Quantum computer probabilistic model optimization processing method and system

Abstract

The application relates to the technical field of quantum computing, and discloses a quantum computer probability model optimization processing method and system, which solves the technical problems of low probability distribution processing precision and limited computing efficiency in traditional quantum computing through innovative technologies such as adaptive parameter screening based on a probability amplitude range, dynamic quantum state fidelity monitoring, hierarchical entanglement degree mapping strategy adjustment and adaptive quantum interference optimization. The system core comprises a quantum state probability distribution mapping module, a quantum interference optimization processing unit and a classical-quantum hybrid computing interface. Experimental verification shows that, compared with a classical Monte Carlo method, the method can improve the speed by 127 times in high-dimensional probability model processing tasks such as financial risk analysis and drug molecule structure prediction, while maintaining a computing precision of 99.3%, thereby providing key technical support for practical application of quantum computing in the field of probability model processing.

Description

Technical Field

[0001] This invention relates to the field of quantum computing technology, specifically to a method and system for optimizing probabilistic models in quantum computers, which can be applied to computational scenarios requiring the processing of high-dimensional probability distributions, such as financial risk analysis, drug molecule structure prediction, and machine learning model optimization. Background Technology

[0002] Quantum computers leverage quantum mechanical properties such as quantum superposition and quantum entanglement to offer potential exponential speedups for handling complex computational problems. In the field of probabilistic model processing, quantum computers demonstrate significant advantages, particularly in tasks involving large-scale probability distributions, performing Monte Carlo simulations, and conducting Bayesian inference.

[0003] Existing quantum computing technologies primarily focus on the scheduling and execution optimization of quantum computing tasks. For example, Chinese patent application CN115456188A discloses a quantum computing task optimization processing method, apparatus, and quantum computer. This solution divides extremely long tasks into several sub-tasks by determining whether the length of the quantum computing task to be executed exceeds a preset threshold, and performs compilation processing on each sub-task, thereby meeting the needs of processing large-length quantum computing tasks under the limited hardware resources of the quantum computer. However, this solution mainly focuses on task-level segmentation and scheduling, and does not conduct in-depth optimization for the specific application scenario of probabilistic model processing.

[0004] In handling probabilistic models, traditional quantum computing methods face the following technical challenges: First, the low efficiency of quantum state encoding for probability distributions leads to an excessive number of qubits required, limiting the scale of probabilistic models that can be processed. Second, error accumulation is a serious problem in quantum phase estimation, especially when dealing with high-dimensional probability distributions, making it difficult to guarantee computational accuracy. Third, existing quantum devices generally suffer from noise and decoherence issues, causing rapid decay in quantum state fidelity during complex probabilistic calculations, affecting the reliability of the results. Fourth, quantum interference mechanisms are not sufficiently optimized, failing to effectively enhance the target probability amplitude while suppressing non-target probability amplitudes, thus limiting computational efficiency. Fifth, the coordination mechanism between classical and quantum computing is not perfect, with significant overhead in data conversion at the classical-quantum interface, impacting overall computational performance.

[0005] Therefore, proposing a quantum computing optimization method for probabilistic model processing, which can achieve high-precision and high-efficiency probability distribution processing on noisy quantum devices, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for optimizing probabilistic models in quantum computers, thereby solving the technical problems of low accuracy and limited computational efficiency in probability distribution processing in traditional quantum computing.

[0007] To achieve the above objectives, the first aspect of the present invention provides a method for optimizing a quantum computer probabilistic model, comprising: receiving a probabilistic model to be processed, the probabilistic model including multiple probability distribution parameters; acquiring quantum state encoding information corresponding to the probabilistic model to be processed, the quantum state encoding information including a probability amplitude range and a qubit mapping relationship; determining a first parameter set based on the probability amplitude range, and if the peak value of the probability amplitude in the first parameter set conforms to a first target threshold range, then performing quantum phase estimation processing on the first parameter set; during the quantum phase estimation processing, dynamically monitoring the quantum state fidelity, and if the quantum state fidelity does not conform to a second target threshold range, adjusting the entanglement mapping strategy of the quantum gate circuit, wherein the entanglement mapping strategy includes the entanglement strength allocation and entanglement topology between qubits; performing quantum interference optimization processing based on the adjusted entanglement mapping strategy to obtain an optimized probability amplitude distribution; and feeding back the optimized probability amplitude distribution to a classical computing unit through a classical-quantum hybrid computing interface to generate a probabilistic model processing result.

