Method for determining quantum state predicted value and related device

Abstract

The invention discloses a method for determining a quantum state predicted value and a related device, and relates to the technical field of quantum information. Comprising the steps that after first quantum state data and second quantum state data of a quantum system to be predicted are determined, the two kinds of data are input into a quantum system evolution predictor, and third quantum state data are obtained; the second quantum state data changing along with time is captured through continuous weak measurement, the problem that an original evolution path of the quantum system is damaged by projection measurement in a traditional scheme is solved, and it is guaranteed that the quantum system evolves according to the original evolution path; meanwhile, after the second quantum state data closely related to the space-time correlation noise is captured through continuous weak measurement, a time sequence model trained in advance, namely a quantum state evolution predictor is adopted to learn the space-time correlation characteristics implied in the second quantum state data, the prediction precision in a complex environment is improved, and the prediction accuracy is improved. The quantum state data in quantum system evolution can be accurately predicted.

Description

Technical Field

[0001] The present application relates to the field of quantum information technology, and in particular to a method and related device for determining a predicted value of a quantum state. Background Art

[0002] In the field of quantum information, quantum computing, quantum sensing, quantum precision measurement, and quantum communication all rely on quantum state data from the evolution of quantum dynamics. Traditional approaches typically use projection-based measurement methods to predict the evolution of quantum systems and obtain the associated quantum state data. However, such measurements disrupt the original evolutionary path of the quantum system, thereby affecting the accuracy of the resulting quantum state data. Therefore, accurately predicting the quantum state data corresponding to the evolution of a quantum system has become a key technical issue that needs to be addressed in the field of quantum information technology. Summary of the Invention

[0003] Based on the above problems, the present application provides a method for determining the predicted value of a quantum state, so as to more accurately predict the quantum data corresponding to the evolution of a quantum system.

[0004] The embodiments of this application disclose the following technical solutions:

[0005] In a first aspect, the present application discloses a method for determining a predicted value of a quantum state, comprising:

[0006] Determining first quantum state data and second quantum state data of a quantum system to be predicted; the first quantum state data is quantum state data obtained by performing projection measurement on initial quantum state data of the quantum system to be predicted; and the second quantum state data is quantum state data that changes with time and is obtained by performing continuous weak measurement on the quantum system to be predicted;

[0007] A quantum system evolution predictor is used to obtain third quantum state data based on the first quantum state data and the second quantum state data; the quantum system evolution predictor is a model obtained by training a time series model and outputting quantum state data corresponding to the evolution of the quantum state.

[0008] In an optional implementation, the step of acquiring the second quantum state data includes:

[0009] Determining a target time period; the initial moment of the target time period is the moment when the initial quantum state data in the quantum system to be predicted appears;

[0010] The second quantum state data is obtained by recording the quantum state data of the quantum system to be predicted at multiple time points within the target time period through the continuous weak measurement.

[0011] In an optional implementation, the step of training the quantum system evolution predictor includes:

[0012] Acquire a plurality of sample data; each of the sample data includes first sample quantum state data and second sample quantum state data; the first sample quantum state data is quantum state data obtained by performing the projection measurement on the initial quantum state data of the sample quantum system; the second sample quantum state data is quantum state data obtained by performing the continuous weak measurement on the sample quantum system and varying with time within a sample time period; the initial moment of the sample time period is the moment when the initial quantum state data of the sample quantum system appears;

[0013] Obtaining a label for the sample data; the label is quantum state data obtained by performing the projection measurement on the final quantum state data of the sample quantum system; the appearance time of the final quantum state data of the sample quantum system is the end time of the sample time period;

[0014] The model to be trained is trained based on the sample data and the labels corresponding to the sample data to obtain the quantum system evolution prediction model.

[0015] In an optional implementation, the second sample quantum state data acquisition step includes:

[0016] Determining the sample time period; the initial moment of the sample time period is the moment when the initial quantum state data of the quantum system to be predicted appears;

[0017] Through the continuous weak measurement, the quantum state data of the sample quantum system at different moments in the evolution process within the sample time period are recorded to obtain the second sample quantum state data.

[0018] In an optional implementation, obtaining the label of the sample data includes:

[0019] Taking the quantum state data of the sample quantum system at the end of the sample time period as the final quantum state data of the sample quantum system;

[0020] The projection measurement is performed on the final quantum state data of the sample quantum system to obtain the label.

