Black box quantum state preparation method based on group processing

Abstract

The present application discloses a black box quantum state preparation method based on group processing. The method includes: determining a rough proportion estimate of the target state based on a marker bit and an initial quantum state, wherein the initial quantum state is a superposition state obtained by processing an input data vector based on a black box. When the rough proportion estimate is less than a grouping threshold, the initial quantum state is separated and processed according to a first auxiliary bit to determine a first intermediate quantum state, wherein the grouping threshold is determined based on the number of input data vectors, and the first intermediate quantum state includes a target state portion and an orthogonal state portion, the target state portion includes a first sub-quantum state, and the orthogonal state portion includes a second sub-quantum state. Based on the first intermediate quantum state, a first precise proportion estimate corresponding to the first sub-quantum state and a second precise proportion estimate corresponding to the second sub-quantum state are determined to achieve quantum state preparation. In this way, direct quantum amplitude amplification of the entire data is avoided, and the number of amplitude amplifications is reduced.

Description

Technical Field

[0001] The present application relates to the field of quantum computing, and more specifically, to a black box quantum state preparation method based on group processing. Background Art

[0002] Based on the unique physical properties of qubits, quantum computing demonstrates significant theoretical advantages over classical computing paradigms when addressing specific complex problems. Quantum state preparation is a key step in quantum computing, providing the data carrier for quantum computing. However, during quantum state preparation, the proportion of target states that meet quantum computing requirements is often low, resulting in a low probability of collapse to the target state during measurement, failing to meet quantum computing requirements and resulting in a poor user experience. Summary of the Invention

[0003] The present application provides a black box quantum state preparation method based on group processing.

[0004] The present application provides a method for preparing a black box quantum state based on group processing, the method comprising:

[0005] Determining a coarse fraction estimate of a target state based on the marker bit and an initial quantum state, wherein the initial quantum state is a superposition state obtained by processing an input data vector based on the black box;

[0006] When the coarse proportion estimate is less than a grouping threshold, separating the initial quantum state according to the first auxiliary bit to determine a first intermediate quantum state, wherein the grouping threshold is determined based on the number of the input data vectors, and the first intermediate quantum state includes a target state portion and an orthogonal state portion, the target state portion includes a first sub-quantum state, and the orthogonal state portion includes a second sub-quantum state;

[0007] Based on the first intermediate quantum state, a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state are determined to achieve the quantum state preparation.

[0008] In this way, the computer device determines a coarse fraction estimate of the target state based on the marker bit and the initial quantum state, where the initial quantum state is a superposition state obtained by processing the input data vector based on a black box. Next, when the coarse fraction estimate is less than a grouping threshold, the computer device separates the initial quantum state based on the first auxiliary bit to determine a first intermediate quantum state, where the grouping threshold is determined based on the number of input data vectors. The first intermediate quantum state includes a target state portion and an orthogonal state portion, where the target state portion includes a first sub-quantum state, and the orthogonal state portion includes a second sub-quantum state. Finally, based on the first intermediate quantum state, the computer device determines a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state, thereby achieving quantum state preparation. Thus, when the coarse fraction estimate is less than the grouping threshold, the computer device utilizes the data distribution characteristics of the initial quantum state to separate the initial quantum state and determine the first intermediate quantum state. Furthermore, the computer device determines the first precise fraction estimate and the second precise fraction estimate based on the first intermediate quantum state, thereby avoiding direct quantum amplitude amplification of the entire data and reducing the number of quantum amplitude amplifications.

[0009] In certain embodiments, the method further comprises:

[0010] Based on the black box, the input data vector is loaded in a superposition state form to generate the initial quantum state, wherein the initial quantum state includes an address register and a data register.

[0011] In this manner, based on the black box, the computer device loads the input data vector in a superposition state to generate the initial quantum state, where the initial quantum state includes an address register and a data register. This black box-based loading of the superposition state achieves efficient conversion of classical data to quantum states. Furthermore, because the initial quantum state preserves the amplitude of the input data vector, subsequent quantum amplitude estimation and measurement operations can directly correlate the original data distribution of the input data vector, providing a basis for subsequent coarse fraction estimation and separation processing.

[0012] In certain embodiments, determining a rough fraction estimate of the target state based on the marker bit and the initial quantum state includes:

[0013] Entangling the data register and the tag bit in the initial quantum state based on a controlled NOT gate;

[0014] Measuring the marker bit based on a preset number of measurements to determine the frequency of the target collapsed state;

[0015] The rough proportion estimate is determined according to the frequency of the target collapsed state and the preset number of measurements.

[0016] In this way, using a controlled NOT gate, the computer device entangles the data register and marker bit in the initial quantum state. Next, based on a preset number of measurements, the computer device measures the marker bit to determine the frequency of the target collapsed state. Finally, the computer device determines a rough fraction estimate based on the target collapsed state frequency and the preset number of measurements. In this way, by entangling the data register and marker bit, the abstract quantum state fraction is converted into a measurable probability of the marker bit. This eliminates the need to analyze the high-dimensional initial quantum state; simply measuring the marker bit allows indirect acquisition of the target state fraction.

[0017] In some embodiments, the initial quantum state includes a data register, and when the coarse proportion estimate is less than a grouping threshold, performing separation processing on the initial quantum state according to the first auxiliary bit to determine the first intermediate quantum state includes:

[0018] Based on a controlled NOT gate, performing association processing on the first auxiliary bit and the highest bit information of the data register;

[0019] Based on a preset rule, the first auxiliary bit is used as a control bit, and the data register is compared and processed to determine the first intermediate quantum state.

[0020] In this way, based on the controlled NOT gate, the computer device associates the first auxiliary bit with the most significant bit of the data register. Then, based on preset rules, the computer device uses the first auxiliary bit as a control bit to perform comparison processing on the data register to determine the first intermediate quantum state. In this way, by associating the first auxiliary bit with the most significant bit of the data register, the amplitude information in the data register is converted into the quantum state of the first auxiliary bit, avoiding full bit measurement of the data register. The separation of the first and second sub-quantum states can be achieved solely through comparison processing.

[0021] In some embodiments, determining, based on the first intermediate quantum state, a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state includes:

[0022] Determine the first precision ratio estimate based on the target state portion based on a first preset algorithm;

[0023] Based on the first preset algorithm, the second precision ratio estimation is determined according to the orthogonal state portion.

[0024] In this manner, based on the first preset algorithm, the computer device determines a first precise fraction estimate based on the target state portion. Next, based on the first preset algorithm, the computer device determines a second precise fraction estimate based on the orthogonal state portion. Thus, by separately solving for the first and second precise fraction estimates based on the first preset algorithm, there is no need to measure the average value of each bit to estimate the fraction, thus reducing the number of quantum amplitude amplifications.

