A method and related apparatus for determining quantum state prediction values
By combining continuous weak measurements and time-series models, the problem of traditional projective measurements disrupting the evolution path of quantum systems is solved, enabling high-precision quantum state data prediction in complex environments.
Patent Information
- Application Number
- CN202510779628.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-11
- Publication Date
- 2026-03-06
- Estimated Expiration
- 2045-06-11
AI Technical Summary
In traditional quantum information technology, predicting the evolution of a quantum system based on projection measurement methods can disrupt the original evolution path of the quantum system and affect the accuracy of quantum state data.
The second quantum state data of a quantum system is obtained by continuous weak measurement, and the spatiotemporal correlation characteristics in the quantum state data are learned by using a trained temporal model such as a recurrent neural network or a long short-term memory network. The quantum system evolution predictor is then used to make predictions.
It improves the prediction accuracy of quantum state data in complex environments, ensures that the quantum system evolves along the original evolution path, and enhances the accuracy of prediction.
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Figure CN120671856B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of quantum information technology, and in particular to a method and related apparatus for determining quantum state prediction values. Background Technology
[0002] In the field of quantum information, quantum computing, quantum sensing, quantum precision measurement, and quantum communication all rely on quantum state data during the quantum dynamics evolution process. Traditional methods typically use projection measurement to predict the evolution of quantum systems and obtain related quantum state data. However, such measurements disrupt the original evolutionary path of the quantum system, thus affecting the accuracy of the obtained quantum state data. Therefore, how to accurately predict the quantum state data corresponding to the evolution of a quantum system has become one of the key technical problems that urgently need to be solved in the field of quantum information technology. Summary of the Invention
[0003] To address the aforementioned issues, this application provides a method for determining quantum state prediction values, which can 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] The first aspect of this application discloses a method for determining quantum state prediction values, including:
[0006] Determine the first quantum state data and the second quantum state data of the quantum system to be predicted; the first quantum state data is the quantum state data obtained by projecting measurements onto the initial quantum state data of the quantum system to be predicted; the second quantum state data is the time-varying quantum state data obtained by performing continuous weak measurements on the quantum system to be predicted.
[0007] The quantum system evolution predictor obtains the 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 that is trained on a time-series model and outputs the quantum state data corresponding to the quantum state evolution.
[0008] In one optional implementation, the step of acquiring the second quantum state data includes:
[0009] Determine the target time period; the initial time of the target time period is the time when the initial quantum state data of the quantum system to be predicted appears;
[0010] By using the continuous weak measurement, the quantum state data of the quantum system to be predicted at multiple time points within the target time period are recorded to obtain the second quantum state data.
[0011] In one alternative implementation, the training steps of the quantum system evolution predictor include:
[0012] Multiple sample data are acquired; each sample data includes a first sample quantum state data and a second sample quantum state data; the first sample quantum state data is quantum state data obtained by projecting the initial quantum state data of the sample quantum system; the second sample quantum state data is quantum state data that changes with time within a sample time period, obtained by performing the continuous weak measurement on the sample quantum system; the initial time of the sample time period is the time when the initial quantum state data of the sample quantum system appears;
[0013] Acquire a label for the sample data; the label is quantum state data obtained by projecting the final state quantum state data of the sample quantum system; the occurrence time of the final state quantum state data of the sample quantum system is the end time of the sample time period;
[0014] Based on the sample data and the labels corresponding to the sample data, the model to be trained is trained to obtain the quantum system evolution prediction model.
[0015] In one optional implementation, the step of acquiring the second sample quantum state data includes:
[0016] The sample time period is determined; the initial time of the sample time period is the time when the initial quantum state data of the quantum system to be predicted appears;
[0017] By using 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 one optional implementation, obtaining the labels of the sample data includes:
[0019] The quantum state data of the sample quantum system at the end of the sample time period is taken as the final quantum state data of the sample quantum system.
[0020] The label is obtained by performing the projection measurement on the final state quantum state data of the sample quantum system.
[0021] In one optional implementation, 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:
[0022] The sample data is input into the model to be trained to obtain the predicted labels;
[0023] The loss function is calculated based on the predicted label and the sample label; and the gradient of the parameters in the model to be trained is updated based on the gradient of the loss function until the preset training cutoff condition is met, and then training is stopped to obtain the quantum system evolution prediction model.