[0021] In an optional implementation, the training of the model to be trained based on the sample data and the labels corresponding to the sample data to obtain the quantum system evolution prediction model includes:

[0022] Inputting the sample data into the model to be trained to obtain a predicted label;

[0023] The value of the loss function is calculated based on the predicted label and the sample label; and the parameter gradient in the to-be-trained model is updated based on the gradient of the loss function, and the training is stopped after a preset training cutoff condition is met, thereby obtaining the quantum system evolution prediction model.

[0024] In an optional implementation, the model to be predicted is a recurrent neural network or a long short-term memory network.

[0025] In a second aspect, the present application discloses a device for determining a predicted value of a quantum state, the device comprising:

[0026] A reference data acquisition module is used to determine first quantum state data and second quantum state data of the quantum system to be predicted; the first quantum state data is quantum state data obtained by projecting the initial quantum state data of the quantum system to be predicted; the second quantum state data is quantum state data that varies with time and obtained by continuous weak measurement of the quantum system to be predicted;

[0027] A target data acquisition module is used to obtain third quantum state data based on the first quantum state data and the second quantum state data through a quantum system evolution predictor; the quantum system evolution predictor is a model obtained by training a time series model and outputting quantum state data corresponding to the evolution of the quantum state.

[0028] A third aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0029] A fourth aspect of the present application discloses an electronic device, comprising:

[0030] a memory having a computer program stored thereon;

[0031] A processor is used to execute the computer program in the memory to implement the steps of any one of the methods of the first aspect.

[0032] Compared with the existing technology, this application has the following beneficial effects:

[0033] The present application discloses a method for determining a quantum state prediction value, comprising: after determining first quantum state data and second quantum state data of a quantum system to be predicted, inputting the above two data into a quantum system evolution predictor to obtain third quantum state data. In the present application, the second quantum state data that changes with time is captured by continuous weak measurement, which solves the problem of projection measurement destroying the original evolution path of the quantum system in the traditional solution, and ensures that the quantum system evolves according to the original evolution path; at the same time, after the continuous weak measurement captures the second quantum state data that is closely related to the spatiotemporal correlation noise, a pre-trained time series model, i.e., a quantum state evolution predictor, is used to learn the spatiotemporal correlation characteristics implicit in the second quantum state data, thereby improving the prediction accuracy in complex environments and being able to more accurately predict the quantum state data in the evolution of the quantum system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0035] Figure 1 A flowchart of a method for determining a predicted value of a quantum state provided in an embodiment of the present application;

[0036] Figure 2 A flowchart of a training method for a quantum system evolution predictor provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram showing how a loss function changes during the training process of a quantum system evolution prediction model provided in an embodiment of the present application;

[0038] Figure 4 A schematic diagram of the effect of a quantum system evolution prediction model provided in an embodiment of the present application on predicting a discrete quantum system;

[0039] Figure 5 A schematic diagram of the effect of a quantum system evolution prediction model provided in an embodiment of the present application on predicting a continuous quantum system;

[0040] Figure 6 A schematic structural diagram of a quantum state prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In the field of quantum information, quantum computing, quantum sensing, quantum precision measurement, and quantum communication all rely on quantum state data from the evolution of quantum dynamics. Traditional approaches typically use projection-based measurement methods to predict the evolution of quantum systems and obtain the associated quantum state data. However, such measurements disrupt the original evolutionary path of the quantum system, thereby affecting the accuracy of the resulting quantum state data. Therefore, accurately predicting the quantum state data corresponding to the evolution of a quantum system has become a key technical issue that needs to be addressed in the field of quantum information technology.

[0042] The present application discloses a method for determining a quantum state prediction value, comprising: after determining first quantum state data and second quantum state data of a quantum system to be predicted, inputting the above two data into a quantum system evolution predictor to obtain third quantum state data. In the present application, the second quantum state data that changes with time is captured by continuous weak measurement, which solves the problem of projection measurement destroying the original evolution path of the quantum system in the traditional solution, and ensures that the quantum system evolves according to the original evolution path; at the same time, after the continuous weak measurement captures the second quantum state data that is closely related to the spatiotemporal correlation noise, a pre-trained time series model, i.e., a quantum state evolution predictor, is used to learn the spatiotemporal correlation characteristics implicit in the second quantum state data, thereby improving the prediction accuracy in complex environments and being able to more accurately predict the quantum state data in the evolution of the quantum system.

