A quantum state preparation method and device based on a quantum circuit

Abstract

The application discloses a quantum state preparation method and device based on a quantum circuit. The method comprises the following steps: first, receiving a target data set to be prepared; then, converting the target data set into a first partial data set containing first coefficients and a second partial data set containing second coefficients; subsequently, using a first quantum functional module to prepare a first quantum state containing the first coefficients and the second coefficients; and finally, using a second quantum functional module to construct and evolve a target quantum state preparation circuit containing at least the first partial data set and the second partial data set information, so as to obtain a target quantum state containing the target data set information.

Description

Technical Field

[0001] This invention belongs to the field of quantum computing technology, specifically a method and apparatus for preparing quantum states based on quantum circuits. Background Technology

[0002] A quantum computer is a physical device that performs high-speed mathematical and logical operations, stores and processes quantum information in accordance with the laws of quantum mechanics. When a device processes and calculates quantum information and runs quantum algorithms, it is a quantum computer. Because of its ability to process mathematical problems more efficiently than ordinary computers—for example, reducing the time to crack RSA keys from hundreds of years to hours—quantum computers have become a key technology under research.

[0003] Quantum computing simulation is a simulation program that uses numerical computation and computer science to simulate computations that follow the laws of quantum mechanics. As a simulation program, it uses the high-speed computing power of computers to characterize the spacetime evolution of quantum states based on the fundamental laws of quantum bits in quantum mechanics.

[0004] Quantum state preparation is an essential component of many high-order quantum algorithms, such as quantum walks, Hamiltonian simulations, data fitting, and equation solving. These algorithms require encoding the data vector to be prepared onto qubits. Since the distribution of the data vector is mostly non-uniform, the proportion of the quantum state obtained from a single transformation operation in the entire Hilbert space is finite and cannot fill the entire space. It is necessary to gradually amplify the proportion of the desired target quantum state through quantum logic gates and multiple amplitude amplification operations until the entire space is filled, at which point a deterministic target quantum state is obtained. This results in current quantum state preparation schemes requiring a large number of qubits, deep quantum circuits, numerous amplitude amplification operations, complex preparation schemes, and low practicality. Therefore, it is necessary to implement a new method for quantum state preparation to address the shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a quantum state preparation method and apparatus based on quantum circuits to overcome the shortcomings of the prior art. It transforms the dataset to be prepared into a dataset represented by at least two parts, and then constructs a quantum state preparation circuit based on the information of each part of the dataset to obtain a target quantum state containing the information of the dataset to be prepared. This reduces the number of quantum state preparation amplitude amplification steps, the depth of the quantum state preparation circuit, and the number of quantum logic gates.

[0006] One embodiment of this application provides a quantum state preparation method based on quantum circuits, the method comprising:

[0007] Receive the target dataset to be prepared, the target dataset being represented by at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient, wherein the first part of the dataset, the second part of the dataset and the target dataset have the same dimension;

[0008] Using the first quantum functional module, a first quantum state containing information about the first coefficient and the second coefficient is prepared;

[0009] Based on the first quantum state, using the second quantum functional module, a target quantum state preparation circuit containing at least the information of the first part of the dataset and the second part of the dataset is constructed and evolved to obtain a target quantum state containing the target dataset information.

[0010] Optionally, the first quantum functional module includes a parameterized quantum rotation gate, the parameters of which are determined based on the gradient descent method.

[0011] Optionally, after preparing a first quantum state containing the information of the first coefficient and the second coefficient using the first quantum functional module, the method further includes:

[0012] The second quantum state containing the information of the first part of the dataset is determined based on the value of the preset cost function.

[0013] Optionally, the preset cost function includes:

[0014]

[0015] in, The preset cost function, Let |ψ0> be the parameter, |ψ> be the first quantum state, and |ψ> be the target quantum state.

[0016] Optionally, the first portion of the dataset information is prepared on the amplitude of the second quantum state.