[0024] In one alternative implementation, the model to be predicted is a recurrent neural network or a long short-term memory network.
[0025] A second aspect of this application discloses an apparatus for determining a quantum state prediction value, the apparatus comprising:
[0026] A reference data acquisition module is used to determine the first quantum state data and the second quantum state data of the quantum system to be predicted; the first quantum state data is quantum state data obtained by projecting measurements on the initial quantum state data of the quantum system to be predicted; the second quantum state data is quantum state data that changes over time obtained by performing continuous weak measurements on the quantum system to be predicted.
[0027] The target data acquisition module is used to obtain the 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 the quantum state data corresponding to the quantum state evolution.
[0028] A third aspect of this application discloses a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any of the first aspects.
[0029] The fourth aspect of this application discloses an electronic device, comprising:
[0030] A memory on which computer programs are stored;
[0031] A processor for executing the computer program in the memory to implement the steps of the method of any one of the first aspects.
[0032] Compared with the prior art, this application has the following beneficial effects:
[0033] This application discloses a method for determining quantum state prediction values, comprising: after determining the first quantum state data and the second quantum state data of the quantum system to be predicted, inputting the two types of data into a quantum system evolution predictor to obtain the third quantum state data. This application captures the time-varying second quantum state data through continuous weak measurements, solving the problem in traditional schemes where projective measurements disrupt the original evolution path of the quantum system, ensuring that the quantum system evolves according to the original evolution path; simultaneously, after capturing the second quantum state data closely related to spatiotemporal correlation noise through continuous weak measurements, a pre-trained time series model, i.e., the quantum state evolution predictor, learns the spatiotemporal correlation characteristics implicit in the second quantum state data, improving prediction accuracy under complex environments and enabling more accurate prediction of quantum state data during the evolution of the quantum system. Attached Figure Description
[0034] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0035] Figure 1 A flowchart illustrating a method for determining a quantum state prediction value, provided as an embodiment of this application;
[0036] Figure 2 A flowchart illustrating a training method for a quantum system evolution predictor provided in this application embodiment;
[0037] Figure 3 A schematic diagram illustrating how a loss function changes during the training process of a quantum system evolution prediction model, as provided in an embodiment of this application.
[0038] Figure 4 This is a schematic diagram illustrating the effect of a quantum system evolution prediction model provided in this application on predicting discrete quantum systems;
[0039] Figure 5 A schematic diagram illustrating the effect of a quantum system evolution prediction model provided in this application on predicting continuous quantum systems;
[0040] Figure 6 This is a schematic diagram of the structure of a quantum state prediction device provided in an embodiment of this application. Detailed Implementation
[0041] In the field of quantum information, quantum computing, quantum sensing, quantum precision measurement, and quantum communication all rely on quantum state data during the quantum dynamics evolution process. Traditional methods typically use projection measurement to predict the evolution of quantum systems and obtain related quantum state data. However, such measurements disrupt the original evolutionary path of the quantum system, thus affecting the accuracy of the obtained quantum state data. Therefore, how to accurately predict the quantum state data corresponding to the evolution of a quantum system has become one of the key technical problems that urgently need to be solved in the field of quantum information technology.
[0042] This application discloses a method for determining quantum state prediction values, comprising: after determining the first quantum state data and the second quantum state data of the quantum system to be predicted, inputting the two types of data into a quantum system evolution predictor to obtain the third quantum state data. This application captures the time-varying second quantum state data through continuous weak measurements, solving the problem in traditional schemes where projective measurements disrupt the original evolution path of the quantum system, ensuring that the quantum system evolves according to the original evolution path; simultaneously, after capturing the second quantum state data closely related to spatiotemporal correlation noise through continuous weak measurements, a pre-trained time series model, i.e., the quantum state evolution predictor, learns the spatiotemporal correlation characteristics implicit in the second quantum state data, improving prediction accuracy under complex environments and enabling more accurate prediction of quantum state data during the evolution of the quantum system.
[0043] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.
[0044] Figure 1 A flowchart illustrating a method for determining a quantum state prediction value, provided as an embodiment of this application.
[0045] Combination Figure 1 As shown, the method for determining quantum state prediction values disclosed in this application includes:
[0046] S101, determine the first quantum state data and the second quantum state data of the quantum system to be predicted.