[0025] In some embodiments, determining the first precision ratio estimate based on the target state portion based on a first preset algorithm includes:

[0026] Simplifying the first intermediate quantum state to determine a second intermediate quantum state;

[0027] performing a transformation process on the second intermediate quantum state to determine a third intermediate quantum state in the form of a linear combination of eigenvectors;

[0028] Based on the first preset algorithm, the first precise fraction estimate is determined according to the third intermediate quantum state.

[0029] In this manner, the computer device simplifies the first intermediate quantum state to determine the second intermediate quantum state. Next, the computer device transforms the second intermediate quantum state to determine a third intermediate quantum state in the form of a linear combination of eigenvectors. Finally, based on the first preset algorithm, the computer device determines the first precise ratio estimate based on the third intermediate quantum state. In this way, by transforming the simplified second intermediate quantum state to determine the third intermediate quantum state in the form of a linear combination of eigenvectors, the amplitude estimation problem is transformed into a phase estimation problem, reducing computational complexity.

[0030] In some embodiments, determining the first precision ratio estimate based on the third intermediate quantum state based on the first preset algorithm includes:

[0031] measuring the third intermediate quantum state to determine a phase in the third intermediate quantum state;

[0032] determining an amplitude based on the phase in the third intermediate quantum state;

[0033] The first precision ratio estimation is determined based on the amplitude.

[0034] In this manner, the computer device measures the third intermediate quantum state and determines its phase. Next, the computer device determines its amplitude based on the phase of the third intermediate quantum state. Finally, the computer device determines the first precise fraction estimate based on the amplitude. In this way, the third intermediate quantum state encodes the target state amplitude as a phase through a linear combination of eigenvectors. Measuring the first auxiliary bit improves phase accuracy, thereby reducing the estimated error in the amplitude, and thus, the estimated error in the first precise fraction estimate.

[0035] In certain embodiments, the method further comprises:

[0036] Based on a second preset algorithm, a target quantum state is determined according to the first precise ratio estimation and the second precise ratio estimation to achieve the quantum state preparation.

[0037] Thus, based on the second preset algorithm, the computer device determines the target quantum state based on the first and second precise fraction estimates to achieve quantum state preparation. Thus, through the second preset algorithm, the first and second precise fraction estimates are linearly superimposed, thereby achieving the synthesis of the target quantum state with constant operation complexity, reducing the number of quantum amplitude amplifications.

[0038] In some embodiments, determining the target quantum state based on the first precise fraction estimate and the second precise fraction estimate based on a second preset algorithm includes:

[0039] Determining a rotation angle according to the first precise ratio estimate and the second precise ratio estimate;

[0040] Based on a preset quantum gate, and according to the rotation angle, performing correlation processing on the first sub-quantum state and the first quantum state of the second auxiliary bit to determine a first combined quantum state;

[0041] Based on the preset quantum gate, and according to the rotation angle, performing correlation processing on the second sub-quantum state and the second quantum state of the second auxiliary bit to determine a second combined quantum state;

[0042] The target quantum state is determined according to the first combined quantum state and the second combined quantum state.

[0043] In this way, the computer device determines the rotation angle based on the first and second precise ratio estimates. Next, based on a preset quantum gate, the computer device correlates the first sub-quantum state and the first quantum state of the second auxiliary bit based on the rotation angle to determine a first combined quantum state. Furthermore, based on a preset quantum gate, the computer device correlates the second sub-quantum state and the second quantum state of the second auxiliary bit based on the rotation angle to determine a second combined quantum state. Finally, the computer device determines the target quantum state based on the first and second combined quantum states. This allows constant-level quantum gate operations to replace polynomial-level amplification, reducing computational complexity. Furthermore, the second auxiliary bit and rotation gate can be used to achieve precise weight control, improving the success rate of preparing the target quantum state.

[0044] In certain embodiments, the method further comprises:

[0045] When the rough proportion estimate is greater than or equal to the grouping threshold, a quantum amplitude amplification process is performed on the initial quantum state based on a third preset algorithm to determine a target quantum state.

[0046] In this way, when the coarse fraction estimate is greater than or equal to the grouping threshold, the computer device performs quantum amplitude amplification processing on the initial quantum state based on the third preset algorithm to determine the target quantum state. In this way, when the coarse fraction estimate is greater than or equal to the grouping threshold, the third preset algorithm is directly used to efficiently amplify the target state probability and determine the target quantum state.

[0047] Additional aspects and advantages of the embodiments of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0049] Figure 1 This is one of the flow charts of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0050] Figure 2 This is a schematic diagram of the preparation of a standard black box quantum state according to an embodiment of the present application;

[0051] Figure 3 This is the second flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0052] Figure 4 This is the third flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0053] Figure 5 This is the fourth flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0054] Figure 6 This is a schematic diagram of a first quantum state proportion estimation circuit module according to an embodiment of the present application;

[0055] Figure 7 This is the fifth flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0056] Figure 8 This is the sixth flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0057] Figure 9 This is the seventh flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0058] Figure 10 This is the eighth flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0059] Figure 11 This is the ninth flow chart of the black box quantum state preparation method based on group processing according to the embodiment of the present application;

[0060] Figure 12 This is a schematic diagram of an LCU algorithm implementation in an embodiment of the present application;

[0061] Figure 13 This is the tenth flow chart of the black box quantum state preparation method based on group processing in the implementation mode of the present application. DETAILED DESCRIPTION

[0062] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of the present application, and should not be understood as limiting the embodiments of the present application.

[0063] Based on the unique physical properties of quantum bits - quantum superposition and quantum entanglement, quantum computing has shown significant theoretical advantages over classical computing paradigms in dealing with specific complex problems. Unlike classical bits that can only represent two discrete states, 0 or 1, a single quantum bit can be in and Quantum computing can process arbitrary superpositions of qubits, while entanglement between multiple qubits can form high-dimensional correlated states, giving quantum computing a natural ability to process information in parallel. In classical computing, many complex problems, such as prime factorization of large integers, have computational complexity that increases exponentially with the value, making it difficult for classical computers to solve them within a reasonable time. However, quantum computing, with its unique physical properties, demonstrates great potential in solving prime factorization problems.

[0064] In related technologies, quantum state preparation is a key step in quantum computing, providing the data carrier for quantum computing. It is responsible for loading classical data (such as information in the form of binary strings) or quantum data into the amplitude of the quantum state, forming an initial state that can be processed by quantum algorithms.

[0065] However, during quantum state preparation, the proportion of target states that meet quantum computing requirements is often low, resulting in a low probability of collapse to the target state during measurement, which fails to meet quantum computing requirements and leads to a poor user experience. Specifically, the proportion of the target state in the overall quantum state is determined by the data distribution. If most elements in the data vector have small values, the initial proportion of the target state may be far less than 1, or even close to 0. In this case, the probability of the target state collapsing during direct measurement is extremely low, and quantum amplitude amplification algorithms must be used to increase the probability. However, this significantly increases the number of operations and the depth of the quantum circuit, reducing the operating efficiency of a real quantum computer.