[0043] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0044] Figure 1 A flowchart of a method for determining a predicted value of a quantum state provided in an embodiment of the present application.

[0045] Combine Figure 1 As shown, the method for determining the predicted value of a quantum state disclosed in this application includes:

[0046] S101, determining first quantum state data and second quantum state data of a quantum system to be predicted.

[0047] The quantum system to be predicted in this application refers to a quantum system whose state needs to be predicted after a period of quantum evolution.

[0048] The quantum system to be predicted in this application can be a physical model coupled to an environment. Because the environment is unknowable and inaccessible, the quantum system undergoes a spatiotemporally correlated non-Markov noise evolution. If the coupling coefficient or time scale is sufficiently small, this can be approximated as Markov noise. The method for determining the predicted value of the quantum state disclosed in this application is particularly suitable for physical models coupled to an environment, because the large amount of noise in such models exacerbates the difficulty of accurately characterizing the model's evolution equations.

[0049] Of course, the quantum system to be predicted in this application may also be a physical system that is independent of the environment, that is, a closed physical system.

[0050] It is understandable that after determining the quantum system to be predicted, the state of the quantum system to be predicted needs to be initialized; after completing the initialization of the quantum system to be predicted, it is necessary to extract the characteristics of the subspace in which the quantum state of the evolving quantum system to be predicted is located.

[0051] It should be noted that the initial state of the quantum system to be predicted and its evolution process determine the complexity of the features that need to be extracted and the expected values ​​of the observables.

[0052] The quantum state prediction method disclosed in this application is not limited to the initial state and evolution process of the quantum system to be predicted. However, for different initial states and different evolution processes, it is necessary to extract different characteristics generated by the quantum system to be predicted during the evolution process. The first quantum state data, second quantum state data, initial quantum state data, final quantum state data, first sample quantum state data, and second sample quantum state data in this application are all extracted characteristics of the quantum system.

[0053] Taking the direct product state of discrete variables as an example, if the Hamiltonian of the quantum system to be predicted only includes the local Pauli operator, then the quantum system to be predicted is always in the direct product state space during its evolution. When obtaining the expected values ​​of the three observable quantities, such as The evolutionary behavior of the quantum system to be predicted can be fully predicted; if the Hamiltonian of the quantum system to be predicted contains two-body or multi-body correlated Pauli operators, the quantum system to be predicted is in the spin-compressed state space during the evolution process, and the expected values ​​of the nine observable quantities are obtained, such as The evolutionary behavior of the quantum system to be predicted can be fully predicted; considering the case of initialization to the maximally entangled state, if the Hamiltonian of the quantum system to be predicted only has local Pauli operators, the system to be predicted is still in the maximally entangled state space during the evolution process, and nine observable expectation values ​​are required to fully characterize the system at this time; if the Hamiltonian of the quantum system to be predicted contains two-body or multi-body correlated Pauli operators, the system to be predicted will have a disentanglement process during the evolution process, and the characterization project needs to be specifically implemented in combination with the entanglement degree.

[0054] It should be noted that and represent the x-, y-, and z-direction components of the Pauli operator on the i-th qubit, respectively; and Respectively represent measuring the Pauli operators in the x, y, and z directions on all qubits and taking the sum of the average values.

[0055] Taking continuous variables as an example, since the initial state of the quantum system to be predicted is usually a vacuum state or a coherent state, it is only necessary to consider whether the Hamiltonian of the quantum system to be predicted contains a compression operator. If the quantum system to be predicted does not contain a compression operator, the quantum system to be predicted will only evolve in the coherent state space, so the first-order moments of the orthogonal components p and q are required { ， <q>} as a feature; if the Hamiltonian of the quantum system to be predicted contains a compression operator, the quantum system to be predicted evolves in a larger Gaussian state, and the first-order moments of p and q are required {< / q> ， <q>} and the second moment { <p 2 >, 2 >,<pq+qp>} as a feature.