[0047] The quantum system to be predicted in this application refers to a quantum system in which the state of quantum state data 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 with an environment. Since the environment is unknown and inaccessible, the quantum system undergoes a spatiotemporally correlated non-Markov noise evolution, which can be approximated as Markov noise if the coupling coefficient or time scale is small enough. The method for determining the predicted quantum state disclosed in this application is particularly suitable for physical models coupled with an environment, because the large amount of noise in such models exacerbates the difficulty of accurately characterizing the evolution equations of the model.
[0049] Of course, the quantum system to be predicted in this application can also be a physical system independent of the environment, i.e., a closed physical system.
[0050] Understandably, after determining the quantum system to be predicted, it is necessary to initialize the state of the quantum system to be predicted; 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 resides.
[0051] It should be noted that the initial state and evolution of the quantum system to be predicted determine the complexity of the features to be extracted and the expected value of the observable.
[0052] The method for predicting quantum states disclosed in this application does not limit 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 features 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 features of the quantum system.
[0053] Taking the 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 will always be in the product state space during its evolution. When obtaining the expected values of the three observables, such as... The evolutionary behavior of the quantum system to be predicted can be completely predicted; if the Hamiltonian of the quantum system to be predicted contains two-body or many-body correlated Pauli operators, then the quantum system to be predicted is in a spin-squeezed state space during the evolution process, and the expected values of the nine observables can be obtained, such as... The evolutionary behavior of the quantum system to be predicted can be completely 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, then the system to be predicted will still be in the maximally entangled state space during the evolution process, and nine observable expectations are needed to fully characterize the system at this time. If the Hamiltonian of the quantum system to be predicted contains two-body or many-body correlated Pauli operators, then the system to be predicted will have a disentanglement process during the evolution process, and the characterization project needs to be combined with the degree of entanglement for specific implementation.
[0054] It should be noted that, and These represent the x, y, and z components of the Pauli operator at the i-th qubit, respectively. and These represent the sum of the average values of the Pauli operators in the x, y, and z directions across all qubits.
[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 squeezing operator. If the quantum system to be predicted does not contain a squeezing operator, then the quantum system to be predicted will only evolve in the coherent state space, therefore the first moments of the orthogonal components p and q are required. ， <q> As a characteristic; if the Hamiltonian of the quantum system to be predicted contains a squeezing operator, then the quantum system to be predicted evolves in a larger Gaussian state, in which case the first moments of p and q are required {< / q> ， <q>} and 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>These represent the expected values of the momentum and position operators, respectively; <p 2 >and 2 > These represent the expected values of the squares of the momentum and position operators, respectively;<pq+qp> This represents the expected value of the product of momentum and position operators.
[0057] The first quantum state data in this application is quantum state data obtained by projecting measurements onto the initial quantum state data of the quantum system to be predicted; the second quantum state data is time-varying quantum state data obtained by performing continuous weak measurements on the quantum system to be predicted.
[0058] For example, after determining the quantum system to be predicted, it is necessary to apply a random quantum gate or control pulse to the quantum system to be predicted to make it evolve. A random state sampled during the evolution process is taken as a random initial state and recorded as the initial quantum state data of the quantum system to be predicted. Then, the initial quantum state data is projected and measured 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; using the first moment as the initial moment, a time period is determined as the target time period; then, continuous weak measurements are used to record the quantum state data of the quantum system to be predicted at multiple moment 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 lies in its small impact on the quantum system and the ability to continuously monitor the system over a long period of time, rather than projecting the system to a specific state all at once.
[0061] In traditional projective measurements, the process causes wavefunction collapse, meaning the state of the quantum system abruptly jumps to one of the eigenstates of the measured operator. This method of measurement has a drastic impact on the system, and the measurement results are discrete. In contrast, continuous weak measurements are a "mild" method that has a smaller impact on the quantum system and does not cause drastic state collapse.
[0062] S102, the third quantum state data is obtained by the quantum system evolution predictor based on the first quantum state data and the second quantum state data.
[0063] The quantum system evolution predictor in this application is a model obtained by training a time-series model and outputting the quantum state data corresponding to the evolution of quantum states. The training data acquisition process of the quantum system evolution predictor and the model training process will be described in detail in subsequent embodiments of this application.
[0064] After obtaining the first quantum state data and the second quantum state data of the quantum system to be predicted, the two types of 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 at the end of the target time period.
