Quantum phase estimation method and device

Abstract

The invention discloses a quantum phase estimation method and device, and the method comprises the steps: obtaining a first quality factor of a first quantum circuit and a second quality factor of a second quantum circuit, and enabling the first quantum circuit and the second quantum circuit to be lines which are constructed through the statistical inference principle and are used for phase estimation; in response to the condition that the first quality factor and the second quality factor meet a target condition, iteratively operating the first quantum circuit to obtain a measurement result until the total action number of a target module reaches a preset iteration termination condition, the target module being a module with a to-be-estimated phase in the quantum circuit; and determining a target estimation value of the to-be-estimated phase based on all the measurement results. According to the embodiment of the invention, the method achieves the phase estimation of a large-scale problem.

Description

Technical Field

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

[0002] Quantum Phase Estimation Algorithm (QPE) is a core step in many quantum algorithms, such as Shor's algorithm, HHL (Harrow Hassidim and Lloyd) algorithm, etc. It is also a very useful step to achieve computational acceleration in many other different quantum computing applications. The role of quantum phase estimation is to quickly estimate the eigenvalue of a unitary transformation. Since the eigenvalues ​​of a unitary matrix are all complex numbers modulo 1, for a unitary matrix, its eigenvalue and phase are basically equivalent, so it is only necessary to estimate the phase of its eigenvalue.

[0003] Eigenvalues ​​can carry information about physical parameters and represent the characteristics of a physical process. Solving for their eigenvalues ​​can be used to study the ground state and excited state energy of quantum systems, which is of great significance for scientific research and industrial development. For example, in lithium battery technology, efficient extraction of ground state properties can promote the development of lithium battery technology. Existing quantum phase estimation methods are based on quantum Fourier transforms. In the estimation process, auxiliary quantum bits need to be introduced, and their number is about twice that of data quantum bits. With the current scale of quantum computers, it is difficult to achieve phase estimation for larger-scale problems, which limits 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 a statistical inference principle;

[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 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;

[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 by 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 comprises:

[0012] Using the first quantum circuit and the second quantum circuit respectively, the initial quantum state is evolved and measured to obtain the 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 execute 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 phase parameter preset 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 step of respectively evolving 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 is returned to be executed.

[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 to be 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 actions of the target module 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 runs, 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, the device comprising:

[0032] A first obtaining module, used 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 using a statistical inference principle;

[0033] a second obtaining module, 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 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;

[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, in which a computer program is stored, 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 with the prior art, the present application first obtains the first quality factor of the first quantum circuit and the second quality factor of the second quantum circuit respectively; then, in response to the first quality factor and the second quality factor satisfying the target condition, it iteratively runs the first quantum circuit to obtain the measurement result until the total number of actions of the target module reaches the preset iteration termination condition; then, based on all the measurement results, the target estimated value of the phase to be estimated is determined. The quality factor is used to select a better quantum circuit from the two quantum circuits, and then the selected quantum circuit is used to obtain the data required for estimating the phase, and the data is processed to obtain the final result. The quantum circuit used is constructed using the principle of statistical inference, and does not require quantum Fourier transform, which reduces the number of auxiliary quantum bits and greatly reduces the use of quantum bit resources, thereby realizing phase estimation of larger-scale problems, thereby promoting scientific research and industrial development. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a network block diagram of a quantum phase estimation system provided in 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 1 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 memories, 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 intranets, local area networks, mobile communication networks and combinations thereof. The connection method can be wired, wireless communication links or optical fiber cables, etc.

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

[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 the 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 any application program executed by it 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 There are two major parts in it: 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 calculations.

[0048] The classical processing system 160 and the quantum processing system 170 may be integrated into one device or distributed in two different devices. For example, the first device including the classical processing system 160 runs a classical computer operating system, on which quantum application development tools and services are provided, as well as storage and network services required for quantum applications. Users develop quantum applications through the quantum application development tools and services thereon, and send quantum programs to the 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 measurement and control system. The quantum processor 170 implements the quantum algorithm corresponding to the quantum program according to the instructions.

[0049] In the classic processing system 160 based on silicon chips, the unit of the classic processor 161 is a CMOS tube. This computing unit is not limited by time and coherence, that is, this computing unit is not limited by the length of use and is available at any time. In addition, in the silicon chip, the number of such computing units is also sufficient. At present, the number of computing units in a classic processor is tens of thousands. The number of computing units is sufficient and the computing logic that can be selected by the CMOS tube is fixed, for example: AND logic. When calculating with the help of CMOS tubes, a large number of CMOS tubes are combined with limited logic functions to achieve the calculation effect.

[0050] Unlike this logic unit 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. Making full use of the quantum bit within the available use time of the quantum bit is a key problem in quantum computing. In addition, the number of quantum bits in a quantum computer is one of the representative indicators of the performance of a quantum computer. Each quantum bit realizes the computing function through the logical function configured on demand. In view of the limited number of quantum bits, the logical functions in the field of quantum computing are diverse, such as: Hadamard gate (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. In quantum computing, it is necessary to use limited quantum bits combined with a variety of logical functions to achieve the computing effect.