[0066] Based on the above questions, please refer to Figure 1 , an embodiment of the present application provides a black box quantum state preparation method based on group processing, the method comprising:

[0067] 01: Determine the rough proportion estimate of the target state based on the marker bit and the initial quantum state;

[0068] 02: When the coarse proportion estimate is less than the grouping threshold, the initial quantum state is separated according to the first auxiliary bit to determine the first intermediate quantum state;

[0069] 03: Based on the first intermediate quantum state, determine a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state to achieve quantum state preparation.

[0070] The embodiment of the present application also provides a computer device, including a memory and a processor. The black box quantum state preparation method based on group processing of the embodiment of the present application can be implemented by the computer device of the embodiment of the present application. Specifically, a computer program is stored in the memory, and the processor is used to determine a rough proportion estimate of the target state based on the marker bit and the initial quantum state. And when the rough proportion estimate is less than the grouping threshold, the initial quantum state is separated and processed according to the first auxiliary bit to determine the first intermediate quantum state. And based on the first intermediate quantum state, a first precise proportion estimate corresponding to the first sub-quantum state and a second precise proportion estimate corresponding to the second sub-quantum state are determined to achieve quantum state preparation.

[0071] The embodiments of the present application also provide a quantum state preparation device. The black box quantum state preparation method based on group processing of the embodiments of the present application can be implemented by the quantum state preparation device of the embodiments of the present application. Specifically, the quantum circuit simulation device includes a determination module. The determination module is used to determine a rough proportion estimate of the target state based on the marker bit and the initial quantum state. And when the rough proportion estimate is less than the grouping threshold, the initial quantum state is separated and processed according to the first auxiliary bit to determine the first intermediate quantum state. And based on the first intermediate quantum state, a first precise proportion estimate corresponding to the first sub-quantum state and a second precise proportion estimate corresponding to the second sub-quantum state are determined to achieve quantum state preparation.

[0072] Specifically, group processing involves dividing the input data vector into several groups based on the size differences of its elements, with minimal size differences within each group. This means that, first, under certain conditions (a rough estimate of proportions is less than a grouping threshold), the input data vector is divided into a numerically larger portion (the "large item group") and a numerically smaller portion (the "small item group") based on a specific processing method. Subsequently, the smaller portion is determined to determine whether the aforementioned conditions (the rough estimate of proportions is less than the grouping threshold) are still met. If so, the division is repeated until the aforementioned conditions (the rough estimate of proportions is less than the grouping threshold) are no longer met. This allows each group to utilize a standard black-box quantum state preparation process, leveraging the uniformity of data distribution within the group to reduce the number of quantum amplitude amplifications. Linear combinations of units (LCUs) are used between groups to avoid the high complexity caused by uneven global data distribution.

[0073] The unitary operator linear combination algorithm is a probabilistic quantum algorithm that can implement the summation of unitary operators in quantum circuits. Assume that the operator U (which may not be a unitary operator) can be decomposed into a linear combination of multiple unitary operators Vi ,in, The algorithm is a probabilistic algorithm, meaning that its output state is not 100% U acting on the input state. Therefore, quantum amplitude amplification (QAA) is required to amplify the probability of the target state to 100%. In the black-box quantum state preparation method based on group processing provided in the embodiments of this application, the unitary operator linear combination algorithm can be used to superimpose the quantum states of the large term group and the small term group according to their respective proportions to synthesize the complete target state, reducing the complexity of quantum state preparation.

[0074] Quantum amplitude amplification is used to increase the probability of the target quantum state. Assuming there is a quantum state ,in, is the target state, is a non-target state orthogonal to the target state; the coefficients satisfy the normalization condition , and the initial target state ratio is , then we need to use the QAA algorithm to exist The proportion in is enlarged to 1, and finally α is enlarged to 1 and β is reduced to 0.

[0075] Black-Box Quantum State Preparation (BBQSP) refers to the process of converting binary string data into , prepared on the quantum state amplitude to form a normalized state process, in which Represents a vector The second norm of , or the square sum of the root modulus, is a mathematical operation. The data source can be classical data or the superposition state basis vector output by a quantum circuit. The name "black box" is derived from the abstraction of the data source as the data source. The core of black box quantum state preparation is to load data into the quantum state amplitude and accelerate the calculation by using quantum parallelism. However, it should be noted that traditional black box quantum state preparation methods are affected by the data distribution of the input data vector. The target state ratio may be extremely low, requiring multiple amplification through QAA. Please refer to Figure 2 , Figure 2 This is a schematic diagram of the preparation of a standard black box quantum state. The black box quantum state preparation process is divided into three steps:

[0076] First, through the black-box module, the data is loaded into the quantum circuit in the form of a superposition state. The quantum state at this time can be expressed as:

[0077]

[0078] in, is the address register, For data register, is the mark bit.

[0079] Second, the compare module will Compare with n and take the proportion With the mark bit The states are entangled (implemented by a controlled NOT gate), and the quantum state of the system becomes:

[0080]

[0081] in, is the target state part, For The orthogonal quantum state part (i.e. the orthogonal state part).

[0082] Third, the target state exist The proportion of ,unless is evenly distributed, otherwise the proportion must be less than 1. Therefore, it is necessary to use the QAA algorithm to Amplify to 1, and get The deterministic target state of the indication:

[0083]

[0084] in, The data can be de-entangled by executing a black-box module.

[0085] The black box quantum state preparation method based on group processing provided in the embodiment of the present application optimizes the above-mentioned quantum amplitude amplification process through group processing, thereby reducing the number of quantum amplitude amplification times.

[0086] The flag bit is an auxiliary bit in a quantum circuit that indicates whether the target state has been successfully prepared. In other words, by measuring the collapse frequency of the flag bit, we can estimate the target state's contribution to the overall quantum state (a rough estimate of the contribution).

[0087] Quantum circuits are tools for quantum computing, used to transform abstract quantum algorithms into sequences of physical operations executable on quantum computing hardware. Essentially, they are graphical models that describe the evolution of quantum bits (qubits), similar to logic circuits in classical computers, but operating on qubits that exhibit quantum superposition and entanglement.

[0088] A quantum bit (Qubit) refers to the basic unit of a quantum circuit and is the carrier of quantum information. It can be in the state of |0>, |1>, and their superposition.

[0089] The initial quantum state refers to the quantum state in which the input data vector is processed into a superposition state by the black box module, which is in the form of: ,in, is the address register, For data register, The initial quantum state has not been normalized or amplified. The target state ratio depends on the data distribution and is the basis for subsequent grouping processing.

[0090] The target state refers to the normalized quantum state that is expected to be prepared, which is in the form of: , that is, the quantum state after the data vector is loaded into the amplitude and normalized.

[0091] The input data vector refers to the binary string data to be prepared into quantum state, which determines the complexity of target state preparation and is recorded as , where each It is a decimal between 0 and 1, which can be represented as an m-bit binary decimal.

