-∈ i

[0066] where i is the iteration number, θ is the phase to be estimated, θ i is the set phase range, 1-∈ i is the set value;

[0067]

[0068]

[0069] n i = min(2 i-1 ,n opt ,n lim )

[0070]

[0071] n i is the number of times the target module acts in a quantum circuit, N tot is the threshold of the number of times the target module acts in the quantum phase estimation process; β is the noise level parameter, n lim is the threshold of the number of times the target module acts in a quantum circuit, θ min is the is the estimated value of the phase to be estimated.

[0072] In some possible embodiments of the present application, the merit function is a function of the loss function and the posterior distribution of the phase to be estimated. Specifically, the merit function can be:

[0073]

[0074] where, is the merit function, is the loss function, n represents the second parameter in vector form, φ represents the first parameter in vector form, v represents the number of times a quantum circuit runs in vector form, x represents the measurement result in vector form, and p(θ|n, φ, v, x) is the posterior distribution of the phase to be estimated.

[0075]

[0076] π(θ) is the prior distribution of θ, and m is the total number of iterations.

[0077] In some embodiments, may include or This can achieve the Heisenberg limit of absolute error and root mean square error estimation.

[0078] S202: in response to the first quality factor and the second quality factor satisfying a target condition, iteratively running the first quantum circuit to obtain a measurement result until a total number of actions of a target module reaches a preset iteration termination condition, wherein the target module is a module in the quantum circuit with a phase to be estimated.

[0079] The target condition can be that the first quality factor is less than or equal to the second quality factor, or that a difference between the first quality factor and the second quality factor is within a preset range, or other conditions that can measure the advantages and disadvantages of the quality factors. The preset iteration termination condition is based on N tot , if N tot is less than the first parameter in the first quantum circuit, stop running, because running the first quantum circuit once more will exceed N tot .

[0080] The total number of actions of the target module is the number of times of appearing in each running quantum circuit in the quantum phase estimation process. For example, the second parameter in the first quantum circuit is 1, and the second parameter in the second quantum circuit is also 1. At this time, the two quantum circuits are run 10 times respectively, so as to satisfy the specified condition, and the number of actions of the target module is 20 times at this time, but the first quality factor is greater than the second quality factor, so the first parameter and the second parameter are updated, the second parameter in the updated first quantum circuit is 2, and the second parameter in the second quantum circuit is 3. At this time, the two quantum circuits are run 5 times respectively, so as to satisfy the specified condition, and the number of actions of the target module is 45 times at this time. Correspondingly, the first quality factor is less than the second quality factor, and N tot is 100 times, so the number of times of iteratively running the first quantum circuit is

[0081] The first quality factor and the second quality factor satisfying the target condition indicate that the phase estimation using the first quantum circuit is better than the phase estimation using the second quantum circuit, and the phase estimation using the first quantum circuit is needed subsequently. When the phase estimation using the first quantum circuit is performed, the number of times of measurement after each running is N . left The remaining number of actions of the target module, which changes with the iteratively running of the first quantum circuit, n i remains unchanged, and N left changes accordingly.

[0082] In some possible implementation manners, in response to the first quality factor and the second quality factor not satisfying the target condition, the first parameter and the second parameter of the two quantum circuits are updated respectively to obtain new first quantum circuit and second quantum circuit, and the step of performing evolution on the initial quantum state by using the first quantum circuit and the second quantum circuit respectively to obtain the probability distribution of the current phase to be estimated is performed again.

[0083] In the present application, the updating manner of the first parameter can be It should be noted that the updating of the first parameter and the second parameter is performed when the first quality factor and the second quality factor do not satisfy the target condition, and the updating is not performed when the first quantum circuit is iteratively run to obtain the measurement result.

[0084] S203: determining a target estimation value of the phase to be estimated based on all the measurement results.

[0085] Processing all the measurement results can determine the target estimation value of the phase to be estimated, which can be to determine a median value from all the measurement results and take the median value as the target estimation value, or to calculate an average value of all the measurement results and take the average value as the target estimation value.

