Information acquisition method and device of quantum system, computer equipment, readable storage medium and program product

Abstract

The invention relates to an information acquisition method and device of a quantum system, computer equipment, a readable storage medium and a program product. The method comprises the following steps: converting a preset initial quantum state of a target quantum system to obtain an initial matrix product state corresponding to the initial quantum state; optimizing the initial matrix product state by taking minimization of the quantum energy value as a target to obtain a target matrix product state; obtaining an initial circuit parameter of the quantum circuit according to the target matrix product state; optimizing the initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit; and determining a ground state energy value of the target quantum system according to the target circuit parameter. By adopting the method, the ground state energy acquisition efficiency of the noisy quantum system can be improved.

Description

Technical Field

[0001] The present application relates to the field of quantum computing technology, and in particular to a method, apparatus, computer device, computer-readable storage medium, and computer program product for acquiring information from a quantum system. Background Art

[0002] Quantum computing has important applications in quantum chemistry, materials science, and the simulation of complex physical systems, where determining the ground-state energy is a key issue. Current mainstream quantum computing solutions rely primarily on hybrid quantum-classical optimization methods, such as the Variational Quantum Eigensolver (VQE). These methods construct target quantum states through parameterized quantum circuits and optimize their parameters to minimize system energy.

[0003] However, when dealing with highly entangled quantum states, existing methods typically construct trial states based on heuristic parameterized quantum circuits with randomly generated initial parameters. Furthermore, to accurately characterize the ground state of strongly correlated systems, complex quantum circuits with considerable depth are often required. However, due to the hardware limitations of current medium-noise quantum devices and unavoidable noise interference, the reliable execution of quantum circuits is significantly constrained. This causes actual energy measurement errors to accumulate and amplify with increasing circuit complexity, resulting in low efficiency in extracting the ground state energy from quantum systems. Summary of the Invention

[0004] Based on this, it is necessary to provide a quantum system information acquisition method, device, computer equipment, computer-readable storage medium and computer program product that can improve the ground state energy acquisition efficiency of noisy quantum systems in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for acquiring information from a quantum system, comprising:

[0006] Converting a preset initial quantum state of the target quantum system to obtain an initial matrix product state corresponding to the initial quantum state;

[0007] Optimizing the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state;

[0008] Obtaining initial circuit parameters of the quantum circuit according to the target matrix product state;

[0009] Optimizing initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit;

[0010] The ground state energy value of the target quantum system is determined according to the target circuit parameters.

[0011] In one embodiment, optimizing the initial matrix product state with the goal of minimizing the quantum energy value to obtain the target matrix product state includes:

[0012] Using the initial matrix product state as the current matrix product state;

[0013] Calculating a quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system;

[0014] Normalizing the current matrix product state into a central orthogonal form to obtain a normalized current matrix product state;

[0015] Based on the normalized current matrix product state, a stochastic gradient descent optimization model is used to adjust the current matrix product state to obtain a new current matrix product state, and the step of calculating the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system is returned to execute until the quantum energy value corresponding to the current matrix product state no longer decreases. The current matrix product state obtained for the last time is used as the target matrix product state.

[0016] In one embodiment, obtaining initial circuit parameters of a quantum circuit according to the target matrix product state includes:

[0017] Initializing an initial mapping matrix product state that conforms to the circuit structure of the target quantum system;

[0018] Iteratively adjusting the initial mapping matrix product state until the similarity between the adjusted initial mapping matrix product state and the target matrix product state is maximized, and then using the adjusted initial mapping matrix product state as the target mapping matrix product state;

[0019] According to the target mapping matrix product state, initial circuit parameters of the quantum circuit are obtained by mapping.

[0020] In one embodiment, optimizing the initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit includes:

[0021] Using the initial circuit parameters as current circuit parameters;

[0022] determining a quantum energy value of the quantum circuit according to the current circuit parameters;

[0023] Based on the stochastic gradient descent optimization model, the current circuit parameters are updated to obtain new current circuit parameters, and the step of determining the quantum energy value of the quantum circuit based on the current circuit parameters is returned to execution until the quantum energy value of the quantum circuit no longer decreases. The current circuit parameters obtained for the last time are then used as the target circuit parameters of the quantum circuit.

[0024] In one embodiment, determining the ground state energy value of the target quantum system according to the target circuit parameters includes:

[0025] By performing circuit folding and circuit stretching operations on a target quantum circuit, a plurality of equivalent quantum circuits under different noise scaling parameters are obtained; the target quantum circuit is a quantum circuit whose corresponding circuit parameters are the target circuit parameters;

[0026] Determine the quantum energy value of each equivalent quantum circuit;

[0027] According to the quantum energy values ​​of the respective equivalent quantum circuits, the noise-free quantum energy value of the target quantum circuit is obtained as the noise-free ground state energy value of the target quantum system.

[0028] In one embodiment, obtaining the noise-free quantum energy value of the target quantum circuit according to the quantum energy values ​​of each equivalent quantum circuit includes:

[0029] Obtaining a mapping relationship between the noise scaling parameter and the quantum energy value of the target quantum circuit according to the quantum energy values ​​of the respective equivalent quantum circuits and the noise scaling parameters of the respective equivalent quantum circuits;

[0030] According to the mapping relationship, the noise-free quantum energy value of the target quantum circuit is obtained.

[0031] In a second aspect, the present application further provides an information acquisition device for a quantum system, comprising:

[0032] A quantum state conversion module is used to convert a preset initial quantum state of the target quantum system to obtain an initial matrix product state corresponding to the initial quantum state;

[0033] A product state optimization module is used to optimize the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state;

[0034] An initial parameter determination module, configured to obtain initial circuit parameters of the quantum circuit according to the target matrix product state;

[0035] a target parameter determination module, configured to optimize the initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit;

[0036] The energy value determination module is used to determine the ground state energy value of the target quantum system according to the target circuit parameters.