[0017] Optionally, the target quantum state preparation circuit includes:

[0018] A coefficient generation sub-circuit is used to prepare a first quantum state containing at least the information of the first coefficient and the second coefficient using the first quantum functional module;

[0019] The first data preparation sub-circuit is used to evolve a first quantum state containing the information of the first coefficient and the second coefficient using the second quantum functional module to obtain a third quantum state containing at least the information of the first part of the dataset and the second part of the dataset.

[0020] The second data preparation sub-circuit is used to prepare the third quantum state information onto the amplitude of the specified quantum bit |0> state to obtain the target quantum state; wherein, the second data preparation sub-circuit is the transpose conjugate of the coefficient generation sub-circuit.

[0021] Another embodiment of this application provides a quantum state preparation device based on quantum circuits, the device comprising:

[0022] A receiving module is configured to receive a target dataset to be prepared, the target dataset being represented by at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient, wherein the first part of the dataset, the second part of the dataset, and the target dataset have the same dimension;

[0023] A preparation module is used to prepare a first quantum state containing information about the first coefficient and the second coefficient using a first quantum functional module;

[0024] A construction module is used to construct and evolve a target quantum state preparation circuit that contains at least the information of the first part of the dataset and the second part of the dataset based on the first quantum state and using a second quantum functional module, so as to obtain a target quantum state containing the target dataset information.

[0025] Optionally, after the preparation module, the apparatus further includes:

[0026] The determination module is used to determine the second quantum state containing the information of the first part of the dataset based on the value of a preset cost function.

[0027] Another embodiment of this application provides a quantum super cooperative operating system, which realizes quantum state preparation based on quantum circuits according to the method described in any of the above claims.

[0028] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any of the preceding claims when running.

[0029] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0030] Compared with existing technologies, the present invention first receives the target dataset to be prepared, then transforms the target dataset into a representation consisting of at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient. Subsequently, using a first quantum functional module, a first quantum state containing information about the first and second coefficients is prepared. Then, using a second quantum functional module, a target quantum state preparation circuit containing information about the first and second parts of the dataset is constructed and evolved to obtain a target quantum state containing information about the target dataset. By transforming the dataset to be prepared into a representation consisting of at least two parts of the dataset, and then constructing a quantum state preparation circuit based on the information of each part of the dataset, a target quantum state containing information about the dataset to be prepared is obtained. This reduces the number of quantum state preparation amplitude amplification steps, the depth of the quantum state preparation circuit, and the number of quantum logic gates. Attached Figure Description

[0031] Figure 1 This is a system network block diagram of a quantum state preparation method based on quantum circuits provided in an embodiment of the present invention;

[0032] Figure 2 This is a schematic flowchart of a quantum state preparation method based on quantum circuits provided in an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of the structure of a target quantum state preparation circuit provided in an embodiment of the present invention;

[0034] Figure 4 This is a schematic diagram of a quantum state preparation device based on quantum circuits provided in an embodiment of the present invention. Detailed Implementation

[0035] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0036] The present invention first provides a quantum state preparation method based on quantum circuits, which can be applied to electronic devices, such as computer terminals, specifically ordinary computers, quantum computers, etc.

[0037] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a system network block diagram of a quantum state preparation method based on quantum circuits provided in an embodiment of the present invention. The system applied to the quantum state preparation method based on quantum circuits may include a network 110, a server 120, a wireless device 130, a client 140, a storage unit 150, a classical processing system 160, a quantum processing system 170, and may also include additional memory, classical processor, quantum processor and other devices not shown.

[0038] Network 110 is a medium that provides communication links between various devices and computers connected together in a system network for quantum state preparation methods based on quantum circuits. This includes, but is not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The connection method can be wired, wireless communication links, or fiber optic cables.

[0039] Server 120 and client 140 are conventional data processing systems that may contain data and applications or software tools that perform conventional computational processes. Client 140 may be a personal computer or a network computer, so the data may also be provided by server 120. Wireless device 130 may be a smartphone, tablet, laptop, smart wearable device, etc. Storage unit 150 may include database 151, which can be configured to store data such as qubit parameters, quantum logic gate parameters, quantum circuits, and quantum programs.