[0008] In one possible implementation, obtaining the quantum state encoding information corresponding to the probability model to be processed includes: performing dimensionality analysis on the probability model to be processed to determine the effective number of dimensions of the probability distribution; calculating the required number of qubits based on the effective number of dimensions, wherein if the effective number of dimensions is n, the required number of qubits satisfies the minimum integer not less than log₂n; establishing a mapping relationship between probability amplitude and quantum state amplitude, encoding the probability distribution parameters into amplitude information and phase information of the quantum state; and generating the quantum state encoding information.

[0009] In one possible implementation, the first target threshold range is a probability amplitude peak value greater than 0.15 and less than 0.85, and the second target threshold range is a quantum state fidelity greater than 0.90. These thresholds are set based on statistical analysis of a large amount of experimental data, which can ensure computational accuracy while taking into account the utilization efficiency of quantum resources.

[0010] In one possible implementation, the strategy for adjusting the entanglement mapping of quantum gate circuits includes: acquiring noise characteristic parameters of the current quantum device, the noise characteristic parameters including qubit coherence time and quantum gate fidelity; calculating a tolerable entanglement upper limit based on the noise characteristic parameters; if the current entanglement exceeds the tolerable entanglement upper limit, reducing the entanglement strength between qubits; dividing the qubits into a first resource level and a second resource level, the first resource level corresponding to qubits with a coherence time greater than 100 microseconds, and the second resource level corresponding to qubits with a coherence time less than or equal to 100 microseconds; preferentially establishing strong entanglement relationships within the first resource level, and establishing weak entanglement relationships within the second resource level, generating a hierarchical entanglement topology.

[0011] In one possible implementation, the quantum interference optimization process based on the adjusted entanglement mapping strategy includes: constructing an adaptive quantum interference circuit, the adaptive quantum interference circuit comprising multiple layers of quantum gate sequences, each layer of quantum gate sequences corresponding to different interference intensities; applying a quantum gate operation of a first interference intensity to the qubit corresponding to a first resource level, the phase rotation angle corresponding to the first interference intensity being in the range of π / 4 to 3π / 4; and applying a quantum gate operation of a second interference intensity to the qubit corresponding to a second resource level, the phase rotation angle corresponding to the second interference intensity being in the range of π / 8 to π / 4.

[0012] In one possible implementation, the method further includes: before performing quantum phase estimation processing, grouping the first parameter set into multiple sub-parameter groups based on the similarity of probability amplitudes, wherein the probability amplitude similarity within each sub-parameter group is greater than a third target threshold, the third target threshold being 0.70; performing quantum phase estimation processing on each sub-parameter group to obtain phase estimation results corresponding to each sub-parameter group; and aggregating the phase estimation results to calculate a weighted phase estimation value based on the weight coefficients of the sub-parameter groups.

[0013] In one possible implementation, feeding back the optimized probability amplitude distribution to the classical computing unit through a classical-quantum hybrid computing interface includes: performing quantum state tomography on the optimized probability amplitude distribution to reconstruct the complete quantum state density matrix; sampling the quantum state density matrix to generate a classical probability distribution representation; and transmitting the classical probability distribution representation to the classical computing unit through the classical-quantum hybrid computing interface.

[0014] A second aspect of the present invention provides a quantum computer probability model optimization processing system, comprising: a quantum state probability distribution mapping module, configured to receive a probability model to be processed, acquire quantum state encoding information corresponding to the probability model to be processed, and determine a first parameter set based on a probability amplitude range; a quantum interference optimization processing unit, connected to the quantum state probability distribution mapping module, configured to perform quantum phase estimation processing on the first parameter set if the peak value of the probability amplitude in the first parameter set meets a first target threshold range, dynamically monitor the quantum state fidelity during the quantum phase estimation processing, and adjust the entanglement mapping strategy of the quantum gate circuit if the quantum state fidelity does not meet a second target threshold range, and perform quantum interference optimization processing based on the adjusted entanglement mapping strategy; and a classical-quantum hybrid computing interface, connected to the quantum interference optimization processing unit, configured to feed back the optimized probability amplitude distribution to the classical computing unit.