[0037] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Converting a preset initial quantum state of the target quantum system to obtain an initial matrix product state corresponding to the initial quantum state;

[0039] Optimizing the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state;

[0040] Obtaining initial circuit parameters of the quantum circuit according to the target matrix product state;

[0041] Optimizing initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit;

[0042] The ground state energy value of the target quantum system is determined according to the target circuit parameters.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0044] Converting a preset initial quantum state of the target quantum system to obtain an initial matrix product state corresponding to the initial quantum state;

[0045] Optimizing the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state;

[0046] Obtaining initial circuit parameters of the quantum circuit according to the target matrix product state;

[0047] Optimizing initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit;

[0048] The ground state energy value of the target quantum system is determined according to the target circuit parameters.

[0049] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0050] Converting a preset initial quantum state of the target quantum system to obtain an initial matrix product state corresponding to the initial quantum state;

[0051] Optimizing the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state;

[0052] Obtaining initial circuit parameters of the quantum circuit according to the target matrix product state;

[0053] Optimizing initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit;

[0054] The ground state energy value of the target quantum system is determined according to the target circuit parameters.

[0055] The above-mentioned quantum system information acquisition method, device, computer equipment, computer-readable storage medium and computer program product, first, convert the preset initial quantum state of the target quantum system to obtain the initial matrix product state corresponding to the initial quantum state. By converting the initial quantum state of the target quantum system through the matrix product state, the storage complexity of the quantum state can be reduced, so that it can efficiently represent high-dimensional quantum states. The matrix product state structure can effectively describe the quantum system, reduce computational redundancy, and provide a mathematically easier-to-handle representation for subsequent optimization; then, with the goal of minimizing the quantum energy value, the initial matrix product state is optimized to obtain the target matrix product state. By optimizing the initial matrix product state to minimize its energy, it can be ensured that the obtained target matrix product state is as close as possible to the ground state of the target quantum system, avoiding directly solving high-dimensional optimization problems on quantum hardware, making the calculation more stable, and being able to make full use of classical computing resources for pre-optimization, avoiding falling into local extreme values, improving convergence stability, and reducing the number of optimization iterations required for subsequent quantum calculations; then, according to the target matrix product state, the initial circuit parameters of the quantum circuit are obtained, and the initial parameters of the quantum circuit are generated by the optimized matrix product state, so that The constructed quantum circuit can be as close as possible to the ground state of the target system, which can effectively reduce the search space during quantum circuit optimization, make the optimization process more physically interpretable, avoid local extreme value problems that may be caused by random initialization, and improve the optimization convergence speed; further, the initial circuit parameters of the quantum circuit are optimized to obtain the target circuit parameters of the quantum circuit. Through an optimization process based on energy minimization, the parameters of the quantum circuit are further adjusted to make it closer to the target ground state. On the quantum hardware, the parameters are dynamically corrected through quantum measurement feedback, and the initial parameters are fine-tuned and optimized to compensate for the modeling error between classical pre-training and the real quantum system. While maintaining a low circuit depth, the real ground state energy is further approached, the error accumulation caused by noise is suppressed, and the experimental feasibility of the final result is improved; finally, according to the target circuit parameters, the ground state energy value of the target quantum system is determined. By measuring the output state of the optimized quantum circuit and calculating the expectation value of the Hamiltonian, the ground state energy value of the target quantum system is finally obtained, ensuring that the calculated result of the ground state energy matches the ground state of the actual physical system and can be applied to different quantum systems, providing a reliable calculation solution for high-precision calculations in fields such as quantum chemistry and material simulation. In the above method, through the collaboration of matrix product state pre-training and quantum parameter optimization, highly entangled state information is compressed and near-optimal initial parameters are generated in the classical computing stage, which greatly reduces the depth of the quantum circuit, breaks through the execution limitations of noisy quantum hardware, and improves the accuracy and computational efficiency of solving the ground state energy of the target quantum system; at the same time, the structural adaptability of the pre-trained parameters and the quantum circuit enables the efficient convergence of the classical-quantum optimization process, and achieves high-precision solution of the ground state energy of the quantum system under limited resources.Compared to traditional variational quantum algorithms, this approach avoids blind optimization directly on quantum circuits and instead utilizes matrix product state optimization to provide better initial conditions, thereby reducing the optimization overhead of quantum circuits. Furthermore, this approach is adaptable to quantum systems of varying sizes and can achieve efficient computation on current medium-noise quantum computers, providing a more stable solution for subsequent quantum computing applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 1 is a flow chart of a method for acquiring information from a quantum system according to an embodiment;

[0058] Figure 2 1 is a flow chart of steps for obtaining the ground state energy value of a target quantum system in one embodiment;

[0059] Figure 3 1 is a flow chart of a method for acquiring information from a quantum system according to another embodiment;

[0060] Figure 4 is a schematic diagram of a matrix product state and its central orthogonal form in one embodiment;

[0061] Figure 5 A schematic diagram of a process for calculating the energy value of a Hamiltonian based on a matrix product state in one embodiment;

[0062] Figure 6 FIG. 1 is a schematic diagram of a process for calculating energy values ​​in a central orthogonal form in one embodiment;

[0063] Figure 7 A schematic diagram of a quantum circuit design framework and noise scaling thereof in one embodiment;

[0064] Figure 8 A block diagram of a device for acquiring information from a quantum system according to an embodiment of the present invention;

[0065] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0067] In one embodiment, Figure 1 As shown, a method for acquiring information from a quantum system is provided. This embodiment uses the method applied to a terminal as an example. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. The terminal or server is a classical computer or a quantum computer. In this embodiment, the method includes the following steps:

[0068] Step S101 : converting a preset initial quantum state of a target quantum system to obtain an initial matrix product state corresponding to the initial quantum state.

[0069] The target quantum system refers to a computational object consisting of multiple quantum bits, whose state can be expressed as a quantum state. To describe.

[0070] The initial quantum state refers to the preset quantum state of the target quantum system, which is usually derived by classical methods (such as Hartree-Fock state) or physical models, providing a computational basis for subsequent optimization.

[0071] Matrix product state is a mathematical structure used to compactly represent multi-body quantum states. It decomposes high-dimensional quantum states into the product of a series of low-rank tensors, greatly reducing the storage and computational complexity of quantum systems.