[0040] The classical processing system 160 (quantum processing system 170) may include a classical processor 161 (quantum processor 171) for processing classical data (quantum data) and a memory 163 (memory 172) for storing classical data (quantum data). The classical data (quantum data) may be a boot file, an operating system image, and an application program 162 (application program 173). The application program 162 (application program 173) may be used to implement a quantum algorithm compiled by the quantum circuit-based quantum state preparation method provided in the embodiments of the present invention.

[0041] Any data or information stored or generated in the classical processing system 160 (quantum processing system 170) can also be configured to be stored or generated in another classical (quantum) processing system in a similar manner, and any application executed therein can also be configured to be executed in another classical (quantum) processing system in a similar manner.

[0042] It should be noted that a true quantum computer has a hybrid structure, which includes at least... Figure 1 The system consists of two main parts: the classical processing system 160, which is responsible for performing classical calculations and control; and the quantum processing system 170, which is responsible for running quantum programs and thus realizing quantum computing.

[0043] The aforementioned classical processing system 160 and quantum processing system 170 can be integrated into a single device or distributed across two different devices. For example, the first device, including the classical processing system 160, runs a classical computer operating system that provides quantum application development tools and services, as well as the storage and network services required for quantum applications. Users develop quantum applications using the quantum application development tools and services on the second device and send the quantum program to the second device, including the quantum processing system 170, via the network services. The second device runs a quantum computer operating system, which parses the code of the quantum program and compiles it into instructions that can be recognized and executed by the quantum computer control system. The quantum processor 170 then implements the quantum algorithm corresponding to the quantum program based on these instructions.

[0044] In the classic silicon-based processing system 160, the units of the classic processor 161 are CMOS transistors. These computing units are not limited by time or coherence; that is, they are available at any time without time constraints. Furthermore, the number of these computing units in a silicon chip is sufficient; currently, a classic processor contains tens of thousands of computing units. The sufficient number of computing units and the fixed selectable computing logic of the CMOS transistors, such as AND logic, allow for computational efficiency through a combination of numerous CMOS transistors and limited logic functions.

[0045] Unlike the logic units in the classical processing system 160, the basic computational unit of the quantum processor 171 in the quantum processing system 170 is the qubit. The input of a qubit is limited by coherence and coherence time; that is, a qubit is limited by its available usage time and is not always readily available. Making full use of qubits within their available usage time is a key challenge in quantum computing. Furthermore, the number of qubits in a quantum computer is one of the representative indicators of its performance. Each qubit performs computational functions through on-demand configured logic functions. Given the limited number of qubits and the diverse logic functions available in quantum computing, such as Hadamard gates (H gates), Pauli-X gates (X gates), Pauli-Y gates (Y gates), Pauli-Z gates (Z gates), X gates, RY gates, RZ gates, CNOT gates, CR gates, iSWAP gates, Tofoli gates, etc., quantum computing requires combining a limited number of qubits with diverse combinations of logic functions to achieve computational effects.

[0046] Based on these differences, the design of logic functions applied to qubits (including the design of whether qubits are used and the design of the efficiency of each qubit) is crucial to improving the computational performance of quantum computers and requires specialized design. The aforementioned design considerations for qubits are technical problems that ordinary computing devices do not need to address. In this application, because the data vector distribution to be prepared is mostly non-uniform, the proportion of the quantum state obtained by a single transformation operation in the entire Hilbert space is finite and cannot fill the entire space. It is necessary to gradually amplify the proportion of the desired target quantum state through quantum logic gates and multiple amplitude amplification operations until the entire space is filled, at which point a deterministic target quantum state can be obtained. This results in current quantum state preparation schemes requiring a large number of qubits, deep quantum circuits, numerous amplitude amplification operations, complex preparation schemes, and low practicality, which has become a problem urgently needing to be solved. This application provides a quantum state preparation method and apparatus based on quantum circuits to address the shortcomings of existing technologies. It transforms the dataset to be prepared into a dataset represented by at least two parts, and then constructs a quantum state preparation circuit based on the information of each part of the dataset to obtain a target quantum state containing the information of the dataset to be prepared. This reduces the number of quantum state preparation amplitude amplification steps, the depth of the quantum state preparation circuit, and the number of quantum logic gates.