[0086] In some possible embodiments of the present application, determining the target estimation value of the phase to be estimated based on all the measurement results can include:

[0087] Determining the target estimation value of the phase to be estimated by using a posterior distribution of the phase to be estimated, wherein the posterior distribution is generated based on all the measurement results

[0088] Generating a posterior distribution of the phase to be estimated based on all the measurement results, and then processing the posterior distribution to obtain the target estimation value. Specifically, selecting a minimum mean square error estimation (MMSE) to obtain the target estimation value as:

[0089]

[0090] When selecting a maximum a posteriori probability estimation (MAP), the target estimation value is obtained as:

[0091]

[0092] In the present application, the limitation of hardware resources is fully considered. Since NISQ (Noisy Intermediate Scale Quantum) machines cannot run very deep quantum circuits, the original quantum phase estimation algorithm cannot be run on existing hardware. However, the scheme provided in the present application can calculate the quantum circuit to be run in the next iteration according to the results of the iteration, and can be run on existing NISQ hardware.

[0093] It can be seen that the first quality factor of the first quantum circuit and the second quality factor of the second quantum circuit are obtained respectively; then, in response to the first quality factor and the second quality factor satisfying a target condition, the first quantum circuit is iteratively run to obtain measurement results until a total number of actions of a target module reaches a preset iteration termination condition; and finally, based on all the measurement results, a target estimation value of the phase to be estimated is determined. The quality factor is used to select a better quantum circuit from two quantum circuits, and the selected quantum circuit is used to obtain data required for estimating the phase, and the final result is obtained by processing the data. The quantum circuit used is constructed by using the statistical inference principle, and does not need to perform quantum Fourier transform, thereby reducing the number of auxiliary quantum bits and greatly reducing the use of quantum bit resources, and thus the phase estimation of a larger scale problem can be realized, thereby promoting scientific research and industrial development.

[0094] Reference is made to Figure 3 , Figure 3 A structure diagram of a quantum phase estimation device provided by an embodiment of the present application corresponds to the flow shown in FIG. 1, and the device comprises: Figure 2

[0095] A first obtaining module 301 is configured to obtain a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit respectively, wherein the first quantum circuit and the second quantum circuit are both circuits for phase estimation constructed by using a statistical inference principle.

[0096] A second obtaining module 302 is configured to, in response to the first quality factor and the second quality factor satisfying a target condition, iteratively run the first quantum circuit to obtain measurement results until a total number of actions of a target module reaches a preset iteration termination condition, wherein the target module is a module with a phase to be estimated in the quantum circuit.

[0097] A determining module 303 is configured to determine a target estimation value of the phase to be estimated based on all the measurement results.

[0098] In some possible embodiments of the present application, the determining module 303 can be specifically configured to:

[0099] determine the target estimation value of the phase to be estimated by using a posterior distribution of the phase to be estimated, wherein the posterior distribution is generated based on all the measurement results.

[0100] In some possible embodiments of the present application, the device can further comprise:

[0101] A third obtaining module is configured to respectively use the first quantum circuit and the second quantum circuit to evolve and measure an initial quantum state to obtain a probability distribution of a current phase to be estimated. ​

[0102] The feedback module is configured to return to execute the third obtaining module until the phase to be estimated satisfies the specified condition when it is determined based on the probability distribution that the phase to be estimated does not satisfy the specified condition.

[0103] In some possible implementation manners of the present application, the first quantum circuit and the second quantum circuit each include a first parameter and a second parameter, wherein the first parameter is a preset phase parameter in the two quantum circuits, and the second parameter is a number of times of action of the target module in one quantum circuit.

[0104] The apparatus can further include:

[0105] The updating module is configured to update the first parameter and the second parameter of the two quantum circuits respectively in response to the first quality factor and the second quality factor not satisfying the target condition, to obtain new first and second quantum circuits, and to return to execute the first obtaining module.

[0106] In some possible implementation manners of the present application, the specified condition is determined based on a probability that the phase to be estimated is within a preset phase range and a preset value, and the preset phase range and the preset value are updated according to the number of iterations.