[0072] For example, the terminal first acquires a preset initial quantum state of the target quantum system and converts it into a matrix product state (MPS) representation using a tensor network-based approach. This process involves decomposing the global quantum state into a series of local tensors and truncating them using numerical methods such as singular value decomposition to ensure that the matrix product state meets a given truncation error threshold, thereby ensuring the accuracy and feasibility of the calculation. After the conversion is complete, the terminal stores and outputs the generated initial matrix product state to provide input data for subsequent optimization steps.

[0073] The Hartree-Fock state is an approximate quantum state calculated using the Hartree-Fock method. Specifically, it is an approximate ground state for a multi-electron system, usually expressed as a Slater determinant, which describes the electron's wave function. During the calculation, an initial guess for the single-electron orbital is first made, and then the Hartree-Fock equation is solved through self-consistent field iteration until the electron density and wave function converge. The specific form of the Hartree-Fock state depends on the Hamiltonian of the molecular system and the selected computational basis set.

[0074] Step S102 , optimizing the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state.

[0075] Among them, the quantum energy value refers to the expected energy corresponding to the Hamiltonian H of the quantum system, that is, , which reflects the energy of the system's current quantum state. The initial matrix product state (MPS) is a tensor network structure used to compactly represent many-body quantum states. The target matrix product state is the quantum state obtained in MPS form after optimization, whose energy value is lower than the initial state and is as close to the system's ground state (lowest energy state) as possible.

[0076] For example, the terminal first constructs the Hamiltonian operator of the system, which describes the interaction and energy relationship of the target quantum system. Subsequently, the terminal optimizes the initial matrix product state based on the variational optimization method with the goal of minimizing the quantum energy value. In the specific implementation, the terminal uses the gradient descent method or the conjugate gradient method to calculate the gradient of each MPS tensor according to the local optimization update rule, and updates it according to the learning rate η, so that the energy expectation value of the MPS gradually decreases. In addition, in order to improve the optimization stability, the terminal maintains the orthogonal normalization of the MPS during the optimization process to ensure the numerical stability of the calculation. After multiple rounds of optimization iterations, when the convergence conditions are met (such as the energy change is lower than the set threshold), the terminal outputs the optimized target matrix product state, providing a basis for subsequent quantum circuit parameter extraction.

[0077] Step S103: Obtaining initial circuit parameters of the quantum circuit according to the target matrix product state.

[0078] A quantum circuit is a sequence of operations used to simulate and solve quantum systems on a quantum computer. It consists of a series of parameterized quantum gates. The initial circuit parameters are adjustable parameters in the quantum circuit (such as the angle of a rotating gate), and their settings determine the quantum state generated by the quantum circuit.

[0079] For example, the terminal first analyzes the tensor structure of the target matrix product state and uses a tensor decomposition method (such as singular value decomposition) to map the local tensor in the target matrix product state to the form of a parameterized quantum gate. The terminal determines the corresponding single-bit rotation gate (such as R y (θ), R z The terminal extracts the numerical information of each tensor and converts it into the corresponding initial circuit parameters θ0. During the parameter mapping process, the terminal uses a transformation learning algorithm or least-squares fitting method to ensure that the resulting initial circuit parameters ensure that the quantum state output by the quantum circuit is as close as possible to the target matrix product state. After the conversion is completed, the terminal stores the resulting initial circuit parameters to provide input for subsequent quantum circuit optimization.

[0080] Step S104: Optimize the initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit.

[0081] For example, the terminal first optimizes the initial circuit parameters based on the energy minimization objective using a variational quantum eigensolver or other quantum optimization algorithm. The terminal then executes the parameterized quantum circuit on quantum hardware, measures the expected value of the system's Hamiltonian, and calculates the gradient information of the parameters. During the optimization process, the terminal estimates the gradient using parameter offset or finite difference methods, and iteratively updates the circuit parameters using classical optimization methods such as gradient descent, conjugate gradient method, or natural gradient optimization.

[0082] During the optimization process, the terminal continuously evaluates the optimized energy value and sets convergence criteria (such as the energy change being less than a certain threshold or reaching an upper limit on the number of optimization iterations). Once the optimization converges, the terminal outputs the final optimized target circuit parameters. These parameters are used to construct an optimal quantum circuit that is as close as possible to the ground state of the target quantum system, providing a high-precision quantum state representation for the final calculation of the ground state energy.

[0083] Step S105 : determining the ground state energy value of the target quantum system according to the target circuit parameters.

[0084] Among them, the ground state energy value is the lowest energy eigenvalue of the quantum system Hamiltonian H, which usually represents the most stable state of the system.

[0085] For example, the terminal first runs an optimized parameterized quantum circuit on a quantum computer to obtain the corresponding quantum state. Then, based on the obtained quantum state, the terminal calculates the ground-state energy value of the target quantum system on a classical computer. To improve computational accuracy, the terminal can sample the quantum measurement results multiple times and employ error mitigation methods (such as measurement error correction or zero-noise extrapolation) to reduce experimental errors. Once the measurement converges, the terminal outputs the ground-state energy value of the target quantum system and stores the measurement data, providing reliable computational results for further quantum simulation or physical system analysis.