[0047] See Figure 2 , Figure 2 This is a flowchart illustrating a quantum state preparation method based on quantum circuits provided in an embodiment of the present invention, which may include the following steps:

[0048] S201: Receive the target dataset to be prepared, the target dataset being represented by at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient, wherein the first part of the dataset, the second part of the dataset and the target dataset have the same dimension.

[0049] Specifically, the system receives the target dataset to be prepared, which can be represented in the form of data vectors. For example, it obtains an N-dimensional target dataset to be prepared, and the target dataset is represented as a data vector. It is represented in the form of, where N = 2 n n is a positive integer, and the data vector The elements can include (α′0,…,α′) i ,…,α′ 2n-1 ).

[0050] Specifically, the target dataset can be transformed so that it is represented by at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient, wherein the first part of the dataset, the second part of the dataset, and the target dataset have the same dimension.

[0051] Specifically, the target dataset can be transformed into a representation consisting of at least a first part of the dataset containing the first coefficient and a second part of the dataset containing the second coefficient. The aim is to classify the data vectors as efficiently as possible. For example, the target dataset... It can be converted to at least Where ν0 and ν1 represent the first coefficient and the second coefficient, respectively. This is the first part of the dataset. This is the second part of the dataset.

[0052] In one alternative implementation, the target dataset needs to be preprocessed before it is transformed into a dataset consisting of at least a first part containing a first coefficient and a second part containing a second coefficient.

[0053] For example, normalization preprocessing methods for target datasets are commonly used in data preprocessing. It's a dimensionless process that transforms data vectors with specific properties into relative data vectors with certain relationships, thereby reducing the range between data vector values. It operates on data represented as matrices or vectors, using a pre-defined normalization method to compensate for the effects of data unevenness. Its main purpose is to eliminate differences between element values, thus ensuring that the structure of the target dataset remains largely unchanged while limiting data elements to a certain range, making the distribution of the target dataset more uniform.

[0054] S202: Using the first quantum functional module, prepare a first quantum state containing the information of the first coefficient and the second coefficient.

[0055] Specifically, the first quantum functional module may include a parameterized quantum rotation gate, such as R. y Quantum logic gates.

[0056] For example, to prepare a first quantum state containing information about the first and second coefficients, a parameterized quantum rotation gate can be configured on the qubit of the first quantum state preparation circuit to transform the initial state |0> into the first quantum state |Ψ0>.

[0057] In another alternative implementation, when the target dataset is represented by multiple coefficients and multiple partial datasets, for example, n direct products of R can be configured for n qubits. y Quantum logic gates, their parameters And randomly initialized to values ​​between (0, π), the first quantum state output by this direct product quantum circuit can be Then, the gradient descent method is used to analyze R. y The parameters of the quantum logic gate are iteratively optimized, and a second quantum state containing the information of the first part of the dataset is determined based on the value of the preset cost function.

[0058] It should be noted that the first part of the dataset information is prepared on the amplitude of the second quantum state. The purpose of the preset cost function is to obtain the parameters when the cost function is minimized. and the amplitude distribution of the first quantum state at this time This process involves continuously optimizing the cost function to convergence. In order not to affect the optimization effect of the cost function, it is often necessary to set a certain value for the convergence factor of the cost function, that is, the amount by which the cost function value decreases or increases each time should not be too large, and the smaller the value of the cost function obtained, the better.

[0059] For example, the preset cost function may include:

[0060]

[0061] in, The preset cost function, Let |ψ0> be the parameter, |ψ> be the first quantum state, and |ψ> be the target quantum state.