[0107] In some possible implementation manners of the present application, the specified condition is:

[0108] Pr[θ∈Θ i ]≥1-∈ i

[0109] wherein i is the number of iterations, θ is the phase to be estimated, Θ i is the preset phase range, and 1-∈ i is the preset value.

[0110]

[0111]

[0112] n i =min(2 i-1 ,no pt ,n lim )

[0113]

[0114] n i is the number of times of action of the target module in one quantum circuit, and N tot is a threshold value of the number of times of action of the target module in the quantum phase estimation process. β is a noise level parameter, and n lima threshold of a number of times of actions of a target module that a quantum circuit can bear, θ min a an estimated value of a phase to be estimated.

[0115] In some possible implementation manners of the present application, the quality factor function is:

[0116]

[0117] wherein, a quality factor function, a loss function, n represents a second parameter in a vector form, φ represents a first parameter in a vector form, v represents a number of times of operation of a quantum circuit in a vector form, x represents a measurement result in a vector form, and p(θ|n, φ, v, x) is a posterior distribution of a phase to be estimated.

[0118] In some possible implementation manners of the present application, comprises or

[0119] a number of measurement results obtained in each iteration is wherein, N left a number of times of actions of a target module.

[0120] It can be seen that the present application first respectively obtains a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit; then in response to the first quality factor and the second quality factor satisfying a target condition, iteratively operates the first quantum circuit to obtain measurement results until a total number of actions of a target module reaches a preset iteration termination condition; and finally determines a target estimated value of a phase to be estimated based on all the measurement results. The quality factor is used to select a better quantum circuit from two quantum circuits, and the selected quantum circuit is used to obtain data required for estimating a phase, and the data is processed to obtain a final result. The quantum circuit used is constructed by using a statistical inference principle, and does not need to perform quantum Fourier transform, thereby reducing the number of auxiliary quantum bits and greatly reducing the use of quantum bit resources, and thus the phase estimation of a larger scale problem can be implemented, thereby promoting scientific research and industrial development.

[0121] The present application also provides a storage medium having a computer program stored therein, wherein the computer program is configured to implement the steps in any of the method embodiments described above when executed.

[0122] Specifically, in the present embodiment, the storage medium described above can be configured to store a computer program for implementing the following steps:

[0123] S201: obtain a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit respectively, wherein the first quantum circuit and the second quantum circuit are both circuits for phase estimation constructed by using a statistical inference principle;

[0124] S202: in response to the first quality factor and the second quality factor satisfying a target condition, iteratively running the first quantum circuit to obtain measurement results until a total number of actions of a target module reaches a preset iteration termination condition, wherein the target module is a module with a phase to be estimated in the quantum circuit;

[0125] S203: determining a target estimated value of the phase to be estimated based on all the measurement results.

[0126] Embodiments of the present application also provide an electronic device including a memory and a processor, the memory storing a computer program, and the processor being configured to run the computer program to implement the steps in any of the above method embodiments.

[0127] Specifically, the above electronic device can further include a transmission device connected to the processor and an input / output device connected to the processor.

[0128] Specifically, in the present embodiment, the processor can be configured to implement the following steps through the computer program:

[0129] S201: obtain a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit respectively, wherein the first quantum circuit and the second quantum circuit are both circuits for phase estimation constructed by using a statistical inference principle;

[0130] S202: in response to the first quality factor and the second quality factor satisfying a target condition, iteratively running the first quantum circuit to obtain measurement results until a total number of actions of a target module reaches a preset iteration termination condition, wherein the target module is a module with a phase to be estimated in the quantum circuit;

[0131] S203: determining a target estimated value of the phase to be estimated based on all the measurement results.

[0132] The above embodiments according to the drawings explain the structure, features and effects of the present application in detail. The above description is only the preferred embodiments of the present application, but the present application is not limited to the drawings. Any changes or modifications made in accordance with the concept of the present application, or equivalent embodiments with equivalent changes, are still within the scope of the present application.