[0086] In the above-mentioned information acquisition method of the quantum system, first, the preset initial quantum state of the target quantum system is converted to obtain the initial matrix product state corresponding to the initial quantum state. By converting the initial quantum state of the target quantum system through the matrix product state, the storage complexity of the quantum state can be reduced, so that it can efficiently represent high-dimensional quantum states. The matrix product state structure can effectively describe the quantum system, reduce computational redundancy, and provide a mathematically easier-to-handle representation for subsequent optimization; then, with the goal of minimizing the quantum energy value, the initial matrix product state is optimized to obtain the target matrix product state. By optimizing the initial matrix product state to minimize its energy, it can be ensured that the obtained target matrix product state is as close as possible to the ground state of the target quantum system, avoiding directly solving high-dimensional optimization problems on quantum hardware, making the calculation more stable, and being able to make full use of classical computing resources for pre-optimization, avoiding falling into local extreme values, improving convergence stability, and reducing the number of optimization iterations required for subsequent quantum calculations; then, according to the target matrix product state, the initial circuit parameters of the quantum circuit are obtained, and the initial parameters of the quantum circuit are generated by the optimized matrix product state, so that the constructed quantum circuit can be as close as possible. It can be close to the ground state of the target system, which can effectively reduce the search space during quantum circuit optimization, make the optimization process more physically explainable, avoid local extreme value problems that may be caused by random initialization, and improve the optimization convergence speed; further, the initial circuit parameters of the quantum circuit are optimized to obtain the target circuit parameters of the quantum circuit. Through the optimization process based on energy minimization, the parameters of the quantum circuit are further adjusted to make it closer to the target ground state. The parameters are dynamically corrected through quantum measurement feedback on the quantum hardware, and the initial parameters are fine-tuned and optimized to compensate for the modeling error between classical pre-training and the real quantum system. While maintaining a low circuit depth, the real ground state energy is further approached, the error accumulation caused by noise is suppressed, and the experimental feasibility of the final result is improved; finally, according to the target circuit parameters, the ground state energy value of the target quantum system is determined, and by measuring the output state of the optimized quantum circuit and calculating the expectation value of the Hamiltonian, the ground state energy value of the target quantum system is finally obtained, ensuring that the calculated result of the ground state energy matches the ground state of the actual physical system, and can be applied to different quantum systems, providing a reliable calculation solution for high-precision calculations in fields such as quantum chemistry and material simulation. This method, through the synergy of matrix product state pre-training and quantum parameter optimization, compresses highly entangled state information and generates near-optimal initial parameters during the classical computation phase. This significantly reduces the depth of the quantum circuit, overcomes the execution limitations of noisy quantum hardware, and improves the accuracy and computational efficiency of solving the ground-state energy of the target quantum system. Furthermore, the structural compatibility of the pre-trained parameters with the quantum circuit enables efficient convergence of the classical-quantum optimization process, achieving high-precision solutions for the ground-state energy of the quantum system with limited resources. Compared to traditional variational quantum algorithms, this method avoids blind optimization directly on the quantum circuit and instead utilizes matrix product state optimization to provide better initial conditions, thereby reducing the optimization overhead of the quantum circuit.In addition, this method can adapt to quantum systems of different sizes and can achieve efficient computing on current medium-noise quantum computers, providing a more stable solution method for subsequent quantum computing applications.

[0087] In an exemplary embodiment, the above-mentioned step S102 optimizes the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state, and also includes: using the initial matrix product state as the current matrix product state; calculating the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system; normalizing the current matrix product state to a central orthogonal form to obtain a normalized current matrix product state; based on the normalized current matrix product state, using a stochastic gradient descent optimization model to adjust the current matrix product state to obtain a new current matrix product state, and returning to execute the step of calculating the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system until the quantum energy value corresponding to the current matrix product state no longer decreases, and then using the current matrix product state obtained for the last time as the target matrix product state.

[0088] The current matrix product state refers to the matrix product state at the current step during the optimization iteration process, and its energy value is continuously updated to approximate the system's ground state energy. The Hamiltonian H is the energy operator of the target quantum system, describing the system's dynamic behavior and interactions.

[0089] The central orthogonal form is a standardized representation of the matrix product state (MPS), making the tensor decomposition process more stable and improving optimization convergence. The stochastic gradient descent optimization model is an iterative optimization method based on gradient information, which can effectively reduce the system energy and gradually optimize the representation of the matrix product state.

[0090] Exemplarily, the terminal first sets the initial matrix product state as the current matrix product state, and calculates its corresponding quantum energy value based on the Hamiltonian of the target quantum system. The terminal analyzes the tensor structure of the current matrix product state, and uses the Pauli operator to expand and calculate the expected value of energy. The calculated energy value is used to judge the optimization progress to ensure that the energy value gradually decreases after each round of optimization iteration. Subsequently, the terminal normalizes the current matrix product state, that is, converts it into a central orthogonal form. The normalization process decomposes the current matrix product state through singular value decomposition to ensure that each tensor meets the orthogonality condition. This normalization operation can improve the stability of the optimization and avoid error accumulation during the numerical calculation process. Based on the normalized matrix product state, the terminal uses a stochastic gradient descent optimization model to adjust the tensor parameters of the current matrix product state. During the optimization process, the terminal is based on the gradient calculation formula:

[0091]

[0092] Where η is the learning rate, Represents the gradient of the current matrix product state with respect to energy. The terminal updates each tensor by The value of , causes the energy of the quantum system to decrease.

[0093] The terminal repeatedly executes: calculating the quantum energy value of the current matrix product state; normalizing the current matrix product state; optimizing using the stochastic gradient descent optimization model to update the matrix product state tensor parameters; and checking whether the energy converges.

[0094] When it is detected that the quantum energy value of the current matrix product state no longer decreases in continuous iterations (that is, the convergence condition is met), the terminal terminates the optimization process and outputs the current matrix product state obtained for the last time as the target matrix product state for subsequent quantum circuit parameter mapping.

[0095] In this embodiment, the use of central orthogonal normalization can ensure the numerical stability of the matrix product state, reduce the accumulation of numerical errors during the optimization process, and improve the convergence speed. At the same time, based on the stochastic gradient descent optimization model, the tensor parameters of the matrix product state can be iteratively updated with the goal of energy minimization to ensure the correct optimization direction and avoid falling into local minima. By dynamically monitoring energy changes, the number of optimization iterations can be adaptively controlled, avoiding unnecessary computational overhead and improving computational efficiency. In addition, the target matrix product state can more accurately represent the ground state of the target quantum system, providing higher-quality input for subsequent quantum circuit parameter extraction, thereby improving the overall solution accuracy and convergence performance of quantum computing.