[0062] S203: Based on the first quantum state, using the second quantum functional module, construct and evolve a target quantum state preparation circuit that contains at least the information of the first part of the dataset and the second part of the dataset, to obtain a target quantum state containing the target dataset information.

[0063] Specifically, the target quantum state preparation circuit may include:

[0064] A coefficient generation sub-circuit is used to prepare a first quantum state containing at least the information of the first coefficient and the second coefficient using the first quantum functional module;

[0065] The first data preparation sub-circuit is used to evolve a first quantum state containing the information of the first coefficient and the second coefficient using the second quantum functional module to obtain a third quantum state containing at least the information of the first part of the dataset and the second part of the dataset.

[0066] The second data preparation sub-circuit is used to prepare the third quantum state information onto the amplitude of the specified quantum bit |0> state to obtain the target quantum state; wherein, the second data preparation sub-circuit is the transpose conjugate of the coefficient generation sub-circuit.

[0067] For example, see Figure 3 , Figure 3 This is a schematic diagram of a target quantum state preparation circuit provided in an embodiment of the present invention. As shown in the figure, the target quantum state preparation circuit includes a set of qubits, including one control bit and several target bits, wherein the initial state of each set of qubits is |0>. This is achieved by a parameterized R... y The coefficient-generating subcircuit, composed of quantum logic gates, serves to prepare a first quantum state containing information about the first coefficient ν0 and the second coefficient ν1. It is controlled by a |0>... Unitary matrix module and a |1> control The unitary matrix modules together form the second quantum functional module; among them, The types and number of quantum logic gates contained in the unitary matrix module are determined by the information in the first part of the dataset. For example, So A unitary matrix module can contain two direct products of H-quantum logic gates. The types and number of quantum logic gates contained in the unitary matrix module are determined by information from the second part of the dataset. For example, So A unitary matrix module can contain two direct products of R. y Quantum logic gates. Through a second quantum functional module, the first quantum state, containing information about the first and second coefficients, is evolved to obtain a third quantum state containing information about the first and second parts of the dataset. This is achieved using a parameterized R... y The second data preparation sub-circuit, formed by the transpose and conjugate of a quantum logic gate, prepares the third quantum state information onto the amplitude of the specified quantum bit's |0> state, thus obtaining the target quantum state.

[0068] It should be noted that before the second data preparation sub-circuit, the information of the first part of the dataset and the second part of the dataset in the target quantum state preparation circuit are quantum entangled with the |0> state and the |1> state, respectively. At this time, it is impossible to obtain the sum of the first part of the dataset and the second part of the dataset by measuring the specified qubit. Therefore, the second data preparation sub-circuit is used to make the |0> state of the specified qubit quantum entangled with the quantum state containing the information of the first part of the dataset and the second part of the dataset, so as to obtain the target quantum state by measurement.

[0069] In one alternative implementation, it is assumed that the data size of the target dataset to be prepared is 2. n The data vector after normalization is The target quantum state is First, configure the coefficient generator sub-circuit containing n qubits with n direct products of R. yQuantum logic gates, their angular vectors And randomly initialized to values ​​between (0, π), the first quantum state output by this direct product quantum circuit can be Set a preset cost function Using gradient descent method to analyze R y The parameters of the quantum logic gates are iteratively optimized, and the second quantum state containing information from the first part of the dataset is determined based on the value of the preset cost function, that is, the parameters are obtained when the cost function is minimized. and the amplitude distribution of the first quantum state at this time Multiply each element in the target dataset by a suitable constant b. 1 To ensure Each element in the array is a positive number, where a 1 The data vector after normalization. The coefficient. Using Alternative Repeat the preparation of the first quantum state described above and use the gradient descent method to process R. y The steps of iteratively optimizing the parameters of a quantum logic gate ensure that each parameter... The proportion of the amplitude distribution of the first quantum state in the target dataset at this time can be expressed as: 1 / b 1 ,1 / (a 1 b 1 b 2 ), 1 / (a 1 b 1 a 2 b 2 b 3 ), ..., and so on, summing and scaling these proportions to 1, and preparing this proportion state using a linear combination algorithm of unitary operators, using each of its basis vectors to control the first part of the dataset to the k-th part of the dataset, thereby achieving control over the target quantum state. Preparation of .

