Measurement-based quantum calculation method of physical drive variable component sub-character solver

Abstract

The invention provides a measurement-based quantum calculation method for a physical driving variable component sub-character solicitation solver, and the method comprises the steps: S1, constructing a variational structure according to a Hamiltonian expression of a to-be-solved quantum multi-body system; s2, converting the variational structure into a graph state and a measurement mode corresponding to a measurement type quantum calculation scheme; s3, initializing an input state; s4, according to the designed measurement sequence, performing a series of single particle measurement on the graph state from the input state to obtain the output state of the variable component sub-line; and S5, continuously iteratively optimizing the variational parameters until the ground state energy of the target Hamiltonian is solved. According to the method, a variational structure is constructed from Hamiltonian of a problem to be solved, the advantages of single-particle measurement operation in measurement type quantum calculation are utilized, implementation of a multi-quantum-bit revolving door is simplified by introducing a small number of auxiliary quantum bits, and therefore quantum resource overhead of a quantum circuit is greatly reduced.

Description

Technical Field

[0001] The present invention is a quantum computing method of a physically driven variational quantum eigensolver based on measurement, which belongs to the field of quantum computing, in particular to the field of quantum simulation. Background Art

[0002] Variational quantum algorithms are considered to be one of the most promising methods to achieve quantum advantage in the near future. This type of algorithm embeds the problem to be solved into a parameterized quantum circuit and obtains an approximate solution to the problem by optimizing the circuit parameters. Well-known variational quantum algorithms include the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA), which have shown great application potential in fields such as quantum chemistry and combinatorial optimization. However, existing variational quantum algorithms are mainly built based on traditional quantum circuit models. Limited by the noise level of existing quantum hardware, the performance of many algorithms in real systems often cannot meet theoretical expectations. At the same time, for some problems with special structures, such as the simulation of quantum many-body systems, traditional quantum circuit construction methods may result in large quantum resource overhead.

[0003] As an emerging universal quantum computing model, the measurement-based quantum computing model (MBQC) provides new ideas for the construction of variational quantum algorithms. Different from the "evolution first, measurement later" paradigm in the quantum circuit model, MBQC uses highly entangled graph states as resources and can implement universal quantum algorithms only through local measurement operations and classical communication. Since single-particle quantum measurements usually have high fidelity and can tolerate a certain degree of measurement error, the MBQC model is expected to show its unique advantages. In addition, we found that the multi-qubit rotation operation in MBQC can be implemented with a constant number of single-particle measurements, which provides a unique advantage for building efficient variational quantum algorithms.

[0004] Based on the above understanding, the present invention proposes a measurement-based physically driven variational quantum eigensolver and its calculation method. The calculation method proposed in the present invention uses the Hamiltonian information of the system to be simulated to design a compact variational structure, and simplifies the implementation of the multi-qubit rotating gate by introducing a small number of auxiliary qubits, thereby greatly reducing the quantum resource overhead. The present invention gives full play to the inherent advantages of the MBQC model, provides a new solution for the efficient and reliable implementation of quantum computing on quantum devices, and accelerates the practical application of quantum computing in quantum chemistry, condensed matter physics and other fields.

[0005] Existing variational quantum algorithms are mainly based on traditional quantum circuit models and have the following shortcomings:

[0006] 1. The implementation of variational quantum circuit algorithms usually requires a large number of quantum gate operations, especially multi-qubit rotation gates, which places high demands on the fidelity of current quantum hardware, making it difficult for the performance of the algorithm in real systems to meet theoretical expectations;

[0007] 2. When designing the variational structure, the existing algorithms do not consider the physical characteristics of the problem to be solved, resulting in a lack of pertinence in the constructed circuits and low efficiency in the use of quantum resources;

[0008] 3. The parameter optimization of the variational quantum circuit algorithm is difficult and is easily affected by noise, which affects the accuracy and robustness of the algorithm.