[0092] Rough proportion estimation refers to roughly estimating the proportion of the target state in the initial quantum state by measuring the collapse frequency of the marker bit. The formula is: Rough proportion estimation = , which can be used to determine whether group processing is required.

[0093] The grouping threshold is a critical value used to determine whether to start grouping processing. It is determined based on the number n of input data vectors and is usually set to If the rough proportion estimate is lower than the grouping threshold, it means that the target state proportion is too small. Direct quantum amplitude amplification will result in a large number of times and high complexity. Grouping processing is required to separate the large item group and the small item group.

[0094] The first auxiliary bit anc refers to a quantum bit used to assist in separating data. It is associated with the highest bit of the data register through a controlled NOT gate to mark the size characteristics of the input vector data.

[0095] The first intermediate quantum state refers to the quantum state after the first auxiliary bit separation process, which corresponds to the second step in the preparation process of the standard black box quantum state. It is in the form of: , and then we can deduce: .in, is the target state part, and the first sub-quantum state is , For The orthogonal quantum state part (i.e. the orthogonal state part), For the major group, = for The orthogonal state part also includes the second sub-quantum state.

[0096] The first precise proportion estimate refers to the proportion of the target state (the first sub-quantum state) in the first intermediate quantum state, that is, , estimated by the Quantum Amplitude Estimation (QAE) algorithm.

[0097] The second precise proportion estimate refers to the proportion of the second sub-quantum state in the first intermediate quantum state, that is, ,in, = for The second norm of is also estimated using the QAE algorithm. The first and second precise ratio estimates are used to calculate the weights in the subsequent linear combination of the LCU algorithm.

[0098] It should be noted that a single grouping process only divides the data into two groups of sub-quantum states: the first sub-quantum state of the large group and the second sub-quantum state of the small group. If, after grouping, the amount of data contained in the orthogonal state portion remains large, resulting in excessive quantum amplitude amplification, further grouping can be performed on the small groups of the orthogonal state portion. This processing method is consistent with the method used for the initial grouping process. For ease of explanation, the embodiments of this application will only describe the initial grouping process.

[0099] The quantum amplitude estimation algorithm refers to an algorithm that estimates the amplitude corresponding to a basis vector in a quantum state. Specifically, for a quantum state , the goal of the quantum amplitude estimation algorithm is to estimate the amplitude The value of .

[0100] First, the computer device generates a signal based on the flag bit and the initial quantum state. , determine the rough proportion estimate of the target state.

[0101] Then, if the rough proportion estimate is less than the grouping threshold (the grouping threshold is determined by the amount of data n, which is ), the initial quantum state is separated by the first auxiliary bit to obtain the first intermediate quantum state The first intermediate quantum state includes a target state portion and an orthogonal state portion, the target state portion includes a first sub-quantum state, and the orthogonal state portion includes a second sub-quantum state.

[0102] Finally, the computer device uses the first intermediate quantum state , respectively determine the first precise proportion estimate corresponding to the first sub-quantum state and the second precise fraction estimate corresponding to the second quantum state .

[0103] The following describes the black box quantum state preparation method based on group processing provided by the embodiment of the present application, taking the input data vector {0.03, 0.03, 0.04, 0.04, 0.03, 0.03, 0.04, 1.0} as an example. It can be seen that among the eight data (n=8), the first seven are very small, and only the last one is very large, and the ratio between them is large. Then, if we follow Figure 2 The standard black box quantum state preparation process shown is

[0104]

[0105] This is a The number is almost the same, so the number of magnifications required is O( ).

[0106] First, execute Figure 2 The quantum circuit shown is executed, but the subsequent QAA circuit is not executed. Based on the marker bit and the initial quantum state, a rough estimate of the target state is determined.

[0107] Then, when the rough ratio estimate is less than the grouping threshold, the initial quantum state is separated according to the first auxiliary bit to determine the first intermediate quantum state .

[0108] Finally, based on the first intermediate quantum state, a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state are determined to achieve quantum state preparation.

[0109] In summary, in the black box quantum state preparation method based on group processing provided in the embodiments of the present application, a computer device determines a coarse fraction estimate of the target state based on the marker bit and the initial quantum state, wherein the initial quantum state is a superposition state obtained by processing the input data vector based on the black box. Then, when the coarse fraction estimate is less than the grouping threshold, the computer device separates the initial quantum state based on the first auxiliary bit to determine a first intermediate quantum state, wherein the grouping threshold is determined based on the number of input data vectors, and the first intermediate quantum state includes a target state portion and an orthogonal state portion, wherein the target state portion includes a first sub-quantum state, and the orthogonal state portion includes a second sub-quantum state. Finally, the computer device determines a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state based on the first intermediate quantum state to achieve quantum state preparation. In this way, when the coarse fraction estimate is less than the grouping threshold, the data distribution characteristics of the initial quantum state are utilized to separate the initial quantum state to determine the first intermediate quantum state, and then determine the first precise fraction estimate and the second precise fraction estimate based on the first intermediate quantum state, thereby avoiding direct quantum amplitude amplification of the entire data and reducing the number of quantum amplitude amplifications.

[0110] See also Figure 3 In certain embodiments, the method further comprises:

[0111] 04: Based on the black box, the input data vector is loaded in the form of superposition to generate the initial quantum state.

[0112] In some embodiments, the determination module is further configured to perform a superposition state loading process on the input data vector based on a black box to generate an initial quantum state.

[0113] In some embodiments, the processor is further configured to perform superposition state loading processing on the input data vector based on a black box to generate an initial quantum state.

[0114] Specifically, the input data vector is converted into Loaded into the quantum circuit in the form of quantum superposition state to generate the initial quantum state The initial quantum state The form is ,in, is the address register, For data register, The address register is used to store the data index (i is a binary string), the final target state The data register is used to store the amplitude of the data vector ( is an m-bit binary decimal, such as = ), supports entanglement operations with marker bits and auxiliary bits, and by retaining the binary structure of the input data vector, it is convenient for subsequent grouping and separation by features such as the highest bit.

[0115] The initial quantum state is a superposition state, directly retaining the amplitude of the original data , rather than normalizing in advance to avoid information loss, so that the subsequent compare module can directly convert Entangled with the marker bits to provide raw data support for precise ratio estimation.

[0116] In this way, based on the black box, the computer device loads the input data vector in a superposition state to generate an initial quantum state, which includes an address register and a data register. This black box-based loading of the superposition state achieves efficient conversion of classical data to quantum states. Furthermore, because the initial quantum state is a superposition state, the amplitude of the input data vector is directly preserved, avoiding information loss caused by premature normalization. Subsequent quantum amplitude estimation and measurement operations can directly correlate the original data distribution of the input data vector, providing a basis for subsequent coarse proportion estimation and separation processing.