[0065] In summary, this application discloses a method for determining quantum state prediction values, 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 types of data into a quantum system evolution predictor to obtain the third quantum state data. This application captures the time-varying second quantum state data through continuous weak measurements, solving the problem in traditional schemes where projective measurements disrupt the original evolution path of the quantum system, ensuring that the quantum system evolves according to the original evolution path; simultaneously, after capturing the second quantum state data closely related to spatiotemporal correlation noise through continuous weak measurements, a pre-trained time series model, i.e., the quantum state evolution predictor, learns the spatiotemporal correlation characteristics implicit in the second quantum state data, improving the prediction accuracy under complex environments and enabling more accurate prediction of quantum state data during the evolution of the quantum system.
[0066] Figure 2 A flowchart illustrating a training method for a quantum system evolution predictor provided in this application embodiment. (Combined with...) Figure 2 As shown, the training method for a quantum system evolution predictor disclosed in this application includes:
[0067] S201, obtain multiple sample data and the label 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 obtained by projecting the initial quantum state data of the sample quantum system; the second sample quantum state data is obtained by performing continuous weak measurements on the sample quantum system, showing the quantum state data changing over time within a sample time period; and the label corresponding to each sample data is 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 cause it to evolve, and a random state sampled during the evolution process is taken as the random initial state σ. i The initial quantum state data of the sample quantum system is recorded as follows: the sample quantum system is then allowed to undergo free 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 within the continuous weak measurement sample time period are recorded to obtain the second sample quantum state data.
[0071] Next, after obtaining the initial quantum state data and the final quantum state data of the sample quantum system, the initial quantum state data is projected to obtain the first sample quantum state data; the final quantum state data is projected to obtain the tag.
[0072] This yields a sample data point of the sample quantum system and its corresponding tag. Repeating this operation N times will produce N sample data points and their corresponding tags. The value of N is not limited in this application.
[0073] S202, Based on the sample data and the labels corresponding to the sample data, the model to be trained is trained 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 in the multiple sample data, the sample data is input into the model to be trained. The model to be trained outputs the expected predicted value of the observable, that is, the predicted label corresponding to the sample data.
[0076] In this application, the first sample quantum state data in the sample data is denoted as { <O i >}, let I(t) be the second sample quantum state data in the sample data; let ({ <O i >},I(t)); denote the labels corresponding to the sample data as { <O f >};Denote the predicted label as
[0077] Then, for each of the multiple sample data, predict the label of that sample data. and the sample labels of the sample data { <O f Substituting these values into formula (1), the loss function is calculated. The expression for formula (1) is:
[0078]
[0079] In formula (1), N represents the total number of sample data; w represents the network weights and bias parameters of the model to be trained; L(w) represents the loss function; the meanings of other letters are described in the previous embodiments.
[0080] Finally, the gradient of the loss function is used to update the gradient of the parameters in the model to be trained until the preset training cutoff condition is met, and then training stops to obtain the quantum system evolution prediction model.
[0081] Specifically, the gradient of the loss function is calculated; based on the gradient of the loss function, the gradient of the parameters in the model to be predicted is updated using the gradient descent method; when the preset training cutoff condition is met, training is stopped to obtain the quantum system evolution prediction model.
[0082] The preset training cutoff condition can be either the convergence of the loss function or reaching a preset maximum number of iterations. In this application, the convergence of the loss function is determined to be within 10... -3 Up to 10 -4 During this period, it is determined that the model to be trained has learned the patterns of continuous weak measurement data and accurately reflects the spatiotemporal correlation characteristics of the system; at this point, training is stopped, and the quantum system evolution prediction model is obtained.
[0083] Figure 3 This is a schematic diagram illustrating how a loss function changes during the training process of a quantum system evolution prediction model, as provided in an embodiment of this application. Figure 3 In the graph, the horizontal axis "episodes" represents the number of iterations, and the vertical axis "lgloss" represents the logarithmic value 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] Combination Figure 3 As shown, 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 suggest that the performance of the system evolution prediction model is improving on both the training and test sets, with no obvious overfitting.
[0085] Figure 4 This is a schematic diagram illustrating the effect of a quantum system evolution prediction model provided in this application on predicting discrete quantum systems. Figure 4 The horizontal axis "time" represents time, from 0 to 5π, indicating the evolution of the discrete quantum system over time; the vertical axis "expectation" represents the expected value, that is, the average value of a certain observable quantity in the quantum state; the blue line "realX" represents the change of the actual expected value of variable X over time; the pink line "realY" represents the change of the actual expected value of variable Y over time; the blue solid dot "pred.X" represents the expected value of variable X predicted by the system evolution prediction model; the pink solid dot "pred.Y" represents the expected value of variable Y predicted by the system evolution prediction model.