[0070] It should be noted that, typically for the preparation of a target quantum state that is close to a direct product state, the first part of the dataset will dominate, that is, 1 / b. 1 It will be a small number close to 1, so the proportion of the second part of the dataset and the kth part of the dataset will be very small, resulting in a quantum state with a high proportion of the target quantum state. For the current NISQ algorithm, it is possible to retain only the information of the first two to three parts of the dataset and complete the construction of the target quantum state preparation circuit.

[0071] As can be seen, the present invention first receives the target dataset to be prepared, then transforms the target dataset into a representation consisting of at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient. Subsequently, using a first quantum functional module, a first quantum state containing information about the first and second coefficients is prepared. Then, using a second quantum functional module, a target quantum state preparation circuit containing information about the first and second parts of the dataset is constructed and evolved to obtain a target quantum state containing information about the target dataset. By transforming the dataset to be prepared into a representation consisting of at least two parts of the dataset, and then constructing a quantum state preparation circuit based on the information of each part of the dataset, a target quantum state containing information about the dataset to be prepared is obtained, thereby reducing the number of quantum state preparation amplitude amplification steps, reducing the depth of the quantum state preparation circuit, and reducing the number of quantum logic gates.

[0072] See Figure 4 , Figure 4 This is a schematic diagram of a quantum state preparation device based on quantum circuits provided in an embodiment of the present invention. Figure 2 The process shown can include:

[0073] The receiving module 401 is used to receive the target dataset to be prepared, the target dataset being represented by at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient, wherein the first part of the dataset, the second part of the dataset and the target dataset have the same dimension;

[0074] Preparation module 402 is used to prepare a first quantum state containing information of the first coefficient and the second coefficient using the first quantum functional module;

[0075] The construction module 403 is used to construct and evolve a target quantum state preparation circuit that contains at least the information of the first part of the dataset and the second part of the dataset based on the first quantum state and using the second quantum functional module, so as to obtain a target quantum state containing the target dataset information.

[0076] Specifically, after the preparation module, the device further includes:

[0077] The determination module is used to determine the second quantum state containing the information of the first part of the dataset based on the value of a preset cost function.

[0078] Compared with existing technologies, the present invention first receives the target dataset to be prepared, then transforms the target dataset into a representation consisting of at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient. Subsequently, using a first quantum functional module, a first quantum state containing information about the first and second coefficients is prepared. Then, using a second quantum functional module, a target quantum state preparation circuit containing information about the first and second parts of the dataset is constructed and evolved to obtain a target quantum state containing information about the target dataset. By transforming the dataset to be prepared into a representation consisting of at least two parts of the dataset, and then constructing a quantum state preparation circuit based on the information of each part of the dataset, a target quantum state containing information about the dataset to be prepared is obtained. This reduces the number of quantum state preparation amplitude amplification steps, the depth of the quantum state preparation circuit, and the number of quantum logic gates.

[0079] This invention also provides a quantum-supercomputer cooperating operating system, which runs on a quantum computer including a quantum processor and / or a supercomputer including a classical processor, for implementing quantum circuit-based quantum state preparation according to the method described in this embodiment of the invention.

[0080] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the method embodiments described above when running.

[0081] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps:

[0082] S201: Receive the target dataset to be prepared, the target dataset being represented by at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient, wherein the first part of the dataset, the second part of the dataset and the target dataset have the same dimension;

[0083] S202: Using the first quantum functional module, prepare a first quantum state containing information about the first coefficient and the second coefficient;

[0084] S203: Based on the first quantum state, using the second quantum functional module, construct and evolve a target quantum state preparation circuit that contains at least the information of the first part of the dataset and the second part of the dataset, to obtain a target quantum state containing the target dataset information.

[0085] Specifically, in this embodiment, the storage medium may include, but is not limited to, USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks, and other media capable of storing computer programs.