[0009] In view of the above problems, the purpose of the present invention is to provide a measurement-based physically driven variational quantum eigensolver and its calculation method. This method constructs a variational structure starting from the Hamiltonian of the problem to be solved, takes advantage of the single-particle measurement operation in measurement-based quantum computing, and introduces a small number of auxiliary quantum bits to simplify the implementation of multi-qubit rotating gates, thereby greatly reducing the quantum resource overhead of quantum circuits. At the same time, the designed variational structure is compact and highly targeted, and can more efficiently parameterize the target Hamiltonian and has better fault tolerance. The present invention can significantly reduce the quantum resource overhead of variational quantum algorithms on recent quantum devices and accelerate their engineering applications in fields such as quantum simulation. Summary of the invention

[0010] The present invention mainly solves the following technical problems:

[0011] 1. The existing variational quantum circuit algorithm requires a large number of quantum gate operations, especially multi-qubit rotation gates, which have high fidelity requirements on current quantum hardware, making it difficult for the algorithm to achieve expected performance in real systems;

[0012] 2. Existing algorithms do not fully consider the physical characteristics of the problem to be solved when designing the variational structure, resulting in a lack of pertinence in the constructed circuits and low efficiency in the use of quantum resources;

[0013] 3. The parameter optimization of the variational quantum circuit algorithm is difficult and is easily affected by noise, which affects the accuracy and robustness of the algorithm.

[0014] To solve the above problems, the present invention adopts the following technical solutions:

[0015] 1. Use the Hamiltonian information of the problem to be solved to construct a measurement-based variational quantum algorithm that matches its physical structure. By associating the variational parameters with the key physical parameters in the Hamiltonian, the constructed quantum graph can more efficiently and accurately describe the ground state properties of the target system. Compared with traditional variational quantum circuits, the method of the present invention has stronger physical connotations and characterization capabilities. By cleverly utilizing the unique advantages of the measurement-based quantum computing model (MBQC), the present invention realizes a variational structure directly related to physical quantities, greatly improving the performance of the algorithm.

[0016] 2. Introduce additional auxiliary qubits to simplify the implementation of multi-qubit rotation operations. The multi-qubit rotation gate in the traditional quantum circuit needs to be decomposed into a series of single and double qubit gates, which increases the circuit depth and quantum resource overhead. The present invention takes advantage of the characteristics of MBQC and introduces a small number of auxiliary bits to implement the multi-qubit rotation gate only through a single single-particle measurement, thereby greatly reducing the complexity and implementation difficulty of quantum computing. The reason why similar solutions are not adopted in the prior art may be that the introduction of additional bits will increase the scale of the system, and researchers are worried that it will lead to an increase in computing costs. However, the present invention cleverly utilizes the advantages of the measurement operation in MBQC, successfully reduces the overall resource consumption of the algorithm, and resolves this contradiction.

[0017] 3. Use a measurement-type quantum computing model to improve the fault tolerance and robustness of the algorithm. Unlike the circuit model that relies on quantum gate operations, MBQC only needs to perform local projection measurements and classical communications, and does not need to perform fault-tolerant quantum gate operations, so it has better fault tolerance. At the same time, through the feedback and correction of measurement results, MBQC has a certain robustness to measurement errors and decoherence noise. The present invention gives full play to these advantages of the measurement-type model, making the actual performance of the algorithm on quantum hardware more stable and reliable. Few works in the prior art have attempted to use MBQC for variational quantum algorithms. The present invention, through systematic theoretical analysis and scheme design, proves that the MBQC model can significantly improve the fault tolerance and robustness of the algorithm, and provides new ideas for the engineering implementation of variational quantum algorithms.

[0018] The specific steps of the present invention are as follows:

[0019] Step S1: construct a variational structure according to the Hamiltonian expression of the quantum many-body system to be solved;

[0020] S1.1: Determine the target physical Hamiltonian;

[0021] S1.2: Design the basic unit operations of the variational structure according to the target physical Hamiltonian;

[0022] Step S2: converting the variational structure into the corresponding graph state and measurement mode under the measurement quantum computing scheme;

[0023] S2.1: Design the corresponding diagram and measurement mode according to the form of the system Hamiltonian;

[0024] S2.2: Using single-qubit measurement operations to implement multi-qubit rotation operations in variational structures;

[0025] Step S3: Initialize input state;

[0026] S3.1: Determine the form of the input state;

[0027] S3.2: Prepare the required input state;

[0028] Step S4: According to the designed measurement sequence, a series of single-particle measurements are performed on the graph state starting from the input state to obtain the output state of the variational quantum circuit;