[0117] See also Figure 4 In some embodiments, step 01 (determining a rough fraction estimate of the target state based on the marker bit and the initial quantum state) includes:

[0118] 011: Entangling the data register and the tag bit in the initial quantum state based on the controlled NOT gate;

[0119] 012: Based on the preset number of measurements, measure the marker bit to determine the frequency of the target collapsed state;

[0120] 013: Determine a rough estimate of the proportion based on the frequency of the target collapsed state and the preset number of measurements.

[0121] In certain embodiments, the determination module is further configured to entangle the data register and the marker bit in the initial quantum state using a controlled NOT gate, measure the marker bit based on a preset number of measurements, and determine the frequency of the target collapsed state. Furthermore, a coarse fraction estimate is determined based on the frequency of the target collapsed state and the preset number of measurements.

[0122] In certain embodiments, the processor is further configured to entangle the data register and the marker bit in the initial quantum state using a controlled NOT gate, measure the marker bit based on a predetermined number of measurements, and determine the frequency of the target collapsed state, and determine a coarse fraction estimate based on the frequency of the target collapsed state and the predetermined number of measurements.

[0123] Specifically, a controlled NOT gate refers to a quantum gate operation consisting of a control bit and a target bit. Generally speaking, the control logic of a controlled NOT gate is: when the control bit is When the control bit is When the target bit state remains unchanged. Through the controlled NOT gate, the state of the mark bit and the amplitude of the data register are Establish an association so that the marker bit is in The probability of the state is directly related to the target state ratio. After being entangled with the marker bit, the marker bit The proportion of , that is, the target state The square of the probability amplitude in the population.

[0124] The preset number of measurements refers to the number of times the marker bit is classically measured repeatedly. Each measurement causes the quantum state to collapse to the eigenstate of the marker bit. or , the target state proportion is estimated by statistically analyzing the frequency of measurement results.

[0125] The target collapsed state refers to the target state eigenstate corresponding to the marker bit after measurement, usually Since it has been It is entangled with the marker bit, so the frequency of occurrence of the target collapsed state directly reflects the proportion of the target state in the overall quantum state.

[0126] Continuing with the above example, the preset number of measurements is 100. The initial quantum state obtained by inputting the data vector {0.03, 0.03, 0.04, 0.04, 0.03, 0.03, 0.04, 1.0} The data register and the marker bit are entangled. Then, the marker bit is measured 100 times to determine the occurrence of Finally, according to The frequency of occurrence and the preset number of measurements (100) determine the rough proportion estimate.

[0127] In this way, using a controlled NOT gate, the computer device entangles the data register and marker bit in the initial quantum state. Next, based on a preset number of measurements, the computer device measures the marker bit to determine the frequency of the target collapsed state. Finally, the computer device determines a rough fraction estimate based on the target collapsed state frequency and the preset number of measurements. In this way, by entangling the data register and marker bit, the abstract quantum state fraction is converted into a measurable probability of the marker bit. This eliminates the need to analyze the high-dimensional initial quantum state; simply measuring the marker bit allows indirect acquisition of the target state fraction.

[0128] See also Figure 5 In some embodiments, the initial quantum state includes a data register, and step 02 (when the coarse proportion estimate is less than the grouping threshold, separating the initial quantum state according to the first auxiliary bit to determine the first intermediate quantum state) includes:

[0129] 021: Based on the controlled NOT gate, the first auxiliary bit and the highest bit information of the data register are associated;

[0130] 022: Based on a preset rule, the first auxiliary bit is used as a control bit, and a comparison process is performed on the data register to determine the first intermediate quantum state.

[0131] In some embodiments, the determination module is further configured to associate the first auxiliary bit with the highest bit of the data register using a controlled NOT gate, and to perform a comparison process on the data register using the first auxiliary bit as a control bit based on a preset rule to determine the first intermediate quantum state.

[0132] In some embodiments, the processor is further configured to associate the first auxiliary bit with the highest bit of the data register using a controlled NOT gate, and to perform a comparison process on the data register using the first auxiliary bit as a control bit based on a preset rule to determine the first intermediate quantum state.

[0133] Specifically, each data in the data register Stored as an m-bit binary decimal, = ), where the highest bit is the first digit of the binary decimal .

[0134] The association process is based on the controlled NOT gate (CNOT gate), which connects the first auxiliary bit anc with the highest bit of the data register. Entanglement is performed so that the state of the auxiliary bit directly reflects The operation logic is as follows: , that is, when =1, the auxiliary bit is flipped to ;when =0, the auxiliary bit remains In this way, continuous amplitude information can be Transformed into discrete quantum states of auxiliary bits ( ), providing controllable quantum bit tags for subsequent group separation.

[0135] Comparison processing refers to the conditional comparison operation of the data register using the first auxiliary bit (anc) as the control bit, =1 and small item group =0 quantum state separation. The operation logic is: when the auxiliary bit is (Large term group), perform the operation to entangle the target state part with the marker bit, and generate When the auxiliary bit is (Small item group), the target state part remains orthogonal to the flag bit In this way, through a conditional operation (with the auxiliary bit as the control bit), the initial quantum state is divided into the target state part (large term group) and the orthogonal state part (small term group), forming a structured first intermediate quantum state. .

[0136] Continuing with the above example, see Figure 6 , Figure 6 This is a schematic diagram of the circuit module for estimating the proportion of the first quantum state (large term group). By entangled the auxiliary bit anc and the marker bit flag, an accurate estimation of the proportion of the principal component in the non-uniform data is achieved. When the rough proportion estimate is less than the grouping threshold, the QAE algorithm is used to estimate the proportion of the root sum of squares of the larger terms in the . ,in, express The larger value item is 1.0 among the 8 data; the quantum circuit corresponding to this step is as follows Figure 6 As shown, the quantum state for ; Introduce the first auxiliary bit anc, use the CNOT gate to associate the information of the highest bit of the data with the first auxiliary bit anc, and the quantum state at this time is

[0137]

[0138] in, Representation data The value of the highest bit.

[0139] Then, the first auxiliary bit anc is used as the control bit of the compare module, and the highest bit is 1. Transforming from the basis vectors to the amplitudes of the labeled bits yields the quantum state:

[0140] , and then we can deduce: .in, is the target state part, and the first sub-quantum state is , For The orthogonal quantum state part (i.e. the orthogonal state part), For the major group, = for The orthogonal state part also includes the second sub-quantum state.

[0141] It should be noted that if multiple grouping processes are required, the first grouping is The highest bit is 1 as the control bit; the second grouping is The highest and second highest bits are 01 as control bits, and so on, which will not be repeated here.

[0142] In this way, based on the controlled NOT gate, the computer device associates the first auxiliary bit with the most significant bit of the data register. Then, based on preset rules, the computer device uses the first auxiliary bit as a control bit to perform comparison processing on the data register to determine the first intermediate quantum state. In this way, by associating the first auxiliary bit with the most significant bit of the data register, the amplitude information in the data register is converted into the quantum state of the first auxiliary bit, avoiding full bit measurement of the data register. The separation of the first and second sub-quantum states can be achieved solely through comparison processing.