[0056] It should be noted that p and q represent the momentum operator and position operator of the quantum system, respectively;​< / q> and <q>represent the expected values ​​of momentum and position operators respectively; <p 2 > and 2 >represent the expected value of the square of momentum and position operators respectively;<pq+qp> Represents the expected value of the product of momentum and position operators.

[0057] The first quantum state data in this application is the quantum state data obtained by projection measurement of the initial quantum state data of the quantum system to be predicted; the second quantum state data is the quantum state data that changes with time and is obtained by continuous weak measurement of the quantum system to be predicted.

[0058] For example, after determining the quantum system to be predicted, it is necessary to apply random quantum gates or control pulses to the quantum system to make it evolve, and take a random state sampled during the evolution process as a random initial state, which is recorded as the initial quantum state data of the quantum system to be predicted; then, a projection measurement is performed on the initial quantum state data to obtain the first quantum state data.

[0059] The moment when the first quantum state data of the quantum system to be predicted appears is taken as the first moment. With the first moment as the initial moment, a period of time is determined as the target time period. Then, continuous weak measurement is used to record the quantum state data of the quantum system to be predicted at multiple time points within the target time period to obtain the second quantum state data. The second quantum state data carries the spatiotemporal correlation evolution characteristics of the quantum system to be predicted.

[0060] It should be noted that the core of continuous weak measurement is that it has little impact on the quantum system and can continuously monitor the system over a long period of time, rather than projecting the system to a specific state at one time.

[0061] In traditional projection measurements, the process causes wave function collapse, meaning the state of the quantum system suddenly jumps to an eigenstate of the operator being measured. This type of projection measurement has a drastic impact on the system, and the measurement results are discrete. In contrast, continuous weak measurement is a "gentle" measurement method that has a lesser impact on the quantum system and does not cause a drastic collapse of the system's state.

[0062] S102: Obtain third quantum state data based on the first quantum state data and the second quantum state data through a quantum system evolution predictor.

[0063] The quantum system evolution predictor in this application is a model that is trained on a time series model and outputs quantum state data corresponding to the quantum state evolution. The subsequent examples of this application will detail the training data collection process of the quantum system evolution predictor and the model training process.

[0064] ​After obtaining the first quantum state data and the second quantum state data of the quantum system to be predicted, the above two data are simultaneously input into the quantum system evolution predictor. The quantum system evolution predictor learns the spatiotemporal correlation evolution characteristics in the first quantum state data and the second quantum state data, and outputs the third quantum state data of the quantum system to be predicted that appears at the end time of the target time period.

[0065] In summary, the present application discloses a method for determining a quantum state prediction value, comprising: after determining the first quantum state data and the second quantum state data of the quantum system to be predicted, inputting the above two data into a quantum system evolution predictor to obtain third quantum state data. In the present application, the second quantum state data that changes with time is captured by continuous weak measurement, which solves the problem of projection measurement destroying the original evolution path of the quantum system in the traditional scheme, and ensures that the quantum system evolves according to the original evolution path; at the same time, after the continuous weak measurement captures the second quantum state data that is closely related to the spatiotemporal correlation noise, a pre-trained time series model, i.e., a quantum state evolution predictor, is used to learn the spatiotemporal correlation characteristics implicit in the second quantum state data, thereby improving the prediction accuracy in complex environments and being able to more accurately predict the quantum state data in the evolution of the quantum system.

[0066] Figure 2 A flowchart of a method for training a quantum system evolution predictor provided in an embodiment of the present application. Figure 2 As shown, the training method of the quantum system evolution predictor disclosed in this application includes:

[0067] S201: Acquire multiple sample data and labels corresponding to each sample data.

[0068] Each sample data includes first sample quantum state data and second sample quantum state data. The first sample quantum state data is the quantum state data obtained by projecting the initial quantum state data of the sample quantum system; the second sample quantum state data is the quantum state data that changes over time within the sample time period obtained by continuous weak measurement of the sample quantum system; and the label corresponding to each sample data is the quantum state data obtained by projecting the final quantum state data of the sample quantum system.

[0069] The initial moment of the sample time period is the moment when the initial quantum state data of the sample quantum system appears; the end moment of the sample time period is the moment when the final quantum state data of the sample quantum system appears.