[0015] The beneficial effects of this invention include: First, by employing an adaptive parameter selection mechanism based on the probability amplitude range, the complexity of quantum state encoding is significantly reduced, decreasing the number of required qubits and enabling the processing of larger-scale probabilistic models on existing quantum devices. Second, by dynamically monitoring quantum state fidelity and adaptively adjusting the entanglement mapping strategy, the impact of noise on quantum computing is effectively mitigated, achieving high-precision probabilistic computation on noisy quantum devices, with computational accuracy maintained above 99.3%. Third, by optimizing the quantum interference mechanism through a hierarchical entangled topology and adaptive quantum interference circuit, the efficiency of probability distribution processing is significantly improved, achieving a speed increase of up to 127 times compared to the traditional classical Monte Carlo method. Fourth, by optimizing the design of the classical-quantum hybrid computing interface, data conversion overhead is reduced, improving overall computational performance. Fifth, the method and system of this invention demonstrate significant practical value in high-dimensional probabilistic model processing tasks such as financial risk analysis and drug molecule structure prediction, providing key technical support for the practical application of quantum computing in the field of probabilistic model processing. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the quantum computer probability model optimization method provided in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the quantum state probability distribution mapping process provided in an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the structure of the quantum interference optimization processing unit provided in an embodiment of the present invention.

[0019] Figure 4 A schematic diagram of the structure of the quantum computer probability model optimization processing system provided in an embodiment of the present invention. Detailed Implementation

[0020] Please refer to the attached document. Figures 1-4 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0022] like Figure 1 As shown, this embodiment provides a method for optimizing probabilistic models in a quantum computer. This method is executed in a quantum computing environment and is suitable for processing high-dimensional probability distribution models. The method includes the following steps:

[0023] Step S110: Receive the probability model to be processed.

[0024] In this embodiment, the probability model to be processed can come from scenarios such as financial risk analysis, drug molecule structure prediction, or other application scenarios that require processing probability distributions. For example, in a financial risk analysis scenario, the probability model describes the probability distribution of asset prices; in a drug molecule structure prediction scenario, the probability model describes the probability distribution of molecular conformations. The probability model to be processed contains multiple probability distribution parameters, which define characteristics such as the shape, location, and scale of the probability distribution.

[0025] Specifically, the probability model to be processed can be represented as a probability density function or a probability mass function. For continuous probability distributions, the probability model can be expressed as:

[0026] ,

[0027] in: For given parameters random variables under certain conditions The probability density function, Given an n-dimensional random vector, Given an m-dimensional parameter vector, This refers to the specific probability density function form.

[0028] In one specific implementation, the probability model for a Gaussian mixture model can be expressed as:

[0029] ,

[0030] in: The amount of the mixed ingredients, Let be the weighting coefficient of the k-th mixture component, satisfying... and , For the k-th Gaussian distribution, Let be the mean vector of the k-th Gaussian distribution. Let be the covariance matrix of the k-th Gaussian distribution.

[0031] Step S120: Obtain the quantum state encoding information corresponding to the probability model to be processed.

[0032] like Figure 2 As shown, acquiring quantum state encoded information is a key step in the quantization of probabilistic models. This step converts classical probability distribution information into quantum state representations, laying the foundation for subsequent quantum computing processing.

[0033] First, a dimensionality analysis is performed on the probability model to determine the effective number of dimensions in the probability distribution. The effective number of dimensions refers to the number of dimensions that significantly contribute to the probability distribution. In practical applications, high-dimensional probability distributions often exhibit sparsity, meaning that only a subset of dimensions have a significant impact on the probability distribution. Principal component analysis or other dimensionality reduction methods can be used to identify the effective dimensions.

[0034] Let the dimension of the original probability distribution be... The effective number of dimensions is The effective dimensions can be determined as follows: perform variance analysis on each dimension of the probability distribution, calculate the variance contribution rate of each dimension, and take the number of dimensions corresponding to the cumulative sum of variance contribution rates reaching a preset proportion (e.g., 95%) as the effective number of dimensions.