[0143] See also Figure 7 In some embodiments, step 03 (determining, based on the first intermediate quantum state, a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state) includes:

[0144] 031: Determine a first precision ratio estimation based on the target state portion based on the first preset algorithm;

[0145] 032: Based on the first preset algorithm, determine a second precision ratio estimate according to the orthogonal state portion.

[0146] In some embodiments, the determination module is further configured to determine a first precision ratio estimate based on the target state portion based on a first preset algorithm, and to determine a second precision ratio estimate based on the orthogonal state portion based on the first preset algorithm.

[0147] In some embodiments, the processor is further configured to determine a first precision ratio estimate based on the target state portion based on a first preset algorithm, and to determine a second precision ratio estimate based on the orthogonal state portion based on the first preset algorithm.

[0148] Specifically, the first preset algorithm refers to a quantum amplitude estimation algorithm.

[0149] A quantum amplitude estimation algorithm is used to estimate the precise fraction of the grouped quantum states. Specifically, the quantum amplitude estimation algorithm estimates the amplitude a of the target state, and then calculates the first precise fraction. The quantum amplitude estimation algorithm also estimates the amplitude b of the orthogonal state, and then calculates the second precise fraction.

[0150] In this manner, based on the first preset algorithm, the computer device determines a first precise fraction estimate based on the target state portion. Next, based on the first preset algorithm, the computer device determines a second precise fraction estimate based on the orthogonal state portion. Thus, by separately solving for the first and second precise fraction estimates based on the first preset algorithm, there is no need to measure the average value of each bit to estimate the fraction, thus reducing the number of quantum amplitude amplifications.

[0151] See also Figure 8 In some embodiments, step 031 (determining a first precision ratio estimate based on the target state portion based on the first preset algorithm) includes:

[0152] 0311: Simplify the first intermediate quantum state and determine the second intermediate quantum state;

[0153] 0312: Transform the second intermediate quantum state to determine the third intermediate quantum state in the form of a linear combination of eigenvectors;

[0154] 0313: Based on the first preset algorithm, determine a first precise ratio estimate according to the third intermediate quantum state.

[0155] In certain embodiments, the determination module is further configured to simplify the first intermediate quantum state to determine a second intermediate quantum state, transform the second intermediate quantum state to determine a third intermediate quantum state in the form of a linear combination of eigenvectors, and determine a first precision ratio estimate based on the third intermediate quantum state based on a first preset algorithm.

[0156] In certain embodiments, the processor is further configured to simplify the first intermediate quantum state to determine a second intermediate quantum state, transform the second intermediate quantum state to determine a third intermediate quantum state in the form of a linear combination of eigenvectors, and determine a first precision ratio estimate based on the third intermediate quantum state based on a first preset algorithm.

[0157] Specifically, the simplification process refers to the mathematical simplification of the first intermediate quantum state, separating the amplitude coefficients of the target state part and the orthogonal state part, so as to facilitate the subsequent application of the quantum amplitude estimation (QAE) algorithm. Simplify and get the second intermediate quantum state ,in, is the amplitude coefficient of the target state part, satisfying ; is the first sub-quantum state; is the second sub-quantum state, In this way, through simplification, the complex quantum state expression is simplified to the separation form of the target state and the orthogonal state on the mark bit, highlighting the amplitude coefficient a of the target state, laying the foundation for the subsequent QAE algorithm to estimate the value of a.

[0158] The conversion process refers to further transforming the simplified second intermediate quantum state into a linear combination of eigenvectors that can be directly processed by the quantum amplitude estimation (QAE) algorithm, so as to accurately measure the amplitude of the target state through QAE.

[0159] The specific steps of the transformation process are: convert the second intermediate quantum state Represented as the eigenvector of an operator A and A linear combination of: ,in, is the phase related to the amplitude a, satisfying ; and is the orthogonal eigenvector of operator A, corresponding to the eigenvalue and Thus, through the third intermediate quantum state Encode the target state amplitude a as phase , transforming the amplitude estimation problem into a phase estimation problem, and the QAE algorithm can accurately measure the phase through operations such as quantum Fourier transform , and thus the value of a can be deduced.

[0160] The computer device simplifies the first intermediate quantum state, separating the amplitude coefficients of the target state and the orthogonal state, and determines the second intermediate quantum state. The computer device then transforms the second intermediate quantum state, converting the amplitude coefficients into phase information. This information is then adapted to the QAE measurement mechanism to determine a third intermediate quantum state in the form of a linear combination of eigenvectors. Finally, based on the first preset algorithm, the computer device determines the first precise ratio estimate by measuring the phase of the third intermediate quantum state.

[0161] In this manner, the computer device simplifies the first intermediate quantum state to determine the second intermediate quantum state. Next, the computer device transforms the second intermediate quantum state to determine a third intermediate quantum state in the form of a linear combination of eigenvectors. Finally, based on the first preset algorithm, the computer device determines the first precise ratio estimate based on the third intermediate quantum state. In this way, by transforming the simplified second intermediate quantum state to determine the third intermediate quantum state in the form of a linear combination of eigenvectors, the amplitude estimation problem is transformed into a phase estimation problem, reducing computational complexity.

[0162] See also Figure 9 In some embodiments, step 0313 (determining a first precision ratio estimate based on the third intermediate quantum state based on the first preset algorithm) includes:

[0163] 03131: Measure the third intermediate quantum state and determine the phase in the third intermediate quantum state;

[0164] 03132: Determine the amplitude based on the phase in the third intermediate quantum state;

[0165] 03133: Based on the amplitude, determine the first precision ratio valuation.

[0166] In some embodiments, the determination module is further configured to measure the third intermediate quantum state, determine the phase of the third intermediate quantum state, determine the amplitude based on the phase of the third intermediate quantum state, and determine the first precise ratio estimate based on the amplitude.

[0167] In some embodiments, the processor is further configured to measure the third intermediate quantum state, determine a phase in the third intermediate quantum state, determine an amplitude based on the phase in the third intermediate quantum state, and determine the first precise ratio estimate based on the amplitude.

[0168] Specifically, the process of extracting the target state precision ratio estimate from the third intermediate quantum state based on the quantum amplitude estimation algorithm (i.e., the first preset algorithm) is as follows:

[0169] The third intermediate quantum state is of the form ,in, is the phase related to the amplitude a, satisfying ; and is the orthogonal eigenvector of operator A, corresponding to the eigenvalue and .

[0170] First, the phase in the third intermediate quantum state is determined. In some embodiments, the phase is converted to Converting to measurable classical binary information: First, the auxiliary bits are initialized and transformed into a uniform superposition state through a Hadamard gate. Second, a controlled phase gate is executed on each auxiliary bit to entangle the auxiliary bit with the target state. Third, a quantum Fourier transform is performed on the auxiliary bit and then measured to obtain the result m.