[0086] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the method embodiments described above.

[0087] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0088] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0089] S201: Receive the target dataset to be prepared, the target dataset being represented by at least a first part of the dataset containing a first coefficient and a second part of the dataset containing a second coefficient, wherein the first part of the dataset, the second part of the dataset and the target dataset have the same dimension;

[0090] S202: Using the first quantum functional module, prepare a first quantum state containing information about the first coefficient and the second coefficient;

[0091] S203: Based on the first quantum state, using the second quantum functional module, construct and evolve a target quantum state preparation circuit that contains at least the information of the first part of the dataset and the second part of the dataset, to obtain a target quantum state containing the target dataset information.

[0092] The above description, based on the embodiments shown in the figures, details the structure, features, and effects of the present invention. The above description is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified to have equivalent changes, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A method of preparing a quantum state based on a quantum circuit, characterized by, The method comprises: receiving a target data set to be prepared, the target data set being represented by at least a first partial data set containing first coefficients and a second partial data set containing second coefficients, wherein the first partial data set, the second partial data set and the target data set have the same dimension; preparing a first quantum state containing information of the first coefficients and the second coefficients by using a coefficient generation sub-circuit composed of a parametric RY gate; The first data preparation sub-circuit is used to evolve a first quantum state containing the first coefficient and the second coefficient information to obtain a third quantum state containing at least the first part of the data set and the second part of the data set information; wherein the second quantum function module comprises a unitary matrix module and a unitary matrix module A second data preparation sub-circuit is used to prepare the third quantum state information to a specified quantum bit on the amplitude of the specified quantum bit, to obtain a target quantum state; wherein the second data preparation sub-circuit is a transpose conjugate of the coefficient generation sub-circuit.

2. The method of claim 1, wherein, the coefficient generation sub-circuit comprises a quantum rotation gate with a parameter, and the parameter of the quantum rotation gate is determined based on a gradient descent method.

3. The method of claim 2, wherein, After the step of preparing the first quantum state containing information of the first coefficients and the second coefficients by using the coefficient generation sub-circuit composed of a parametric RY gate, the method further comprises: determining a second quantum state containing information of the first partial data set according to a value of a preset cost function.

4. The method of claim 3, wherein, The preset cost function comprises: wherein, is the preset cost function, is the parameter, is the first quantum state, is the target quantum state.

5. The method of claim 3, wherein, the information of the first partial data set is prepared on an amplitude of the second quantum state.

6. An apparatus for preparing a quantum state based on a quantum circuit, characterized by The device comprises: a receiving module configured to receive a target data set to be prepared, the target data set being represented by at least a first partial data set containing first coefficients and a second partial data set containing second coefficients, wherein the first partial data set, the second partial data set and the target data set have the same dimension; The preparation module is configured to generate a coefficient generation sub-circuit composed of a participation RY gate, and to prepare a first quantum state containing the first coefficient and the second coefficient information; a first data preparation sub-circuit is configured to evolve the first quantum state containing the first coefficient and the second coefficient information to obtain a third quantum state containing at least the first part of the data set and the second part of the data set information; wherein the second quantum function module comprises a unitary matrix module configured to generate a unitary matrix of a unitary matrix module configured to generate a unitary matrix of constructing a module for preparing the third quantum state information to a specified quantum bit by using a second data preparation sub-circuit on the amplitude of the specified quantum bit, obtaining a target quantum state; wherein the second data preparation sub-circuit is a transpose conjugate of the coefficient generation sub-circuit.

7. A quantity super-synergetic operating system, characterized in that, The super-collaborative operating system realizes quantum state preparation based on a quantum circuit according to the method in any one of claims 1 to 5.

8. A storage medium, characterized by The storage medium stores a computer program, wherein the computer program is configured to execute the method in any one of claims 1 to 5 when running. 9.An electronic device comprising a memory and a processor, the electronic device characterized by, The memory stores a computer program, and the processor is configured to execute the computer program to execute the method in any one of claims 1 to 5.

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