[0029] S4.1: Perform single-particle projection measurement on each qubit in the graph state using the obtained measurement pattern;

[0030] S4.2: Determine and perform corrections to the subsequent measurement basis based on the results of each measurement;

[0031] S4.3: After all measurements are completed, the final Pauli correction is performed to obtain the output state of the variational circuit;

[0032] Step S5: Continuously iteratively optimize the variational parameters until the ground state energy of the target Hamiltonian is solved;

[0033] S5.1: Define the optimization objective function;

[0034] S5.2: Select a classic optimization algorithm;

[0035] S5.3: Continue to minimize the expected energy and iteratively execute steps S4, S5.1 and S5.2 until convergence to obtain the ground state energy.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. Make full use of the physical structure information of the problem to be solved and construct a measurement-based variational quantum algorithm that matches the Hamiltonian form. By associating the variational parameters with the key physical parameters in the Hamiltonian, the constructed quantum graph can more efficiently and accurately describe the ground state properties of the target system. Compared with traditional variational quantum circuits, the method of the present invention has stronger physical connotations and characterization capabilities.

[0038] 2. Introduce additional auxiliary qubits to simplify the implementation of multi-qubit rotation operations. The multi-qubit rotation gate in the traditional quantum circuit needs to be decomposed into a series of single and double qubit gates, which increases the circuit depth and quantum resource overhead. The present invention utilizes the characteristics of measurement-based quantum computing, introduces a small number of auxiliary bits and uses simple entanglement operations and single-particle measurements to realize the multi-qubit rotation gate only through a single single-particle measurement, thereby greatly reducing the complexity and implementation difficulty of the quantum circuit.

[0039] 3. Use a measurement-type quantum computing model to improve the fault tolerance and robustness of the algorithm. Unlike the circuit model that relies on quantum gate operations, measurement-type quantum computing only requires local projection measurement and classical communication, and does not require fault-tolerant quantum gate operations, so it has better fault tolerance. At the same time, through the feedback and correction of measurement results, the measurement-type computing model has a certain robustness to measurement errors and decoherence noise. The present invention gives full play to these advantages of the measurement-type model, making the actual performance of the algorithm on quantum hardware more stable and reliable.

[0040] 4. It can be extended to a variety of physical system models. The measurement-based variational quantum solution method provided by the present invention has strong universality and is not only applicable to physical models describing spin systems, but can also be extended to other strongly correlated lattice models such as the fermion Hubbard model. By appropriately modifying the physically driven variational structure and measurement mode, the present invention can provide a unified solution for the simulation research of various quantum many-body systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the implementation steps of the present invention.

[0042] Figure 2 Schematic diagram of multi-qubit Z rotation operation.

[0043] Figure 3 The diagram is a schematic diagram of the state formed by the method of the present invention on a one-dimensional Fermi-Hubbard chain.

[0044] Figure 4 It is the measurement mode of some nodes in the graph. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] This embodiment provides a quantum computing method based on a physically driven variational quantum eigensolver, referred to as the MBHVA method. The process is as follows: Figure 1 As shown, it mainly includes the following steps:

[0047] Step S1: According to the Hamiltonian expression of the quantum many-body system to be solved, the variational structure of the corresponding Hamiltonian variation ansatz (HVA) is constructed.

[0048] S1.1: Determine the target physical Hamiltonian H. H can be decomposed into the sum of a series of local Hamiltonians Ha, as shown in formula (1):

[0049]

[0050] Among them, Ha usually chooses the tensor product form of Pauli operators {I, X, Y, Z}.

[0051] S1.2: Design the basic unit operation U(θ) of the variational structure according to the target physical Hamiltonian H:

[0052] |ψ out (θ)>=U D (θ)...U2(θ)U1(θ)|ψ0> (2)

[0053]

[0054] Where D is the number of layers (depth) of the variational structure, θ a,d is the variational parameter corresponding to the ath local Hamiltonian of the dth layer. ψ0 represents the input state, ψ out (θ) represents the output state after passing through the parameter-containing quantum circuit,

[0055] U D (θ) represents the parameter-containing quantum circuit with D layers (i.e. the last layer), U d (θ) is the parameter-containing quantum circuit of the dth layer.