[0171] Then, the amplitude is determined based on the mathematical mapping of phase and amplitude. In some embodiments, the phase estimate corresponding to the measurement result m is: , substitute The target state amplitude a can be obtained. For example, if t = 0 (10 auxiliary bits), the measured value m = 341, then: , .

[0172] Finally, the first precise proportion estimate is determined based on the amplitude. The amplitude a represents the probability amplitude of the target state in the first intermediate quantum state, and its modulus is is the collapse probability of the target state in the intermediate state. In the grouping scenario, ,in, is the two-norm of the large-item group data, and n is the total amount of input data vector. The first precise proportion estimate is the normalized proportion of the target state part in the overall data, which needs to be combined with the overall two-norm Calculate. If a=0.353 (corresponding to n=8), , then the first precision ratio valuation = .

[0173] It should be noted that the calculation of the second precision ratio estimation is the same, except that the corresponding parameters are transformed to obtain , I will not go into details here.

[0174] In this manner, the computer device measures the third intermediate quantum state and determines its phase. Next, the computer device determines its amplitude based on the phase of the third intermediate quantum state. Finally, the computer device determines the first precise fraction estimate based on the amplitude. In this way, the third intermediate quantum state encodes the target state amplitude as a phase through a linear combination of eigenvectors. Measuring the first auxiliary bit improves phase accuracy, thereby reducing the estimated error in the amplitude, and thus, the estimated error in the first precise fraction estimate.

[0175] See also Figure 10 In certain embodiments, the method further comprises:

[0176] 05: Based on the second preset algorithm, the target quantum state is determined according to the first precise ratio estimation and the second precise ratio estimation to achieve quantum state preparation.

[0177] In some embodiments, the determination module is further configured to determine the target quantum state based on a second preset algorithm according to the first precise fraction estimation and the second precise fraction estimation, so as to achieve quantum state preparation.

[0178] In some embodiments, the processor is further configured to determine a target quantum state based on a second preset algorithm according to the first precise fraction estimate and the second precise fraction estimate to achieve quantum state preparation.

[0179] Specifically, the second preset algorithm refers to the unitary operator linear combination algorithm. The core of the unitary operator linear combination algorithm is to linearly combine two or more unitary operators according to weights, and the formula is: ,in, and Corresponding to the unitary transformation of the major term group and the minor term group, 、 is the combination coefficient (i.e., the precise proportion valuation).

[0180] Based on the unitary operator linear combination algorithm, the computer device determines the target quantum state according to the first precise ratio estimation and the second precise ratio estimation to realize quantum state preparation.

[0181] Thus, based on the second preset algorithm, the computer device determines the target quantum state based on the first and second precise fraction estimates to achieve quantum state preparation. Thus, through the second preset algorithm, the first and second precise fraction estimates are linearly superimposed, thereby achieving the synthesis of the target quantum state with constant operation complexity, reducing the number of quantum amplitude amplifications.

[0182] See also Figure 11 In some embodiments, step 05 (determining the target quantum state based on the first precise fraction estimate and the second precise fraction estimate based on the second preset algorithm) includes:

[0183] 051: Determine the rotation angle based on the first precision ratio estimation and the second precision ratio estimation;

[0184] 052: Based on the preset quantum gate, according to the rotation angle, the first sub-quantum state and the first quantum state of the second auxiliary bit are correlated to determine the first combined quantum state;

[0185] 053: Based on the preset quantum gate, according to the rotation angle, the second sub-quantum state and the second quantum state of the second auxiliary bit are correlated to determine the second combined quantum state;

[0186] 054: Determine a target quantum state based on the first combined quantum state and the second combined quantum state.

[0187] In certain embodiments, the determination module is further configured to determine a rotation angle based on the first precise fraction estimate and the second precise fraction estimate; and to correlate the first sub-quantum state and the first quantum state of the second auxiliary bit based on the rotation angle, using a preset quantum gate, to determine a first combined quantum state. The determination module is further configured to correlate the second sub-quantum state and the second quantum state of the second auxiliary bit based on the rotation angle, using a preset quantum gate, to determine a second combined quantum state; and to determine a target quantum state based on the first combined quantum state and the second combined quantum state.

[0188] In certain embodiments, the processor is further configured to determine a rotation angle based on the first precise fraction estimate and the second precise fraction estimate; and to correlate the first sub-quantum state and the first quantum state of the second auxiliary bit based on the rotation angle, using a preset quantum gate, to determine a first combined quantum state. The processor is further configured to correlate the second sub-quantum state and the second quantum state of the second auxiliary bit based on the rotation angle, using a preset quantum gate, to determine a second combined quantum state; and to determine a target quantum state based on the first combined quantum state and the second combined quantum state.

[0189] Specifically, the rotation angle Refers to the valuation based on the first precision ratio and the second-highest proportion valuation The calculated quantum gate rotation parameters are used to control the superposition weight of the linear combination of unitary operators (LCU), and the formula is: .

[0190] The preset quantum gate refers to the Ry gate (Y-axis rotation gate) that realizes the linear combination. It is a single-qubit rotation gate that rotates the quantum state around the Y axis by a specified angle. Through the Ry gate operation, the first quantum state of the second auxiliary bit can be associated with the amplitude weight of the first sub-quantum state, and the second quantum state of the second auxiliary bit can be associated with the amplitude weight of the second sub-quantum state, thereby realizing the linear combination of unitary operators.

[0191] Correlation processing involves using a preset quantum gate (Ry gate) to entangle the quantum state of the second auxiliary bit with the amplitude weights of the grouped quantum state (the first or second sub-quantum state), so that the measurement result of the second auxiliary bit can indicate the successful preparation of the target state. In this way, through correlation processing, the abstract linear combination weights can be converted into measurable quantum state parameters.

[0192] The second auxiliary bit anc1 refers to the single-qubit auxiliary register introduced in the LCU algorithm, which is used to store the weight information of the linear combination and mark the successful preparation of the target state.

[0193] The first quantum state of the second auxiliary bit refers to the second auxiliary bit anc1 state.

[0194] The second quantum state of the second auxiliary bit refers to the second quantum state of the second auxiliary bit anc1. state.

[0195] The target quantum state refers to the complete normalized quantum state obtained by linearly combining the major item group and the minor item group according to their proportions through the LCU algorithm.

[0196] Continuing with the above example, see Figure 12 , Figure 12 The diagram for LCU algorithm implementation is shown below. Assuming the input data vector is {0.03, 0.03, 0.04, 0.04, 0.03, 0.03, 0.04, 1.0}, the first and second precision ratio estimates are 1.0084 and 0.0084 respectively. Then the rotation angle for The auxiliary bit 0 state indicates the target state. In this example, its proportion is 1 / 1.0871=0.92. Therefore, there is no need to amplify the amplitude. Each time the auxiliary bit anc1 is measured, the bb register below will collapse to the target state with a probability of 84.6%. If If amplitude amplification is not performed, the flag bit and the auxiliary bit anc1 of the LCU are measured simultaneously, and the measured value 10 indicates the successful preparation of the target state with a probability of 63.5%. The probability of obtaining the target state at least once after three consecutive measurements is 95.1%.