[0096] In an exemplary embodiment, the above-mentioned step S103 obtains the initial circuit parameters of the quantum circuit according to the target matrix product state, and further includes: initializing an initial mapping matrix product state that conforms to the circuit structure of the target quantum system; iteratively adjusting the initial mapping matrix product state until the similarity between the adjusted initial mapping matrix product state and the target matrix product state is maximized, and then using the adjusted initial mapping matrix product state as the target mapping matrix product state; and mapping according to the target mapping matrix product state to obtain the initial circuit parameters of the quantum circuit.

[0097] Among them, similarity is used to measure the closeness between two quantum states, and is usually calculated using the fidelity of the state.

[0098] For example, the terminal first constructs an initial mapping matrix product state that conforms to the circuit structure of the target quantum system as the starting point for optimization. The construction method of this mapping state is based on the preset quantum circuit architecture, including a fixed quantum gate topology, such as a single-bit rotation gate (such as R x (θ), R y (θ), R z(θ)) and two-qubit entanglement gates (such as CNOT gates). The terminal generates the initial mapping matrix product state through random initialization or physics-inspired methods (such as Hartree-Fock approximation) and ensures that its tensor structure matches that of the target quantum system.

[0099] Subsequently, the terminal iteratively adjusts the initial mapping matrix product state to maximize its similarity with the target matrix product state. The fidelity is:

[0100]

[0101] in, and are the target matrix product state and the mapping matrix product state respectively.

[0102] During the optimization process, the terminal uses variational optimization or stochastic gradient descent, with the fidelity of the state as the optimization target, calculates the gradient, and updates the tensor parameters of the mapping matrix product state according to the gradient direction. The terminal repeatedly calculates the similarity of the state and continuously updates the initial mapping matrix product state until the fidelity between it and the target matrix product state reaches the set threshold, which is usually close to 1 (such as 0.99).

[0103] When the optimization process is completed, the terminal uses the final target mapping matrix product state for parameter mapping of the quantum circuit. The terminal analyzes the tensor structure of the target mapping matrix product state and extracts the initial circuit parameters required for the quantum circuit based on its local tensor value. This mapping process is based on the conversion rule from matrix to quantum gate, for example: a single-bit tensor component is mapped to R x (θ), R y (θ), R z The rotation angle (θ); the two-bit tensor is mapped to the parameters of the CNOT gate or controlled rotation gate.

[0104] Finally, the terminal stores and outputs the initial circuit parameters of the quantum circuit, providing a basis for subsequent quantum circuit optimization steps.

[0105] In this embodiment, optimization is performed by maximizing state fidelity, ensuring that the final quantum circuit output state closely matches the target system's ground state, reducing the optimization difficulties associated with random initialization in traditional methods. Furthermore, a gradient-based optimization strategy effectively adjusts the tensor parameters of the mapped matrix product state, enabling its conversion into a parameterized representation of the quantum circuit, improving solution accuracy and computational efficiency. This approach can provide a more optimal parameter initialization scheme within a limited quantum bit and circuit depth, establishing favorable initial conditions for subsequent quantum circuit optimization and enhancing the convergence speed and computational accuracy of the overall solution.

[0106] In an exemplary embodiment, the above step S104 optimizes the initial circuit parameters of the quantum circuit to obtain the target circuit parameters of the quantum circuit, and further includes: using the initial circuit parameters as current circuit parameters; determining the quantum energy value of the quantum circuit based on the current circuit parameters; updating the current circuit parameters based on the stochastic gradient descent optimization model to obtain new current circuit parameters, and returning to the step of determining the quantum energy value of the quantum circuit based on the current circuit parameters until the quantum energy value of the quantum circuit no longer decreases, and then using the current circuit parameters obtained last time as the target circuit parameters of the quantum circuit.

[0107] For example, the terminal first sets the initial circuit parameters to the current circuit parameters and constructs a parameterized quantum circuit based on these parameters. The terminal then executes the quantum circuit on a quantum computer or classical simulator to obtain its quantum state. The terminal then calculates the quantum energy value based on the quantum state on a classical computer. Subsequently, the terminal calculates the gradient of the circuit parameters using a stochastic gradient descent optimization model and updates the parameters along the gradient descent direction.

[0108] After the terminal updates the circuit parameters, it repeats the following steps: calculating the quantum energy value corresponding to the current circuit parameters; updating the current circuit parameters based on gradient optimization; and checking whether the energy converges.

[0109] When it is detected that the energy value of the quantum circuit no longer decreases in consecutive iterations during the optimization process (that is, the convergence condition is met, such as the energy change is lower than the preset threshold), the terminal terminates the optimization process and outputs the current circuit parameters obtained from the last optimization as the target circuit parameters for subsequent measurement and calculation.

[0110] In this embodiment, a stochastic gradient descent optimization model is used to update the quantum circuit parameters along the optimal direction, ultimately obtaining circuit parameters that are closest to the ground state of the target quantum system. The parameter offset method is used to calculate the gradient, avoiding the computationally complex analytical gradient, while reducing the demand for quantum computer hardware and improving optimization stability. By dynamically monitoring changes in quantum energy values, the optimization step size can be adaptively adjusted to ensure efficient search for the optimal parameter configuration and avoid local minimum traps. In addition, this optimization method combines classical computing and quantum measurement to improve the convergence speed of the optimization process and reduce the consumption of quantum resources, making it suitable for efficient solutions in current medium-noise quantum computer environments.

[0111] In an exemplary embodiment, Figure 2 As shown, the above step S105 determines the ground state energy value of the target quantum system according to the target circuit parameters, which can also be achieved by the following steps:

[0112] Step S201 : performing circuit folding and circuit stretching operations on a target quantum circuit to obtain multiple equivalent quantum circuits under different noise scaling parameters.

[0113] The target quantum circuit is a quantum circuit whose corresponding circuit parameters are the target circuit parameters.

[0114] Step S202: Determine the quantum energy value of each equivalent quantum circuit.

[0115] Step S203: Obtain the noise-free quantum energy value of the target quantum circuit according to the quantum energy values ​​of each equivalent quantum circuit, as the noise-free ground state energy value of the target quantum system.