[0086] Combination Figure 4 As shown, the predictions of the system evolution prediction model for variables X and Y are very close to the actual values; this indicates that the system evolution prediction model has good performance in predicting quantum dynamics.
[0087] Figure 5 This is a schematic diagram illustrating the effect of a quantum system evolution prediction model provided in this application on predicting continuous quantum systems. Figure 5 The horizontal axis "time" represents time, from 0 to 5π, indicating the evolution of the continuous quantum system over time; the vertical axis "expectation" represents the expected value, that is, the average value of a certain observable quantity in the quantum state; the purple line "real< / q> "Represents the change in the true expectation value of the momentum operator p over time; the red line "real" indicates the change in the true expectation value of the momentum operator p over time. <q>"" indicates the change in the true expected value of the positional operator q over time; purple dots Yellow dots represent the expected value of the momentum operator p predicted by the system evolution prediction model. This represents the expected value of the position operator q predicted by the system evolution prediction model.
[0088] Combination Figure 5 As shown, the predictions of the system evolution prediction model for the momentum operator p and the position operator q are very close to the actual values; this indicates that the system evolution prediction model has good performance in predicting quantum dynamics.
[0089] Based on the same inventive concept, this application also discloses a quantum state prediction device. Figure 6 This is a schematic diagram of a quantum state prediction device provided in an embodiment of this application. (Combined with...) Figure 6 As shown, the quantum state prediction device 600 disclosed in this application includes:
[0090] The reference data acquisition module 601 is used to determine the first quantum state data and the second quantum state data of the quantum system to be predicted; the first quantum state data is quantum state data obtained by projecting measurements on the initial quantum state data of the quantum system to be predicted; the second quantum state data is quantum state data that changes over time obtained by performing continuous weak measurements on the quantum system to be predicted.
[0091] The target data acquisition module 602 is used to obtain the 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 the quantum state data corresponding to the quantum state evolution.
[0092] In one alternative implementation, the benchmark data acquisition module 601 includes:
[0093] A target time period determination unit is used to determine a target time period; the initial time of the target time period is the time when the initial quantum state data appears in the quantum system to be predicted;
[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, so as to obtain the second quantum state data.
[0095] In one alternative implementation, the quantum state prediction device 600 further includes:
[0096] A sample data acquisition module is used to acquire multiple sample data; each 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 projecting the initial quantum state data of the sample quantum system; the second sample quantum state data is quantum state data that changes with time within a sample time period, obtained by performing continuous weak measurements on the sample quantum system; the initial time of the sample time period is the time when the initial quantum state data of the sample quantum system appears;
[0097] A tag acquisition module is used to acquire tags for the sample data; the tags are quantum state data obtained by projecting the final state quantum state data of the sample quantum system; the occurrence time of the final state 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 model to be trained based on the sample data and the labels corresponding to the sample data, so as to obtain the quantum system evolution prediction model.
[0099] In one alternative implementation, the sample data acquisition module includes:
[0100] A sample time period determination unit is used to determine the sample time period; the initial time of the sample time period is the time when the initial quantum state data of 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 times during the evolution process of the sample quantum system within the sample time period through the continuous weak measurement, so as to obtain the second sample quantum state data.
[0102] In one alternative implementation, the tag acquisition module includes:
[0103] The final state quantum state data acquisition unit is used to take the quantum state data of the sample quantum system at the end of the sample time period as the final state quantum state data of the sample quantum system.
[0104] The tag acquisition unit is used to perform the projection measurement on the final state quantum state data of the sample quantum system to obtain the tag.
[0105] In one alternative implementation, the model training module includes:
[0106] The prediction label acquisition unit is used to input the sample data into the model to be trained to obtain the prediction label;
[0107] The parameter update 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 model to be trained based on the gradient of the loss function until the preset training cutoff condition is met, and then stop training to obtain the quantum system evolution prediction model.
[0108] Based on the same inventive concept, this application also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for determining a quantum state prediction value.
[0109] Based on the same inventive concept, this application also discloses an electronic device, comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the steps of a method for determining a quantum state prediction value.