[0051] Based on these differences, the design of the logical function acting on quantum bits (including the design of whether the quantum bits are used and the design of the efficiency of the use of each quantum bit) is the key to improving the computing performance of quantum computers, and requires special design. The above-mentioned design for quantum bits is a technical problem that ordinary computing devices do not need to consider or face. Based on this, in order to achieve phase estimation of larger-scale problems in quantum computing, this application proposes a quantum phase estimation method and device, which aims to achieve phase estimation of larger-scale problems.

[0052] See also Figure 2 , Figure 2 A schematic flow chart of a quantum phase estimation method provided in an embodiment of the present application may include the following steps:

[0053] S201: 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.

[0054] The first quantum circuit and the second quantum circuit are circuits for quantum phase estimation, but quantum phase estimation does not require 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 there is at least one basic circuit unit with different parameters. 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 the quality factor function using the measurement results obtained from the current operation of the quantum circuit. Specifically, the quality factor function is constructed based on the 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 eliminating the influence of other peaks on θ estimation and eliminating the ambiguity of the quantum phase estimation algorithm.

[0056] In the present application, 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. Changing the first parameter and / or the second parameter may change the measurement result of the quantum circuit, thereby possibly obtaining different quality factors, and then a better quantum circuit may be selected to estimate the phase.

[0057] The basic circuit units of quantum circuits include unitary operations Unitary operation with phase φ The U operator is a unitary operator, realizing U|ψ>=e2πiθ |ψ>. is the target module, θ is the phase to be estimated, |ψ> is the quantum state, the first parameter is the phase φ in the quantum circuit, and the second parameter is the phase in a quantum circuit. The number of times it acts.

[0058] In some possible implementations of the present application, the method may further include:

[0059] Using the first quantum circuit and the second quantum circuit respectively, the initial quantum state is evolved and measured to obtain the 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 the specified condition, returning to execute 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.

[0061] When the first quantum circuit and the second quantum circuit meet certain conditions, the advantages and disadvantages of the two quantum circuits are judged, which can improve the accuracy of phase estimation. When judging whether the first quantum circuit and the second quantum circuit meet certain conditions, the first quantum circuit and the second quantum circuit are used respectively to evolve and measure the same initial quantum state to obtain the probability distribution of θ, and then the probability distribution is used to obtain the phase value of the current phase to be estimated, and the phase value is used to judge whether the specified conditions are met. If the specified conditions are met, the measurement results of the two quantum circuits are used respectively to calculate the corresponding quality factor. If the specified conditions are not met, the two quantum circuits are iteratively run until the specified conditions are met. It should be noted that during the iterative operation, 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 implementations of the present application, 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.

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

[0064] In some implementations of the present application, the specified condition may be:

[0065] Pr[θ∈Θ i ]≥1-∈ i

[0066] Where i is the number of iterations, θ is the phase to be estimated, and θ i is the phase range to be set, 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 actions of the target module 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.

[0072] In some possible implementations of the present application, the quality factor function is a function of the loss function and the posterior distribution of the phase to be estimated. Specifically, the quality factor function may be:

[0073]

[0074] 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 runs, 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, Can include or This achieves the Heisenberg limit for both absolute and RMS error estimates.

[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 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.

[0079] The target condition can be that the first quality factor is less than or equal to the second quality factor, or the difference between the first quality factor and the second quality factor is within a preset range, or other conditions that can measure the quality of the quality factor. The preset iteration termination condition is based on N tot If N tot If the difference between the total number of times the current target module has been used is less than the second parameter in the first quantum circuit, the operation is stopped, because if the first quantum circuit is run again, it will exceed N tot .

[0080] The total number of actions of the target module is the number of times it appears in each quantum circuit run during 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 to meet the specified conditions. At this time, the number of actions of the target module is 20 times, but the first quality factor is greater than the second quality factor, then the first parameter and the second parameter are updated. After the update, the second parameter in the first quantum circuit is 2, and the second quantum circuit is 3. At this time, the two quantum circuits are run 5 times respectively to meet the specified conditions. At this time, the number of actions of the target module is 45 times. The corresponding first quality factor is less than the second quality factor, N tot is 100 times, then the number of iterations of the first quantum circuit is

[0081] The first quality factor and the second quality factor meet the target conditions, indicating that the phase estimation using the first quantum circuit is better than the second quantum circuit. The first quantum circuit needs to be used for phase estimation in the future. When the first quantum circuit is used for phase estimation, the number of measurements after each run is N left is the number of remaining target module actions. As the first quantum circuit is iterated, n i No change, N left Change accordingly.

[0082] In some possible implementations, in response to the first quality factor and the second quality factor not satisfying a target condition, the first parameters and the second parameters of the two quantum circuits are updated respectively to obtain a new first quantum circuit and a second quantum circuit, and the step of respectively evolving the initial quantum state using the first quantum circuit and the second quantum circuit to obtain a probability distribution of the current phase to be estimated is returned to be executed.

[0083] In this application, the updating method of the first parameter can be It should be noted that the first parameter and the second parameter are updated when the first quality factor and the second quality factor do not meet the target condition, and are not updated when the first quantum circuit is iteratively operated to obtain the measurement result.