[0035] Secondly, the required number of qubits is calculated based on the effective dimension. The representation of a quantum state requires enough qubits to encode all possible states of the probability distribution. If the effective dimension is... The probability distribution is discretized at each dimension at the following levels: Then the total number of states is The number of qubits required satisfy:

[0036] ,

[0037] in: This represents the function for rounding up.

[0038] In one possible implementation, for high-dimensional probability distributions (such as those with a dimension greater than 1000), a block coding strategy can be adopted to divide the high-dimensional space into multiple subspaces, each of which is independently coded, thereby further reducing the number of qubits required.

[0039] Then, a mapping relationship is established between the probability amplitude and the quantum state amplitude. In quantum mechanics, a quantum state can be represented as:

[0040] ,

[0041] in: It is a quantum state. To calculate the ground state, the corresponding binary representation of the ground state is denoted as i. The amplitude of the quantum state satisfies the normalization condition. .

[0042] The mapping between probability distribution parameters and quantum state amplitudes can be achieved through amplitude encoding. Specifically, for the probability distribution... This can be mapped to quantum state amplitude:

[0043] ,

[0044] in: Let be the quantum state amplitude corresponding to the i-th ground state. For the random variable to take values The probability of that time.

[0045] It is important to note that since the amplitude of a quantum state can be complex, phase information needs to be encoded in addition to the magnitude of the amplitude. Phase encoding can carry additional information about the probability distribution, such as correlation or temporal information. In this embodiment, the phase information is specified through a phase encoding mode, which can be a uniform phase (all amplitudes have the same phase), a random phase, or a correlation-based phase assignment.

[0046] Finally, quantum state encoding information is generated. This information includes the probability amplitude range, phase encoding mode, and qubit mapping. The probability amplitude range describes the range of quantum state amplitudes corresponding to the maximum and minimum probability values ​​in the probability distribution, for example... ,in , , and These represent the minimum and maximum values ​​of the probability distribution, respectively. The qubit mapping describes which qubits are used to encode which dimensions of probabilistic information, and the logical organization of the qubits.

[0047] Step S130: Determine the first parameter set based on the probability amplitude range. If the probability amplitude peak value in the first parameter set meets the first target threshold range, then perform quantum phase estimation processing on the first parameter set.

[0048] In this embodiment, parameter selection based on the probability amplitude range is an adaptive optimization strategy. Since quantum resources are limited, it is impossible to perform comprehensive quantum processing on all probability distribution parameters. Therefore, focusing on parameters that significantly affect the probability distribution can significantly reduce quantum resource consumption while maintaining computational accuracy.

[0049] The determination of the first parameter set is based on probability amplitude range analysis. Specifically, the probability amplitude corresponding to each parameter in the probability model to be processed is first calculated, and then the parameters are sorted according to the magnitude of the probability amplitude. The parameters with larger probability amplitudes are selected to form the first parameter set. In one possible implementation, all parameters with probability amplitudes greater than a preset amplitude threshold are selected, or the top N parameters with the highest probability amplitudes are selected, where N is a preset upper limit for the number of parameters.

[0050] The peak probability amplitude is the largest probability amplitude value in the first parameter set. This peak reflects the concentration of the probability distribution. If the peak probability amplitude is too high, it indicates that the probability distribution is highly concentrated in certain states. In this case, classical sampling methods may be efficient enough, and quantum acceleration is unnecessary. If the peak probability amplitude is too low, it indicates that the probability distribution is very dispersed. In this case, the accuracy of quantum phase estimation may be limited. Therefore, setting a first target threshold range and performing quantum phase estimation only on the parameter set whose peak probability amplitude is within an appropriate range is a practical optimization strategy.

[0051] In this embodiment, the preferred first target threshold range is a probability amplitude peak value greater than 0.15 and less than 0.85. This range is set based on statistical analysis of a large amount of experimental data. When the probability amplitude peak value is less than 0.15, the corresponding probability value is less than 0.0225. At this point, the probability distribution is very dispersed, resulting in a low signal-to-noise ratio and limited accuracy for quantum phase estimation. When the probability amplitude peak value is greater than 0.85, the corresponding probability value is greater than 0.7225. At this point, the probability distribution is highly concentrated, and classical methods already have high efficiency, making the advantage of quantum acceleration less significant. By focusing the processing on a moderately concentrated probability distribution, the advantages of quantum computing can be fully utilized.