[0197] In this way, the computer device determines the rotation angle based on the first and second precise ratio estimates. Next, based on a preset quantum gate, the computer device correlates the first sub-quantum state and the first quantum state of the second auxiliary bit based on the rotation angle to determine a first combined quantum state. Furthermore, based on a preset quantum gate, the computer device correlates the second sub-quantum state and the second quantum state of the second auxiliary bit based on the rotation angle to determine a second combined quantum state. Finally, the computer device determines the target quantum state based on the first and second combined quantum states. This allows constant-level quantum gate operations to replace polynomial-level amplification, reducing computational complexity. Furthermore, the second auxiliary bit and rotation gate can be used to achieve precise weight control, improving the success rate of preparing the target quantum state.

[0198] See also Figure 13 In certain embodiments, the method further comprises:

[0199] 06: When the coarse proportion estimate is greater than or equal to the grouping threshold, the initial quantum state is amplified based on the third preset algorithm to determine the target quantum state.

[0200] In some embodiments, the confirmation module is further configured to, when the coarse proportion estimate is greater than or equal to the grouping threshold, perform quantum amplitude amplification processing on the initial quantum state based on a third preset algorithm to determine the target quantum state.

[0201] In some embodiments, the processor is further configured to, when the coarse proportion estimate is greater than or equal to the grouping threshold, perform quantum amplitude amplification processing on the initial quantum state based on a third preset algorithm to determine the target quantum state.

[0202] Specifically, when the data distribution is relatively uniform (the rough proportion estimate is greater than or equal to the grouping threshold), the quantum amplitude amplification algorithm is directly used to prepare the target quantum state.

[0203] In this way, when the coarse fraction estimate is greater than or equal to the grouping threshold, the computer device performs quantum amplitude amplification processing on the initial quantum state based on the third preset algorithm to determine the target quantum state. In this way, when the coarse fraction estimate is greater than or equal to the grouping threshold, the third preset algorithm is directly used to efficiently amplify the target state probability and determine the target quantum state.

[0204] The present application also provides a computer-readable storage medium containing a computer program. When the computer program is executed by one or more processors, the one or more processors execute the method of the present application.

[0205] It is understood that a computer program includes computer program code. The computer program code may be in source code form, object code form, executable file, or some intermediate form. Computer-readable storage media may include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media.

[0206] In the description of this specification, the descriptions with reference to the terms "particularly", "further", "particularly", "understandably", etc. are intended to mean that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms are not intended to refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0207] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0208] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A black box quantum state preparation method based on group processing, characterized in that: The method comprises: Determining a coarse fraction estimate of the target state based on the marker bit and the initial quantum state, wherein the initial quantum state is a superposition state obtained by processing the input data vector based on the black box, and the coarse fraction estimate is an estimate calculated by measuring the frequency of occurrence of the target collapsed state after the marker bit is entangled with the initial quantum state and combining it with a preset number of measurements; When the coarse proportion estimate is less than a grouping threshold, separating the initial quantum state according to the first auxiliary bit to determine a first intermediate quantum state, wherein the grouping threshold is determined based on the number of the input data vectors, and the first intermediate quantum state includes a target state portion and an orthogonal state portion, the target state portion includes a first sub-quantum state, and the orthogonal state portion includes a second sub-quantum state; Based on the first intermediate quantum state, a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state are determined to achieve the quantum state preparation, wherein the first precise fraction estimate is the fraction of the first sub-quantum state in the first intermediate quantum state, and the second precise fraction estimate is the fraction of the second sub-quantum state in the first intermediate quantum state.

2. The method according to claim 1, characterized in that The method further comprises: Based on the black box, the input data vector is loaded in a superposition state form to generate the initial quantum state, wherein the initial quantum state includes an address register and a data register.

3. The method according to claim 1, characterized in that Determining a rough proportion estimate of the target state based on the marker bit and the initial quantum state includes: Entangling the data register and the tag bit in the initial quantum state based on a controlled NOT gate; Measuring the marker bit based on a preset number of measurements to determine the frequency of the target collapsed state; The rough proportion estimate is determined according to the frequency of the target collapsed state and the preset number of measurements.

4. The method according to claim 1, wherein The initial quantum state includes a data register, and when the coarse ratio estimate is less than a grouping threshold, separating the initial quantum state according to the first auxiliary bit to determine a first intermediate quantum state includes: Based on a controlled NOT gate, performing association processing on the first auxiliary bit and the highest bit information of the data register; Based on a preset rule, the first auxiliary bit is used as a control bit, and a comparison process is performed on the data register to determine the first intermediate quantum state.

5. The method according to claim 4, characterized in that Determining, based on the first intermediate quantum state, a first precise fraction estimate corresponding to the first sub-quantum state and a second precise fraction estimate corresponding to the second sub-quantum state includes: Determine the first precision ratio estimate based on the target state portion based on a first preset algorithm; Based on the first preset algorithm, the second precision ratio estimation is determined according to the orthogonal state portion.

6. The method according to claim 5, characterized in that The determining the first precision ratio estimation based on the target state portion based on the first preset algorithm includes: Simplifying the first intermediate quantum state to determine a second intermediate quantum state; performing a transformation process on the second intermediate quantum state to determine a third intermediate quantum state in the form of a linear combination of eigenvectors; Based on the first preset algorithm, the first precise fraction estimate is determined according to the third intermediate quantum state.

7. The method according to claim 6, characterized in that The determining, based on the first preset algorithm and according to the third intermediate quantum state, the first precision ratio estimate includes: measuring the third intermediate quantum state to determine a phase in the third intermediate quantum state; determining an amplitude based on the phase in the third intermediate quantum state; The first precision ratio estimation is determined based on the amplitude.

8. The method according to claim 1, characterized in that The method further comprises: Based on a second preset algorithm, a target quantum state is determined according to the first precise ratio estimation and the second precise ratio estimation to achieve the quantum state preparation.

9. The method according to claim 8, characterized in that The determining of the target quantum state based on the first precise ratio estimate and the second precise ratio estimate based on the second preset algorithm includes: Determining a rotation angle according to the first precise ratio estimate and the second precise ratio estimate; Based on a preset quantum gate, and according to the rotation angle, performing correlation processing on the first sub-quantum state and the first quantum state of the second auxiliary bit to determine a first combined quantum state; Based on the preset quantum gate, and according to the rotation angle, performing correlation processing on the second sub-quantum state and the second quantum state of the second auxiliary bit to determine a second combined quantum state; The target quantum state is determined according to the first combined quantum state and the second combined quantum state.

10. The method according to claim 1, characterized in that The method further comprises: When the rough proportion estimate is greater than or equal to the grouping threshold, a quantum amplitude amplification process is performed on the initial quantum state based on a third preset algorithm to determine a target quantum state.

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