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

[0085] All measurement results are processed to determine a target estimated value of the phase to be estimated. The median value may be determined from all measurement results and used as the target estimated value. Alternatively, the average value of all measurement results may be calculated and used as the target estimated value.

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

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

[0088] The posterior distribution of the phase to be estimated is generated by all measurement results, and then the posterior distribution is processed to obtain the target estimated value. Specifically, the minimum mean square error estimation (MMSE) is selected, and the target estimated value is obtained as follows:

[0089]

[0090] When the maximum a posteriori probability estimate (MAP) is selected, the target estimate is:

[0091]

[0092] In this 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. The solution provided in this application, under the limitation of limited resources, calculates the quantum circuit to be run in the next iteration based on the results of the iteration, and can be run on existing NISQ hardware.

[0093] It can be seen that the present application first obtains the first quality factor of the first quantum circuit and the second quality factor of the second quantum circuit respectively; then, in response to the first quality factor and the second quality factor satisfying the target condition, iteratively runs the first quantum circuit to obtain the measurement result until the total number of actions of the target module reaches the preset iteration termination condition; then, based on all the measurement results, determines the target estimated value of the phase to be estimated. The quality factor is used to select a better quantum circuit from the two quantum circuits, and then the selected quantum circuit is used to obtain the data required for estimating the phase, and the data is processed to obtain the final result. The quantum circuit used is constructed using the principle of statistical inference, and does not require quantum Fourier transform, which reduces the number of auxiliary quantum bits and greatly reduces the use of quantum bit resources, thereby realizing phase estimation of larger-scale problems, thereby promoting scientific research and industrial development.

[0094] See also Figure 3 , Figure 3 A schematic diagram of the structure of a quantum phase estimation device provided in an embodiment of the present application, and Figure 2 Corresponding to the process shown, the device includes:

[0095] A first obtaining module 301 is used 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 using statistical inference principles;

[0096] A second obtaining module 302 is configured to iteratively run the first quantum circuit to obtain a measurement result in response to the first quality factor and the second quality factor satisfying a target condition, until the 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] The determination module 303 is used to determine a target estimated value of the phase to be estimated based on all the measurement results.

[0098] In some possible implementations of the present application, the determining module 303 may be specifically used to:

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

[0100] In some possible implementations of the present application, the device may further include:

[0101] A third acquisition module is used to respectively use the first quantum circuit and the second quantum circuit to evolve and measure the initial quantum state to obtain the probability distribution of the current phase to be estimated;

[0102] The feedback module is used for returning to execute the third obtaining module when it is determined based on the probability distribution that the phase to be estimated does not meet the specified condition, until the phase to be estimated meets the specified condition.

[0103] In some possible implementations of the present application, 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;

[0104] The device may also include:

[0105] An updating module is used for updating the first parameters and the second parameters of the two quantum circuits respectively in response to the first quality factor and the second quality factor not meeting the target condition, obtaining a new first quantum circuit and a new second quantum circuit, and returning to execute the first obtaining module.

[0106] In some possible implementations of the present application, 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.

[0107] In some possible implementations of the present application, the specified conditions are:

[0108] Pr[θ∈Θ i ]≥1-∈ i

[0109] Where i is the number of iterations, θ is the phase to be estimated, Θ i is the phase range to be set, 1-∈ i is the set value;

[0110]

[0111]

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

[0113]

[0114] n i is the number of actions of the target module 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 limis 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.

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

[0116]

[0117] 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 runs, 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.

[0118] In some possible implementations of the present application, include or

[0119] The number of measurements obtained in each iteration is Among them, N left The number of remaining target module actions.

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

[0121] An embodiment of the present application further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to implement the steps of any of the above method embodiments when executed.

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

[0123] S201: 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 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 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;

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

[0126] An embodiment of the present application further 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 the steps in any one of the above method embodiments.

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

[0128] Specifically, in this embodiment, the processor may be configured to implement the following steps through a computer program:

[0129] S201: 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 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 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;

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

[0132] The above describes in detail the structure, features and effects of the present application based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present application, but the present application does not limit the scope of implementation to what is shown in the drawings. Any changes made in accordance with the concept of the present application, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection 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 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 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; 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 step of determining a target estimated value of the phase to be estimated based on all the measurement results includes: A target estimated value of the phase to be estimated is determined by 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, the initial quantum state is evolved and measured to obtain the 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 execute 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 phase parameter preset in the two quantum circuits, and the second parameter is the number of times the target module acts in one quantum circuit; 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 step of respectively evolving 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 is returned to be executed.

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: Pr[θ∈Θ i ]≥1-∈ i Where i is the number of iterations, θ is the phase to be estimated, Θ i is the phase range to be set, 1-∈ i is the set value; n i =min(2 i-1 ,n opt ,n lim ) n i is the number of actions of the target module 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.

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, n represents the second parameter in vector form, φ represents the first parameter in vector form, ν represents the number of times a quantum circuit in vector form runs, 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.

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

9. A quantum phase estimation device, characterized in that: The device comprises: A first obtaining module, used 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 using a statistical inference principle; a second obtaining module, 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 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; 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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