[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 mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate. The components indicated as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment solution according to actual needs. Those skilled in the art can understand and implement this without creative effort.
[0111] The above description is merely one specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.< / q>
Claims
1. A method of determining a quantum state prediction value, characterized by, 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 a projection measurement on initial quantum state data of the quantum system to be predicted; the second quantum state data is quantum state data varying over time obtained by performing a continuous weak measurement on the quantum system to be predicted; obtaining third quantum state data based on the first quantum state data and the second quantum state data by using a quantum system evolution predictor; the quantum system evolution predictor is a model trained on a time series model and outputting quantum state data corresponding to an evolved quantum state; the training step of the quantum system evolution predictor comprises: obtaining a plurality of sample data; each sample data comprises 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 initial quantum state data of a sample quantum system; the second sample quantum state data is quantum state data varying over time within a sample time period obtained by performing the continuous weak measurement on the sample quantum system; the initial time of the sample time period is the time when the initial quantum state data of the sample quantum system appears; obtaining a label of the sample data; the label is quantum state data obtained by performing the projection measurement on final quantum state data of the sample quantum system; the time when the final quantum state data of the sample quantum system appears is the termination time of the sample time period; training a model to be trained based on the sample data and the label corresponding to the sample data to obtain the quantum system evolution prediction model.
2. The method of claim 1, wherein, the obtaining step of the second quantum state data comprises: determining a target time period; the initial time of the target time period is the time when the initial quantum state data in the quantum system to be predicted appears; recording quantum state data of a plurality of time points within the target time period of the quantum system to be predicted by using the continuous weak measurement to obtain the second quantum state data.
3. The method of claim 1, wherein, the obtaining step of the second sample quantum state data comprises: determining the sample time period; the initial time of the sample time period is the time when the initial quantum state data in the quantum system to be predicted appears; recording quantum state data of different times in the evolution process of the sample quantum system within the sample time period by using the continuous weak measurement to obtain the second sample quantum state data.
4. The method of claim 3, wherein, the obtaining step of the label of the sample data comprises: taking quantum state data appearing at the termination time of the sample time period of the sample quantum system as final quantum state data of the sample quantum system; performing the projection measurement on the final quantum state data of the sample quantum system to obtain the label.
5. The method of claim 1, wherein, the training of the model to be trained based on the sample data and the label corresponding to the sample data to obtain the quantum system evolution prediction model comprises: inputting the sample data into the model to be trained to obtain a predicted label; Calculate a value of a loss function based on the predicted label and a label of the sample data; and update a parameter gradient in the to-be-trained model based on a gradient of the loss function until a preset training stop condition is met to stop training, and obtain the quantum system evolution prediction model.
6. The method according to any one of claims 1-5, characterized in that, The to-be-trained model is a recurrent neural network or a long short-term memory network.
7. An apparatus for determining the predicted value of a quantum state, characterized in that, The device comprises: A reference data acquisition module configured to determine first quantum state data and second quantum state data of a to-be-predicted quantum system; the first quantum state data is quantum state data obtained by performing a projection measurement on initial quantum state data of the to-be-predicted quantum system; and the second quantum state data is quantum state data varying over time obtained by performing a continuous weak measurement on the to-be-predicted quantum system; A target data acquisition module configured to obtain third quantum state data based on the first quantum state data and the second quantum state data by using a quantum system evolution predictor; the quantum system evolution predictor is a model trained on a time series model and outputting quantum state data corresponding to an evolved quantum state; A sample data acquisition module configured to acquire a plurality of sample data; each of the sample data comprises 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 initial quantum state data of a sample quantum system; and the second sample quantum state data is quantum state data varying over time within a sample time period obtained by performing the continuous weak measurement on the sample quantum system; an initial time of the sample time period is a time at which the initial quantum state data of the sample quantum system appears; A label acquisition module configured to acquire a label of the sample data; the label is quantum state data obtained by performing the projection measurement on final quantum state data of the sample quantum system; and a time at which the final quantum state data of the sample quantum system appears is a termination time of the sample time period; A model training module configured to train a to-be-trained model based on the sample data and the label corresponding to the sample data, and obtain the quantum system evolution prediction model.
8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method of any one of claims 1-6.
9. An electronic device, comprising: Comprise: 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 of any one of claims 1-6.