Claims

1. A quantum phase estimation method, characterized in that: The method comprises: Obtaining a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit, respectively, wherein both the first quantum circuit and the second quantum circuit are circuits for phase estimation constructed using statistical inference principles, and the quality factors are calculated based on a quality factor function using measurement results obtained from a current operation of the quantum circuits; In response to the first quality factor and the second quality factor satisfying the target condition, iteratively running the first quantum circuit to obtain measurement results until the total number of effects of the target module reaches a preset iteration termination condition, wherein the target module is a module with a phase to be estimated in the quantum circuit, the target condition is a condition that measures whether the first quality factor is better than the second quality factor, and the preset iteration termination condition is The difference between the total number of times the current target module has been used and the total number of times the current target module has been used is less than the second parameter of the first quantum circuit. is the threshold value of the number of times the target module acts in the quantum phase estimation process, and the second parameter is the number of times the target module acts in a quantum circuit; Based on all the measurement results, a target estimated value of the phase to be estimated is determined.

2. The method according to claim 1, characterized in that The determining, based on all measurement results, a target estimated value of the phase to be estimated, includes: A target estimated value of the phase to be estimated is determined using a posterior distribution of the phase to be estimated, wherein the posterior distribution is generated based on all measurement results.

3. The method according to claim 1 or 2, characterized in that The method further comprises: Using the first quantum circuit and the second quantum circuit respectively, evolving and measuring the initial quantum state to obtain a probability distribution of the current phase to be estimated; When it is determined based on the probability distribution that the phase to be estimated does not satisfy the specified condition, returning to the step of respectively evolving and measuring the initial quantum state using the first quantum circuit and the second quantum circuit to obtain the probability distribution of the current phase to be estimated, until the phase to be estimated satisfies the specified condition.

4. The method according to claim 3, characterized in that The first quantum circuit and the second quantum circuit both include a first parameter and a second parameter, wherein the first parameter is a pre-set phase parameter in the two quantum circuits; The method further comprises: In response to the first quality factor and the second quality factor not satisfying the target condition, the first parameters and the second parameters of the two quantum circuits are updated respectively to obtain new first quantum circuits and second quantum circuits, and the process returns to the step of evolving the initial quantum state using the first quantum circuit and the second quantum circuit respectively to obtain the probability distribution of the current phase to be estimated.

5. The method according to claim 4, characterized in that The specified condition is determined based on the probability that the phase to be estimated is within a set phase range and a set value, and the set phase range and the set value are updated according to the number of iterations.

6. The method according to claim 5, characterized in that The specified conditions are: in, is the number of iterations, is the phase to be estimated, is the phase range to be set, is the set value; ; , is the noise level parameter, is the threshold of the number of times a quantum circuit can carry the action of the target module, for ; is the estimated value of the phase to be estimated.

7. The method according to claim 6, characterized in that The quality factor function is: in, is the quality factor function, is the loss function, represents the second parameter in vector form, represents the first parameter in vector form, represents the number of times a quantum circuit in vector form is run, Represents the measurement results in vector form, is the posterior distribution of the phase to be estimated.

8. The method according to claim 7, characterized in that include or ; The number of measurements obtained in each iteration is ,in, The number of remaining target module actions.

9. A quantum phase estimation device, characterized in that The device comprises: a first obtaining module, configured to respectively obtain a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit, wherein both the first quantum circuit and the second quantum circuit are circuits for phase estimation constructed using statistical inference principles, and the quality factors are calculated based on a quality factor function using measurement results obtained from a current operation of the quantum circuits; A second acquisition module is configured to iteratively run the first quantum circuit in response to the first quality factor and the second quality factor satisfying a target condition to obtain a measurement result until the total number of actions of the target module reaches a preset iteration termination condition, wherein the target module is a module with a phase to be estimated in the quantum circuit, the target condition is a condition for measuring whether the first quality factor is better than the second quality factor, and the preset iteration termination condition is The difference between the total number of times the current target module has been used and the total number of times the current target module has been used is less than the second parameter of the first quantum circuit. is the threshold value of the number of times the target module acts in the quantum phase estimation process, and the second parameter is the number of times the target module acts in a quantum circuit; A determination module is used to determine a target estimated value of the phase to be estimated based on all the measurement results.

10. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to implement the method according to any one of claims 1 to 8 when executed.

11. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the computer program to implement the method according to any one of claims 1 to 8.

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