[0116] Circuit folding refers to artificially enhancing quantum noise by adding circuit gate operations without changing the ideal computational result. For example, replacing a quantum gate U with UU†U is theoretically equivalent to the original gate operation, but increases noise accumulation in actual execution. Circuit stretching refers to controlling the noise level by adjusting the execution time of quantum gates or by adding equivalent operations. For example, stretching the amplitude of a rotating gate or adjusting the application of an entanglement gate causes the noise effect to vary proportionally. The noise scaling parameter reflects the noise amplification factor of the equivalent quantum circuit relative to the original circuit and is used in subsequent zero-noise extrapolation calculations. The noise-free ground state energy value refers to the ground state energy of a quantum system under ideal noise-free conditions, as inferred using noise scaling techniques.

[0117] Exemplarily, the terminal first constructs a target quantum circuit, where the parameters of each quantum gate are determined by the target circuit parameters. Subsequently, the terminal constructs multiple equivalent quantum circuits with different noise scaling parameters based on circuit folding and circuit stretching operations.

[0118] In the circuit folding operation, the terminal performs equivalent transformations on single-bit gates U and two-bit gates (such as CNOT gates), for example: 、 .

[0119] These transformations keep the computational results of the circuit unchanged, but can increase the effects of noise during actual implementation.

[0120] In circuit stretching operations, the terminal adjusts the way the quantum gate is executed, for example, increasing the amplitude of the rotating gate , where λ is the noise factor; or adjust the physical execution time of the gate by repeatedly executing an equivalent gate sequence.

[0121] The terminal performs calculations for each equivalent quantum circuit and measures its quantum energy. It collects energy data corresponding to all noise scaling parameters. Then, using the known energy data for different noise scaling parameters, the terminal uses a zero-noise extrapolation method to fit the ground-state energy value in the noise-free case.

[0122] In this embodiment, multiple equivalent quantum circuits are constructed through circuit folding and circuit stretching techniques, making it possible to measure quantum energy values ​​under different noise levels, and to calculate the ground state energy under noise-free conditions based on zero-noise extrapolation, thereby improving the calculation accuracy. By controlling the noise scaling factor, the impact of noise on the measurement results can be effectively captured, and the noise effect can be eliminated through extrapolation methods to obtain a more accurate value of the ground state energy of the quantum system. This method is suitable for medium-noise quantum computers. In the case that current quantum hardware still has noise, it provides an efficient quantum noise mitigation technology, enhancing the applicability of quantum computing in solving practical problems.

[0123] In an exemplary embodiment, step S203 of obtaining the noise-free quantum energy value of the target quantum circuit based on the quantum energy values ​​of each equivalent quantum circuit further includes: obtaining a mapping relationship between the noise scaling parameter and the quantum energy value of the target quantum circuit based on the quantum energy values ​​of each equivalent quantum circuit and the noise scaling parameter of each equivalent quantum circuit; and obtaining the noise-free quantum energy value of the target quantum circuit based on the mapping relationship.

[0124] Exemplarily, the terminal first collects energy measurement data under different noise scaling parameters. Then, based on the noise impact model, the terminal fits the mapping relationship between the noise scaling parameters and the quantum energy value. Common mapping models include: linear extrapolation model, exponential decay model, polynomial fitting or neural network model.

[0125] After the fitting is completed, the terminal calculates the noise-free quantum energy value of the target quantum circuit based on the fitted mapping relationship.

[0126] In this embodiment, by establishing a mapping relationship between the noise scaling parameter and the quantum energy value, it is possible to accurately extrapolate the ground-state energy under noise-free conditions, effectively eliminating the influence of noise on quantum measurement results. Compared to directly measuring noise-affected data, data fitting techniques can be used to reduce experimental errors and improve calculation accuracy. Furthermore, an appropriate extrapolation model can be selected based on the hardware characteristics of the quantum computer, adaptively optimizing the calculation process to ensure efficient and more accurate acquisition of the quantum system's ground-state energy value.

[0127] In another exemplary embodiment, Figure 3 As shown, the present application provides a method for obtaining information from a quantum system, comprising:

[0128] Step S301 : converting a preset initial quantum state of a target quantum system to obtain an initial matrix product state corresponding to the initial quantum state.

[0129] Step S302: Using the initial matrix product state as the current matrix product state.

[0130] Step S303: Calculate the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system.

[0131] Step S304 , normalizing the current matrix product state into a central orthogonal form to obtain the normalized current matrix product state.

[0132] In step S305, based on the normalized current matrix product state, a stochastic gradient descent optimization model is used to adjust the current matrix product state to obtain a new current matrix product state, and the process returns to step S303 until the quantum energy value corresponding to the current matrix product state no longer decreases. The current matrix product state obtained last time is then used as the target matrix product state.

[0133] Step S306 , initializing an initial mapping matrix product state that conforms to the circuit structure of the target quantum system.

[0134] Step S307 , iteratively adjust the initial mapping matrix product state until the similarity between the adjusted initial mapping matrix product state and the target matrix product state is maximized, and then use the adjusted initial mapping matrix product state as the target mapping matrix product state.

[0135] Step S308 : mapping and obtaining initial circuit parameters of the quantum circuit according to the target mapping matrix product state.

[0136] Step S309: Using the initial circuit parameters as current circuit parameters.

[0137] Step S310: determining the quantum energy value of the quantum circuit according to the current circuit parameters.

[0138] In step S311, the current circuit parameters are updated based on the stochastic gradient descent optimization model to obtain new current circuit parameters, and the process returns to step S310 until the quantum energy value of the quantum circuit no longer decreases. The current circuit parameters obtained last time are used as the target circuit parameters of the quantum circuit.

[0139] Step S312 , performing circuit folding and circuit stretching operations on the target quantum circuit to obtain multiple equivalent quantum circuits under different noise scaling parameters.

[0140] The target quantum circuit is a quantum circuit whose corresponding circuit parameters are the target circuit parameters.

[0141] Step S313: determining the quantum energy value of each equivalent quantum circuit, and obtaining a mapping relationship between the noise scaling parameter of the target quantum circuit and the quantum energy value according to the quantum energy value of each equivalent quantum circuit and the noise scaling parameter of each equivalent quantum circuit.