[0070] For example, after determining the sample quantum system, a random quantum gate or control pulse is applied to the sample quantum system to make it evolve, and a random state sampled during the evolution process is used as the random initial state σ i , recorded as the initial quantum state data of the sample quantum system; then the sample quantum system is allowed to undergo free evolution or controlled evolution for a period of time (the sample time period in this application) to obtain the final quantum state data of the sample quantum system; at the same time, the quantum state data at multiple moments in the sample time period are recorded using continuous weak measurement to obtain the second sample quantum state data.

[0071] Then, after obtaining the initial quantum state data and final quantum state data of the sample quantum system, projection measurement is performed on the initial quantum state data to obtain first sample quantum state data; and projection measurement is performed on the final quantum state data to obtain a label.

[0072] In this way, a sample data of the sample quantum system and the label corresponding to the sample data are obtained. Repeating this operation N times can obtain N sample data and the label corresponding to each sample data. The value of N is not limited in this application.

[0073] S202: Training the model to be trained based on the sample data and the labels corresponding to the sample data to obtain the quantum system evolution prediction model.

[0074] The model to be predicted in this application is a time series model, such as a recurrent neural network or a long short-term memory network.

[0075] First, for each sample data among a plurality of sample data, the sample data is input into the model to be trained, and the model to be trained outputs the expected predicted value of the observable quantity, that is, outputs the predicted label corresponding to the sample data.

[0076] The first sample quantum state data in the sample data of this application is denoted as { <O i >}, the second sample quantum state data in the sample data is recorded as I(t); the sample data is recorded as ({ <O i >},I(t)); the label corresponding to the sample data is recorded as { <O f >}; let the predicted label be

[0077] Then, for each sample data in the multiple sample data, the predicted label of the sample data is And the sample label of the sample data { <O f >} is substituted into formula (1) to calculate the loss function. The expression of formula (1) is:

[0078]

[0079] Wherein, N in formula (1) represents the total number of sample data; w represents the network weight and bias parameter of the model to be trained; L(w) represents the loss function; the meanings of other letters refer to the introduction of the above embodiment.

[0080] Finally, the parameter gradients in the model to be trained are updated based on the gradient of the loss function until the preset training cutoff condition is met and the training is stopped to obtain a quantum system evolution prediction model.

[0081] Specifically, the gradient of the loss function is calculated through the loss function; based on the gradient of the loss function, the parameter gradient in the prediction model is updated using the gradient descent method. When the preset training cutoff condition is met, the training is stopped to obtain the quantum system evolution prediction model.

[0082] The preset training cutoff condition can be the convergence of the loss function or the reaching of the preset maximum number of iterations. -3 to 10 -4 When , it is determined that the model to be trained has learned the rules of continuous weak measurement data and accurately reflects the spatiotemporal correlation characteristics of the system; at this time, the training is stopped and the quantum system evolution prediction model is obtained.

[0083] Figure 3 A schematic diagram of a loss function that changes with the training process of a quantum system evolution prediction model provided in an embodiment of the present application. Figure 3 The horizontal axis "episodes" represents the number of iterations, the vertical axis "lgloss" represents the logarithm of the loss function, the blue curve (Train) represents the change of the loss value on the training set with the number of iterations; the orange curve (Test) represents the change of the loss value on the test set with the number of iterations.

[0084] Combine Figure 3 As shown in the figure, the loss values ​​on both the training and test sets gradually decrease with increasing iterations, indicating that the system evolution prediction model is learning and gradually improving its prediction accuracy. The similar trends of the two curves indicate that the performance of the system evolution prediction model on both the training and test sets is improving, with no obvious overfitting.

[0085] Figure 4 A schematic diagram of the effect of a quantum system evolution prediction model provided in an embodiment of the present application on predicting a discrete quantum system. Figure 4 The horizontal axis "time" in the figure represents time, from 0 to 5π, indicating the process of the discrete quantum system evolving over time; the vertical axis "expectation" represents the expected value, that is, the average value of an observable quantity in the quantum state; the blue line "realX" represents the change of the true expected value of the variable X over time; the pink line "realY" represents the change of the true expected value of the variable Y over time; the blue solid point "pred.X" represents the expected value of the variable X predicted by the system evolution prediction model; the pink solid point "pred.Y" represents the expected value of the variable Y predicted by the system evolution prediction model.