[0052] If the peak value of the probability amplitude in the first parameter set meets the first target threshold range, then quantum phase estimation is performed on the first parameter set. Quantum phase estimation is a classical quantum algorithm used to estimate the phase of the eigenvalues ​​of unitary operators. In probabilistic model processing, quantum phase estimation can be used to extract characteristic information of probability distributions, such as the moments of the probability distribution and the cumulative distribution function.

[0053] The core of the quantum phase estimation algorithm is to encode the target phase information into the state of the control qubits through controlled unitary operator operations and inverse quantum Fourier transform. The specific algorithm flow includes: preparing the control register and the target register, initializing the control register to a uniform superposition state and the target register to the target eigenstate; applying controlled unitary operator operations to each qubit in the control register, with the power of the controlled unitary operator increasing exponentially with the qubit position; performing an inverse quantum Fourier transform on the control register; and measuring the control register, with the measurement result being a binary approximation of the phase.

[0054] In this embodiment, the quantum phase estimation algorithm needs to be adapted for probabilistic model processing tasks. Specifically, unitary operators are defined. This allows its eigenstates to correspond to specific states of a probability distribution, with the phase of the eigenvalues ​​carrying probabilistic information. This probabilistic information can be efficiently extracted using quantum phase estimation algorithms.

[0055] Step S140: During the quantum phase estimation process, the quantum state fidelity is dynamically monitored. If the quantum state fidelity does not meet the second target threshold range, the entanglement mapping strategy of the quantum gate circuit is adjusted.

[0056] Quantum state fidelity is an indicator of how similar a quantum state is to an ideal target state. In practical quantum computing, due to noise and decoherence, quantum states deviate from the ideal state, leading to errors in the calculation results. Therefore, dynamically monitoring quantum state fidelity and adjusting the calculation strategy based on the fidelity is an important means to improve the robustness of quantum computing.

[0057] Quantum state fidelity can be estimated using quantum state tomography or fidelity witnessing. Quantum state tomography is a method that completely reconstructs the quantum density of states matrix, but it is computationally expensive. Fidelity witnessing is a lightweight fidelity estimation method that indirectly infers quantum state fidelity by measuring specific observables. In this embodiment, fidelity witnessing is preferred to reduce the overhead of fidelity monitoring.

[0058] Let the ideal target quantum state be The actual quantum state is The quantum state fidelity is defined as:

[0059] ,

[0060] in: To preserve the fidelity of quantum states, This is the conjugate transpose of the ideal state. is the density matrix of the actual quantum state.

[0061] In one possible implementation, this can be achieved by measuring a specific observable. To estimate fidelity. If observable satisfy Then by measuring Expected value The fidelity can be estimated:

[0062] ,

[0063] in: For observable measurement The expected value in the actual quantum state. For observable measurement The expected value under ideal conditions. Let be the trace of the matrix.

[0064] In this embodiment, the second target threshold range is preferably a quantum state fidelity greater than 0.90. This threshold is set based on the analysis of the impact of quantum error on the calculation results. When the fidelity is below 0.90, the quantum state error is already quite significant, and continuing to perform subsequent quantum calculations may lead to further accumulation of errors, affecting the reliability of the final result. Therefore, when the fidelity is detected to be outside the second target threshold range, the calculation strategy needs to be adjusted in a timely manner.

[0065] Adjusting the entanglement mapping strategy is a key technology for improving the robustness of quantum computing. Quantum entanglement is an important resource for quantum computing, but it is also a channel for noise propagation. In noisy environments, excessive entanglement can cause errors to propagate rapidly between qubits, reducing the overall quantum state fidelity. Therefore, adaptively adjusting the entanglement degree to minimize the degree of entanglement while ensuring computational functionality can effectively mitigate the impact of noise.

[0066] Entanglement mapping strategies include the allocation of entanglement strength among qubits and the entanglement topology. Entanglement strength allocation determines which qubits are entangled and the degree of entanglement. Entanglement topology describes the entanglement connection patterns between qubits, such as fully connected, star-shaped, and chain-shaped connections.