[0142] Step S314: Obtain the noise-free quantum energy value of the target quantum circuit according to the mapping relationship as the noise-free ground state energy value of the target quantum system.

[0143] For example, the Hamiltonian is usually expressed as a linear combination of a set of Pauli operators. For a system of n qubits, the Hamiltonian can be formally expressed as:

[0144]

[0145] in are real coefficients, represents the tensor product of Pauli operators, such as , I is the identity operator, X, Y, and Z are Pauli operators, which are quantum operations acting on a single bit.

[0146] Matrix product states convert high-order tensors Decompose into a series of low-rank tensors The product of:

[0147] ,

[0148] in,

[0149]

[0150] in, represents a set of orthogonal and normalized computational bases.

[0151] like Figure 4 As shown, is the local tensor corresponding to the nth qubit, where represents a physical index of dimension d, The dimension is The problem description at this time is as follows Figure 5 shown.

[0152] Pair wave function Perform preprocessing. Normalized to a central orthogonal form, such as Figure 4 shown.

[0153] definition is the orthogonal center, and the tensor to the left of the orthogonal center satisfies the left orthogonality condition:

[0154] ,

[0155] The tensors to the right of the orthogonal center satisfy the right orthogonality condition:

[0156] .

[0157] in, is the Kronecker symbol, specifically a diagonal matrix.

[0158] When updating local tensors, only the tensor at the orthogonal center is updated at a time, and the other tensors are considered known tensors. The orthogonal center moves from the leftmost end to the rightmost end and then back from the rightmost end to the leftmost end, so that each local tensor in the matrix product state is updated twice.

[0159] like Figure 6 As shown, if the local coupling is located to the left of the orthogonal center, that is, , according to the left-right orthogonality condition, the contraction of the tensor on the left side of the i-th tensor is equal to the identity operator, and the contraction of the tensor on the right side of the orthogonal center is also equal to the identity operator. The tensor is contracted with the Hamiltonian coefficient tensor. When , the calculation simplification is exactly the same.

[0160] In the training of step S305, the objective function is: .

[0161] The update rule for the nth local tensor is:

[0162] , where η is the learning rate.

[0163] This embodiment uses the structural advantage of the matrix product state to design the circuit framework of the wave function. The nth local tensor Simulate the quantum state evolution and entanglement on the nth quantum bit. Design framework as Figure 7 As shown, the local tensor Mapping to parameterized quantum circuits . In constructing the wave function When, the operator Acting on the initial Hartree-Fock state :

[0164]

[0165] For n>0, the two-qubit unitary operator Decomposition into single-bit unitary operators and CNOT gate:

[0166]

[0167] When n=0, the single-qubit unitary operator Breaks down to: .

[0168] In step S312, when performing noise scaling, the technique for increasing the circuit noise level at the gate level is to increase its depth. Figure 7 As shown, the mapping is performed or to achieve circuit folding.

[0169] Extrapolate to the noise-free limit. Extrapolate the noise-free expected value by fitting a curve to the expected values ​​measured at different noise levels. Perform the extrapolation as follows:

[0170] (1) Selecting an extrapolation model: Assuming the expected value By function Description, where f is a noise scaling parameter that depends on and a set of real parameters This embodiment uses a simple neural network consisting of three fully connected layers to fit the expected value function under different noise levels.

[0171] (2) Fitting data: Use the extrapolated model f to fit the expected values ​​measured under different noise scaling to obtain a set of best fitting parameters }.

[0172] (3) Extrapolation of the noise-free limit: by calculating Get the expected value in the noise-free case.

[0173] In this embodiment, by optimizing quantum circuit design, pre-training initialization parameters, and introducing zero-noise extrapolation technology to mitigate noise errors in quantum devices, the calculation accuracy in noisy environments is much higher than that of previous quantum eigensolvers, and the growth of circuit complexity with system size is effectively controlled. Specifically, the quantum circuit structure is designed based on the matrix product state structure, effectively controlling the circuit depth and complexity. The quantum circuit parameters are pre-trained using matrix product states to avoid the impact of initialization on the stability of quantum circuit optimization. The zero-noise extrapolation technology is introduced to mitigate quantum noise, and a neural network is used to improve the noise fitting accuracy, thereby significantly improving the accuracy of noise error mitigation and enhancing the applicability and flexibility of the method under different quantum circuits and noise models.

[0174] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0175] Based on the same inventive concept, embodiments of the present application also provide a quantum system information acquisition device for implementing the aforementioned quantum system information acquisition method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of one or more quantum system information acquisition devices provided below can be found in the above-described limitations on the quantum system information acquisition method and are not further elaborated here.

[0176] In an exemplary embodiment, Figure 8 As shown, a device for acquiring information of a quantum system is provided, comprising: a quantum state conversion module 801, a product state optimization module 802, an initial parameter determination module 803, a target parameter determination module 804, and an energy value determination module 805, wherein:

[0177] The quantum state conversion module 801 is used to convert the preset initial quantum state of the target quantum system to obtain an initial matrix product state corresponding to the initial quantum state;

[0178] A product state optimization module 802 is configured to optimize the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state;

[0179] An initial parameter determination module 803 is used to obtain initial circuit parameters of the quantum circuit according to the target matrix product state;

[0180] A target parameter determination module 804 is used to optimize the initial circuit parameters of the quantum circuit to obtain the target circuit parameters of the quantum circuit;

[0181] The energy value determination module 805 is used to determine the ground state energy value of the target quantum system according to the target circuit parameters.

[0182] In one embodiment, the product state optimization module 802 is further configured to use the initial matrix product state as the current matrix product state; calculate the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system; normalize the current matrix product state into a central orthogonal form to obtain a normalized current matrix product state; adjust the current matrix product state based on the normalized current matrix product state using a stochastic gradient descent optimization model to obtain a new current matrix product state, and return to the step of calculating the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system until the quantum energy value corresponding to the current matrix product state no longer decreases, and then use the current matrix product state obtained last time as the target matrix product state.