[0056] Step S2: Convert the variational structure in HVA into the corresponding graph and measurement mode under the measurement-based quantum computing (MBQC) scheme.

[0057] S2.1: According to the form of the system Hamiltonian H, design the corresponding graph state |G> and measurement mode. Use the basic theory of MBQC to design the corresponding graph state for the corresponding quantum operation, and combine all parts to design the graph state |G>. According to each quantum bit and related operations, the measurement basis {bases(θ)} of each quantum bit is obtained, and according to the prescribed MBQC measurement order, it is usually prescribed to measure from shallow to deep, from top to bottom, and combine to design the measurement mode of the graph state.

[0058] S2.2: Using the single-qubit measurement operation to realize the multi-qubit rotation operation in the variational structure. Multi-qubit Z rotation operation The MBQC is expressed as Figure 2 (b) In order to implement this operation in MBQC, a series of Controlled-Z two-qubit entanglement operations (CZ operations for short) need to be performed on the auxiliary qubit during the graph state establishment process to entangle the auxiliary qubit with the multiple qubits acted upon by the multi-qubit Z rotation operation. Then, the equivalent execution of this multi-qubit Z rotation operation on the graph state only requires a projection measurement of the auxiliary qubit on {bases(π / 2,θ)}, followed by a classical Pauli correction. Figure 2 (a) is a simple representation of a quantum circuit, which needs to be split into Figure 2 (c), assuming the system size is n, a total of 2(n-1) CNOT gates and 1 Rz rotation gate need to be executed.

[0059] Step S3: Initialize the input state |ψ0>.

[0060] S3.1: Determine the form of the input state |ψ0>, which is usually set to all |+> or |0>. You can also customize the form of the input state.

[0061] S3.2: Prepare the required input state |ψ0>. If the form of the input state is customized, it is necessary to add the auxiliary quantum bits required for the customized input state in the graph state |G> for preparation;

[0062] Step S4: According to the designed measurement sequence, a series of single-particle measurements are performed on the graph state starting from the input state to obtain the output state of the variational quantum circuit |ψ out (θ)>.

[0063] S4.1: Perform single-particle projection measurements on each qubit in the graph state using the obtained measurement pattern in the order determined in S2.1, such as Figure 4 The measurement mode shown in (a) requires performing an XY plane measurement on the first auxiliary qubit, where

[0064] S4.2: According to the results of each measurement, determine the correction of the subsequent measurement basis according to the measurement mode. For example, the correction operator to be performed is X s , where X is the Pau li operator X, s is the measurement result which may be 0 or 1. If the measurement result s is 1, the Pau li operator X needs to be corrected. In mathematical form, the output state of the final variational structure |ψ out (θ)> multiplied by the Hermitian conjugate of X, that is,

[0065] S4.3: According to the measurement mode of the graph state designed in step S2, iteratively execute S4.1 and S4.2. After all measurements are completed, perform Pauli correction on the target quantum bit according to the measurement results to obtain the output state |ψ of the variational structure out (θ)>.

[0066] Step S5: Continue to iterate and optimize the variational parameter θ until the ground state energy of the target Hamiltonian H is solved.

[0067] S5.1: Define the optimization objective function, that is, the target Hamiltonian H in the variational output state |ψ out Expected value under (θ)>:

[0068] E(θ)=<ψ out (θ)|H|ψ out (θ)> (4)

[0069] S5.2: Select a classical optimization algorithm, such as gradient descent method, quasi-Newton method, etc., iteratively optimize the variational parameter θ, and calculate the objective function E(θ) according to formula (4).

[0070] S5.3: Continue to minimize the expected energy, iteratively execute steps S4, S5.1 and S5.2 until convergence. Calculate the final expected energy E(θ) of the target Hamiltonian H, which is its ground state energy.

[0071] This embodiment takes solving the ground state energy and correlation function of the one-dimensional Fermi-Hubbard model as an example to verify the effectiveness of the method of the present invention. Considering the one-dimensional Fermi-Hubbard model under the periodic boundary conditions of N lattice points, its Hamiltonian is:

[0072]

[0073] Among them, t is the transition integral between the nearest neighboring grid points, U is the Coulomb repulsion energy within the grid point, is the fermion creation (annihilation) operator of the i-th lattice point spin σ (σ=↑or↓), n iσ is the particle number operator,<i,j> In this example, t=1 is taken as the energy scale, U=1 is taken as the Coulomb repulsion energy, and the Fermi particle number n=3.