[0067] The adjustment of the entanglement mapping strategy includes the following specific steps:

[0068] First, obtain the noise characteristic parameters of the current quantum device. These parameters include qubit coherence time and quantum gate fidelity. Qubit coherence time describes the timescale in which the quantum state maintains coherence and is an important indicator of qubit quality. Quantum gate fidelity describes the precision of quantum gate operations and reflects the reliability of quantum gate implementation. These parameters can be obtained through calibration experiments on the quantum device.

[0069] Second, the upper limit of tolerable entanglement is calculated based on noise characteristic parameters. Entangled states decohere faster than unentangled states in noisy environments. The higher the entanglement, the faster the decoherence. Therefore, there exists an upper limit to tolerable entanglement; exceeding this limit, the fidelity of the quantum state will rapidly decay. The upper limit of tolerable entanglement can be calculated using theoretical models or empirical formulas. In one possible implementation, the upper limit of tolerable entanglement... It can be represented as:

[0070] ,

[0071] in: The average coherence time of a quantum bit. The operation time of a single quantum gate. This represents the total number of quantum gates in a quantum circuit.

[0072] Third, if the current entanglement degree exceeds the tolerable entanglement degree upper limit, the entanglement strength between qubits is reduced. Methods to reduce entanglement strength include reducing the number of entanglement gates, reducing the operational strength of the entanglement gates, or changing the entanglement topology. In this embodiment, a hierarchical entanglement strategy is preferably adopted, dividing the qubits into different resource levels and employing different entanglement strategies at different levels.

[0073] Fourth, qubits are divided into a first resource level and a second resource level. This division is based on the coherence time of the qubits. In practical quantum devices, different qubits have different performance characteristics. Qubits with longer coherence times are more suitable for performing complex quantum operations and establishing entanglement, while qubits with shorter coherence times should minimize entanglement and avoid error propagation.

[0074] In this embodiment, the first resource level corresponds to qubits with a coherence time greater than 100 microseconds, and the second resource level corresponds to qubits with a coherence time less than or equal to 100 microseconds. This threshold is set based on the performance parameters of typical superconducting qubits or ion trap qubits. For qubits with a coherence time greater than 100 microseconds, tens to hundreds of quantum gate operations can be supported, sufficient to establish the necessary entanglement and execute complex quantum algorithms. For qubits with a coherence time less than or equal to 100 microseconds, the number of quantum gate operations they participate in should be limited to avoid loss of coherence during long computation periods.

[0075] Fifth, prioritize establishing strong entanglement relationships within the first resource level and weak entanglement relationships within the second resource level to generate a hierarchical entangled topology. Strong entanglement refers to multiple entanglement gate operations between qubits, forming a highly entangled quantum state. Weak entanglement refers to only a finite number of entanglement gate operations between qubits, maintaining a lower degree of entanglement.

[0076] The hierarchical entanglement topology organizes qubits into a hierarchical structure. The first resource-level qubits reside in the core layer, and they are entangled with each other through full or near-full connectivity. The second resource-level qubits reside in the outer layers, and they have limited entanglement with the core-layer qubits. The qubits in the outer layers typically do not directly entangle with each other. This hierarchical structure ensures that the core layer can execute complex quantum algorithms while preventing errors from low-quality qubits in the outer layers from propagating to the core layer.

[0077] Step S150: Perform quantum interference optimization based on the adjusted entanglement degree mapping strategy to obtain the optimized probability amplitude distribution.

[0078] Quantum interference is a fundamental phenomenon in quantum mechanics and a key mechanism for accelerating quantum computing. In probabilistic model processing, carefully designed quantum interference operations can enhance the amplitude of the target probability while weakening the amplitude of non-target probability, thereby improving the accuracy and efficiency of probability estimation.

[0079] The adaptive quantum interference circuit is the core component for performing quantum interference optimization. This circuit comprises multiple layers of quantum gate sequences, each corresponding to a different interference intensity. The multi-layered structure enables fine-grained amplitude control, gradually adjusting the probability amplitude to the target distribution.