[0183] In one embodiment, the initial parameter determination module 803 is further configured to initialize an initial mapping matrix product state that conforms to the circuit structure of a target quantum system; iteratively adjust the initial mapping matrix product state until the similarity between the adjusted initial mapping matrix product state and the target matrix product state is maximized, and then use the adjusted initial mapping matrix product state as the target mapping matrix product state; and map the target mapping matrix product state to obtain initial circuit parameters of the quantum circuit.

[0184] In one embodiment, the target parameter determination module 804 is further configured to use the initial circuit parameters as current circuit parameters; determine the quantum energy value of the quantum circuit based on the current circuit parameters; update the current circuit parameters based on a stochastic gradient descent optimization model to obtain new current circuit parameters; and return to the step of determining the quantum energy value of the quantum circuit based on the current circuit parameters until the quantum energy value of the quantum circuit no longer decreases, whereupon the last obtained current circuit parameters are used as the target circuit parameters of the quantum circuit.

[0185] In one embodiment, the energy value determination module 805 is further configured to obtain multiple equivalent quantum circuits under different noise scaling parameters by performing circuit folding and circuit stretching operations on a target quantum circuit, wherein the target quantum circuit is a quantum circuit whose corresponding circuit parameters are the target circuit parameters; determine the quantum energy value of each equivalent quantum circuit; and obtain, based on the quantum energy values ​​of each equivalent quantum circuit, a noise-free quantum energy value of the target quantum circuit as the noise-free ground state energy value of the target quantum system.

[0186] In one embodiment, the energy value determination module 805 is further configured to obtain a mapping relationship between the noise scaling parameter and the quantum energy value of the target quantum circuit based on the quantum energy values ​​of the respective equivalent quantum circuits and the noise scaling parameters of the respective equivalent quantum circuits; and obtain the noise-free quantum energy value of the target quantum circuit based on the mapping relationship.

[0187] Each module in the aforementioned quantum system information acquisition device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0188] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 9 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for acquiring information from a quantum system. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0189] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0190] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0191] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0192] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0193] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0194] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0195] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0196] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for obtaining information from a quantum system, characterized in that: The method comprises: Converting a preset initial quantum state of a target quantum system to obtain an initial matrix product state corresponding to the initial quantum state; With the goal of minimizing the quantum energy value, optimizing the initial matrix product state to obtain a target matrix product state; Obtaining initial circuit parameters of the quantum circuit according to the target matrix product state; Optimizing initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit; According to the target circuit parameters, a ground state energy value of the target quantum system is determined.

2. The method according to claim 1, characterized in that The step of optimizing the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state includes: Using the initial matrix product state as the current matrix product state; Calculating the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system; Normalizing the current matrix product state into a central orthogonal form to obtain a normalized current matrix product state; Based on the standardized current matrix product state, a stochastic gradient descent optimization model is used to adjust the current matrix product state to obtain a new current matrix product state, and the step of calculating the quantum energy value corresponding to the current matrix product state according to the Hamiltonian of the target quantum system is returned to execute until the quantum energy value corresponding to the current matrix product state no longer decreases, and the current matrix product state obtained for the last time is used as the target matrix product state.

3. The method according to claim 1, characterized in that The step of obtaining initial circuit parameters of a quantum circuit according to the target matrix product state comprises: Initializing an initial mapping matrix product state that conforms to the circuit structure of the target quantum system; Iteratively adjusting the initial mapping matrix product state until the similarity between the adjusted initial mapping matrix product state and the target matrix product state is maximized, and then using the adjusted initial mapping matrix product state as the target mapping matrix product state; According to the target mapping matrix product state, initial circuit parameters of the quantum circuit are obtained by mapping.

4. The method according to claim 1, characterized in that: The step of optimizing the initial circuit parameters of the quantum circuit to obtain target circuit parameters of the quantum circuit includes: Using the initial circuit parameters as current circuit parameters; Determining a quantum energy value of the quantum circuit according to the current circuit parameters; Based on the stochastic gradient descent optimization model, the current circuit parameters are updated to obtain new current circuit parameters, and the step of determining the quantum energy value of the quantum circuit according to the current circuit parameters is returned to be executed until the quantum energy value of the quantum circuit no longer decreases, and the current circuit parameters obtained for the last time are used as the target circuit parameters of the quantum circuit.

5. The method according to claim 1, characterized in that Determining the ground state energy value of the target quantum system according to the target circuit parameter includes: By performing circuit folding operations and circuit stretching operations on a target quantum circuit, a plurality of equivalent quantum circuits under different noise scaling parameters are obtained; the target quantum circuit is a quantum circuit whose corresponding circuit parameters are the target circuit parameters; Determine the quantum energy value of each equivalent quantum circuit; According to the quantum energy values ​​of the respective equivalent quantum circuits, the noise-free quantum energy value of the target quantum circuit is obtained as the noise-free ground state energy value of the target quantum system.

6. The method according to claim 5, characterized in that The step of obtaining the noise-free quantum energy value of the target quantum circuit according to the quantum energy values ​​of each equivalent quantum circuit comprises: According to the quantum energy values ​​of the respective equivalent quantum circuits and the noise scaling parameters of the respective equivalent quantum circuits, a mapping relationship between the noise scaling parameters and the quantum energy values ​​of the target quantum circuit is obtained; According to the mapping relationship, the noise-free quantum energy value of the target quantum circuit is obtained.

7. A device for acquiring information from a quantum system, characterized in that: The device comprises: A quantum state conversion module, used to convert a preset initial quantum state of a target quantum system to obtain an initial matrix product state corresponding to the initial quantum state; A product state optimization module, used to optimize the initial matrix product state with the goal of minimizing the quantum energy value to obtain a target matrix product state; An initial parameter determination module, used to obtain initial circuit parameters of the quantum circuit according to the target matrix product state; A target parameter determination module, used for optimizing the initial circuit parameters of the quantum circuit to obtain the target circuit parameters of the quantum circuit; The energy value determination module is used to determine the ground state energy value of the target quantum system according to the target circuit parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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