[0086] Combine Figure 4 As shown, the predictions of variables X and Y by the system evolution prediction model are very close to the true values, indicating that the system evolution prediction model has good performance in predicting quantum dynamics.

[0087] Figure 5 A schematic diagram of the effect of a quantum system evolution prediction model provided in an embodiment of the present application on predicting a continuous quantum system. Figure 5 The horizontal axis "time" in the figure represents time, from 0 to 5π, indicating the process of continuous quantum system evolving over time; the vertical axis "expectation" represents the expected value, that is, the average value of an observable quantity in the quantum state; the purple line "real< / q> " represents the change of the real expected value of the momentum operator p over time; the red line "real <q>" represents the change of the true expected value of the position operator q over time; the purple solid point Represents the expected value of the momentum operator p predicted by the system evolution prediction model; yellow solid point Represents the expected value of the position operator q predicted by the system evolution prediction model.

[0088] Combine Figure 5 As shown, the predictions of the momentum operator p and position operator q by the system evolution prediction model are very close to the true values; this indicates that the system evolution prediction model has good performance in predicting quantum dynamics.

[0089] Based on the same inventive concept, the present application also discloses a quantum state prediction device. Figure 6 A schematic diagram of the structure of a quantum state prediction device provided in an embodiment of the present application. Figure 6 As shown, the quantum state prediction device 600 disclosed in this application includes:

[0090] A reference data acquisition module 601 is configured to determine first quantum state data and second quantum state data of a quantum system to be predicted; the first quantum state data is quantum state data obtained by performing a projection measurement on the initial quantum state data of the quantum system to be predicted; and the second quantum state data is quantum state data that varies with time and is obtained by performing continuous weak measurements on the quantum system to be predicted.

[0091] The target data acquisition module 602 is used to obtain third quantum state data based on the first quantum state data and the second quantum state data through a quantum system evolution predictor; the quantum system evolution predictor is a model obtained by training a time series model and outputting quantum state data corresponding to the evolution of the quantum state.

[0092] In an optional implementation, the benchmark data acquisition module 601 includes:

[0093] a target time period determining unit, configured to determine a target time period; the initial moment of the target time period being the moment when the initial quantum state data in the quantum system to be predicted appears;

[0094] The second quantum state data acquisition unit is used to record the quantum state data of the quantum system to be predicted at multiple time points within the target time period through the continuous weak measurement to obtain the second quantum state data.

[0095] In an optional implementation, the quantum state prediction device 600 further includes:

[0096] a sample data acquisition module, configured to acquire a plurality of sample data; each sample data comprising first sample quantum state data and second sample quantum state data; the first sample quantum state data being quantum state data obtained by performing the projection measurement on the initial quantum state data of the sample quantum system; the second sample quantum state data being quantum state data varying with time within a sample time period, obtained by performing the continuous weak measurement on the sample quantum system; the initial moment of the sample time period being the moment when the initial quantum state data of the sample quantum system appears;

[0097] a label acquisition module, configured to acquire a label for the sample data; the label is quantum state data obtained by performing the projection measurement on the final quantum state data of the sample quantum system; the appearance time of the final quantum state data of the sample quantum system is the end time of the sample time period;

[0098] The model training module is used to train the to-be-trained model based on the sample data and the labels corresponding to the sample data to obtain the quantum system evolution prediction model.

[0099] In an optional implementation, the sample data acquisition module includes:

[0100] a sample time period determining unit, configured to determine the sample time period; the initial moment of the sample time period being the moment when the initial quantum state data in the quantum system to be predicted appears;

[0101] The second sample quantum state data acquisition unit is used to record the quantum state data of the sample quantum system at different moments in the evolution process within the sample time period through the continuous weak measurement to obtain the second sample quantum state data.

[0102] In an optional implementation, the tag acquisition module includes:

[0103] a final quantum state data acquisition unit, configured to use the quantum state data of the sample quantum system at the end of the sample time period as the final quantum state data of the sample quantum system;

[0104] The label acquisition unit is used to perform the projection measurement on the final quantum state data of the sample quantum system to obtain the label.