[0074] Unlike the spin model, the Hamiltonian form of the fermion system contains creation (annihilation) operators, but the Jordan-Wigner algorithm can be used to convert it into the Pauli operator under the spin system. At this time, the number of spin particles in the spin system is twice the number of Fermi particles, that is, N=6.

[0075] According to steps S1, S2 and S3, the input state |ψ0> is set to all |0>, and the corresponding MBHVA graph state is designed, such as Figure 3shown. Figure 3 The measurement modes of the blue, yellow and red nodes are as follows Figure 4 (c) and requires four single-particle projection measurements, where M XY (-θ) indicates measurement with an angle of -θ on the XY plane. The measurement mode of the orange node is as follows: Figure 4 (b) Two single-particle projection measurements are required, and the green node measurement mode is as follows Figure 2 (b) Only one single-particle projection measurement at an angle of -θ is required for the auxiliary quantum bit a in the YZ plane.

[0076] According to steps S3, S4 and S5, the constructed graph states |G> are measured one by one, and the variational parameters are optimized, which is repeated many times until convergence to obtain the ground state energy of the one-dimensional fermion Hubbard model.

[0077] When the number of spin particles N = 6, in order to obtain the output state |ψ out (θ)>, only 131 single-particle projection measurement operations need to be performed after the MBQC state |G> is established. The calculation mode based on quantum circuits requires a total of 164 gate operations, including 94 single-qubit gate operations and 70 double-qubit gate operations.

[0078] By comparing the number of operations, we can see that when the quantum graph is established, the calculation mode of MBQC, that is, the method described in the present invention, has fewer operations than the calculation mode based on quantum circuits. Since the fidelity of single-particle projection measurement operations is currently better than that of dual-qubit gate operations, the method described in the present invention may also be better than the calculation mode based on quantum circuits in terms of the fidelity of the calculation results.

[0079] In summary, the measurement-based physically driven variational quantum eigensolver provided by the present invention utilizes the problem structure to design the variational structure and introduces additional auxiliary bits to simplify the implementation of the multi-body rotation gate, which can significantly reduce the number of operations in the quantum resources in quantum computing and may have advantages in the fidelity of the calculation results.

[0080] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A quantum computing method based on a physically driven variational quantum eigensolver, characterized in that: The specific steps are as follows: Step S1: construct a variational structure according to the Hamiltonian expression of the quantum many-body system to be solved; S1.1: Determine the target physical Hamiltonian; S1.2: Design the basic unit operations of the variational structure according to the target physical Hamiltonian; Step S2: converting the variational structure into the corresponding graph state and measurement mode under the measurement quantum computing scheme; S2.1: Design the corresponding diagram and measurement mode according to the form of the system Hamiltonian; S2.2: Using single-qubit measurement operations to implement multi-qubit rotation operations in variational structures; Step S3: Initialize input state; S3.1: Determine the form of the input state; S3.2: Prepare the required input state; Step S4: According to the designed measurement sequence, a series of single-particle measurements are performed on the graph state starting from the input state to obtain the output state of the variational quantum circuit; S4.1: Perform single-particle projection measurement on each qubit in the graph state using the obtained measurement pattern; S4.2: Determine and perform corrections to the subsequent measurement basis based on the results of each measurement; S4.3: After all measurements are completed, the final Pauli correction is performed to obtain the output state of the variational circuit; Step S5: Continuously iteratively optimize the variational parameters until the ground state energy of the target Hamiltonian is solved; S5.1: define the optimization objective function; S5.2: Select a classic optimization algorithm; S5.3: Continue to minimize the expected energy and iteratively execute steps S4, S5.1 and S5.2 until convergence to obtain the ground state energy.

2. The quantum computing method of the physically driven variational quantum eigensolver based on measurement according to claim 1, characterized in that: In step S1.1, the target physical Hamiltonian H is determined; H can be decomposed into the sum of a series of local Hamiltonians Ha, as shown in formula (1): Among them, Ha usually chooses the tensor product form of Pauli operators {I, X, Y, Z}.