[0105] In an optional implementation, the model training module includes:

[0106] A prediction label acquisition unit, configured to input the sample data into the model to be trained to obtain a prediction label;

[0107] A parameter updating unit is used to calculate the value of the loss function based on the predicted label and the sample label; and update the parameter gradient in the to-be-trained model based on the gradient of the loss function until the training is stopped after a preset training cutoff condition is met, thereby obtaining the quantum system evolution prediction model.

[0108] Based on the same inventive concept, the present application also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining a predicted value of a quantum state.

[0109] Based on the same inventive concept, the present application also discloses an electronic device, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the steps of a method for determining a predicted value of a quantum state.

[0110] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0111] The above is merely one specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.< / q>

Claims

1. A method for determining a predicted value of a quantum state, characterized in that: The method comprises: Determining first quantum state data and second quantum state data of a quantum system to be predicted; the first quantum state data is quantum state data obtained by performing projection measurement on initial quantum state data of the quantum system to be predicted; and the second quantum state data is quantum state data that changes with time and is obtained by performing continuous weak measurement on the quantum system to be predicted; A quantum system evolution predictor is used to obtain third quantum state data based on the first quantum state data and the second quantum state data; the quantum system evolution predictor is a model obtained by training a time series model and outputting quantum state data corresponding to the evolution of the quantum state.

2. The method according to claim 1, characterized in that The step of acquiring the second quantum state data includes: Determining a target time period; the initial moment of the target time period is the moment when the initial quantum state data in the quantum system to be predicted appears; The second quantum state data is obtained by recording the quantum state data of the quantum system to be predicted at multiple time points within the target time period through the continuous weak measurement.

3. The method according to claim 1, characterized in that The training steps of the quantum system evolution predictor include: Acquire a plurality of sample data; each of the sample data includes first sample quantum state data and second sample quantum state data; the first sample quantum state data is quantum state data obtained by performing the projection measurement on the initial quantum state data of the sample quantum system; the second sample quantum state data is quantum state data obtained by performing the continuous weak measurement on the sample quantum system and varying with time within a sample time period; the initial moment of the sample time period is the moment when the initial quantum state data of the sample quantum system appears; Obtaining a label for the sample data; the label is quantum state data obtained by performing the projection measurement on the final quantum state data of the sample quantum system; the appearance time of the final quantum state data of the sample quantum system is the end time of the sample time period; The model to be trained is trained based on the sample data and the labels corresponding to the sample data to obtain the quantum system evolution prediction model.

4. The method according to claim 3, characterized in that The second sample quantum state data acquisition step includes: Determining the sample time period; the initial moment of the sample time period is the moment when the initial quantum state data of the quantum system to be predicted appears; Through the continuous weak measurement, the quantum state data of the sample quantum system at different moments in the evolution process within the sample time period are recorded to obtain the second sample quantum state data.

5. The method according to claim 4, characterized in that Obtaining labels for the sample data includes: Taking the quantum state data of the sample quantum system at the end of the sample time period as the final quantum state data of the sample quantum system; The projection measurement is performed on the final quantum state data of the sample quantum system to obtain the label.

6. The method according to claim 3, characterized in that The step of training the model to be trained based on the sample data and the labels corresponding to the sample data to obtain the quantum system evolution prediction model includes: Inputting the sample data into the model to be trained to obtain a predicted label; The value of the loss function is calculated based on the predicted label and the sample label; and the parameter gradient in the to-be-trained model is updated based on the gradient of the loss function, and the training is stopped after a preset training cutoff condition is met, thereby obtaining the quantum system evolution prediction model.

7. The method according to any one of claims 3 to 6, characterized in that The model to be predicted is a recurrent neural network or a long short-term memory network.

8. A device for determining a predicted value of a quantum state, characterized in that: The device comprises: A reference data acquisition module is used to determine first quantum state data and second quantum state data of the quantum system to be predicted; the first quantum state data is quantum state data obtained by projecting the initial quantum state data of the quantum system to be predicted; the second quantum state data is quantum state data that varies with time and obtained by continuous weak measurement of the quantum system to be predicted; A target data acquisition module is used to obtain third quantum state data based on the first quantum state data and the second quantum state data through a quantum system evolution predictor; the quantum system evolution predictor is a model obtained by training a time series model and outputting quantum state data corresponding to the evolution of the quantum state.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

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