3. A quantum computing method based on a physically driven variational quantum eigensolver according to claim 1 or 2, characterized in that: In step S1.2, the basic unit operation U(θ) of the variational structure is designed according to the target physical Hamiltonian H: |ψ out (θ)>=U D (θ)...U2(θ)U1(θ)|ψ0> (2) Where D is the number of layers of the variational structure, θ a,d is the variational parameter corresponding to the ath local Hamiltonian of the dth layer; ψ0 represents the input state, ψ out (θ) represents the output state after passing through the parameter-containing quantum circuit, U D (θ) represents a parameter-containing quantum circuit with D layers, U d (θ) is the parameter-containing quantum circuit of the dth layer.

4. The quantum computing method of the physically driven variational quantum eigensolver based on measurement according to claim 1, characterized in that: In step S2.1, according to the form of the system Hamiltonian H, the corresponding graph state |G> and measurement mode are designed; the basic theory of MBQC is used to design the corresponding graph state for the corresponding quantum operation, and all parts are combined to design the graph state |G>; the measurement basis {bases(θ)} of each quantum bit is obtained according to each quantum bit and related operations, and according to the prescribed MBQC measurement order, it is usually prescribed to measure from shallow to deep layers and from top to bottom in sequence, and the measurement mode of the graph state is designed in combination.

5. The quantum computing method of the physically driven variational quantum eigensolver based on measurement according to claim 1 or 4, characterized in that: In step S2.2, a single-qubit measurement operation is used to implement a multi-qubit rotation operation in a variational structure; Multi-qubit Z-rotation operation In order to implement this operation in MBQC, a series of Controlled-Z two-qubit entanglement operations, referred to as CZ operations, need to be performed on the auxiliary qubit during the graph state establishment process to entangle the auxiliary qubit with the multiple qubits acted upon by the multi-qubit Z rotation operation. Then, the equivalent execution of this multi-qubit Z rotation operation on the graph state only requires a projection measurement of the auxiliary qubit on {bases(π / 2,θ)}, followed by classical Pauli correction.

6. The quantum computing method of the physically driven variational quantum eigensolver based on measurement according to claim 1, characterized in that: In step S3.1, the form of the input state |ψ0> is determined and set to full |+> or |0>, or the form of a custom input state; In step S3.2, the required input state |ψ0> is prepared; if the form of the input state is customized, it is necessary to add the auxiliary quantum bits required for the customized input state in the graph state |G> for preparation.

7. The quantum computing method of the physically driven variational quantum eigensolver based on measurement according to claim 1, characterized in that: In step S4.1, the obtained measurement mode is used to perform single particle projection measurement on each qubit in the graph state, and the first auxiliary qubit needs to be measured in the XY plane, where 8. The quantum computing method of the measurement-based physically driven variational quantum eigensolver according to claim 7, characterized in that: In step S4.2, based on the results of each measurement, the correction of the subsequent measurement basis is determined according to the measurement mode. The correction operator to be executed is X s , where X is the Paul i operator X, s is the measurement result which may be 0 or 1. If the measurement result s is 1, the Paul i operator X needs to be corrected. In mathematical form, the output state of the final variational structure |ψ out (θ)> multiplied by the Hermitian conjugate of X, that is, 9. The quantum computing method of the physically driven variational quantum eigensolver based on measurement according to claim 8, characterized in that: In step S4.3, according to the measurement mode of the designed graph state, S4.1 and S4.2 are iteratively executed. After all measurements are completed, the target quantum bit is Pauli corrected according to the measurement results to obtain the output state |ψ of the variational structure. out (θ)>.

10. The quantum computing method of the physically driven variational quantum eigensolver based on measurement according to claim 1, characterized in that: In step S5.1, the optimization objective function is defined, that is, the target Hamiltonian H in the variational output state |ψ out Expected value under (θ)>: E(θ)=<ψ out (θ)|H|ψ out (i)> (4) In step S5.2, a classical optimization algorithm is selected, including the gradient descent method and the quasi-Newton method, to iteratively optimize the variational parameter θ, and the objective function E(θ) is calculated according to formula (4); In step S5.3, the expected energy is continuously minimized, and steps S4, S5.1 and S5.2 are iteratively executed until convergence; the final expected energy E(θ) of the target Hamiltonian H is calculated, which is its ground state energy.

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