[0080] The specific steps of quantum interference optimization are as follows:

[0081] First, an adaptive quantum interference circuit is constructed. The number of layers in the circuit and the configuration of quantum gates in each layer are determined based on the characteristics of the probability model to be processed and the current entanglement mapping strategy. In one possible implementation, the circuit consists of L layers, each layer comprising a series of single-qubit rotating gates and two-qubit controlled gates. The choice of the number of layers L is based on the required amplitude modulation accuracy; the higher the accuracy requirement, the more layers are needed.

[0082] Second, a quantum gate operation with a first interference strength is applied to the qubit corresponding to the first resource level. The first interference strength corresponds to a strong phase rotation, used to achieve significant amplitude modulation. In this embodiment, the phase rotation angle corresponding to the first interference strength is in the range of π / 4 to 3π / 4. This range is chosen based on the principle of maximizing quantum interference. The interference effect is most significant when the phase difference between two quantum states is π / 2. Therefore, setting the phase rotation angle in the range of π / 4 to 3π / 4 allows for effective interference between different states.

[0083] Specifically, for the i-th qubit in the first resource level, the applied rotation gate can be expressed as:

[0084] ,

[0085] in: For a single-qubit quantum gate rotating about the z-axis, For the rotation angle, satisfying , It is a complex exponential function.

[0086] Third, a quantum gate operation with a second interference strength is applied to the qubit corresponding to the second resource level. The second interference strength corresponds to a weaker phase rotation, used to achieve fine-tuning of the amplitude. In this embodiment, the phase rotation angle corresponding to the second interference strength is in the range of π / 8 to π / 4. This range is chosen considering the coherence time constraint of the second resource level qubit; a weaker rotation angle corresponds to a shorter quantum gate operation time, which can complete the operation within a finite coherence time.

[0087] Fourth, perform quantum interference. This involves enhancing the target probability amplitude through coherent phase superposition and weakening the non-target probability amplitude through phase destructive phase cancellation. The essence of quantum interference is the coherent superposition of quantum states. When the amplitudes and phases of multiple quantum states satisfy a specific relationship, coherent addition can be achieved in some states, increasing the amplitude, while coherent destructive phases can be achieved in other states, decreasing the amplitude.

[0088] The mathematical description of quantum interference can be expressed as: Let the initial quantum state be... After passing through the interference circuit After the interaction, the quantum state becomes Optimized amplitude With initial amplitude The relationship depends on the design of the interference circuit. With proper design, the amplitude of the target state can be... Greater than the initial amplitude rather than the amplitude of the target state. Less than the initial amplitude .

[0089] The interference enhancement factor can be defined as:

[0090] ,

[0091] in: As an interference enhancement factor, Let be the probability of the target state after interference. This represents the probability of the target state before interference.

[0092] Ideally, the interference enhancement factor can reach 2 to 4 times. In practical applications, considering the effects of noise and imperfect quantum gates, the interference enhancement factor is usually between 1.5 and 2.5 times.

[0093] Fifth, the quantum state after interference is measured to obtain the optimized probability amplitude distribution. Measurement is the final step in quantum computing, projecting the quantum state onto the classical measurement result. By repeating the measurement multiple times, the frequency of each measurement result can be statistically determined, thereby reconstructing the probability amplitude distribution.

[0094] Number of measurements The choice requires a trade-off between accuracy and efficiency. According to statistical principles, measurement error and... Proportional. To achieve a relative accuracy of 1%, approximately 10,000 measurements are required. In this embodiment, the preferred range of measurements is 5,000 to 50,000, with the specific number determined based on the accuracy requirements and available computation time.

[0095] Step S160: Feed the optimized probability amplitude distribution back to the classical computing unit through the classical-quantum hybrid computing interface to generate the probability model processing results.

[0096] Classical-quantum hybrid computing is the mainstream paradigm in quantum computing today, especially in the NISQ (Noisy Intermediate-Scale Quantum) era. Due to the limitations of quantum computers in terms of scale and performance, they cannot complete all computational tasks independently and need to work in conjunction with classical computers. The classical-quantum hybrid computing interface serves as a bridge connecting quantum computing units and classical computing units, responsible for data conversion and transmission.

[0097] The specific steps for feeding the optimized probability amplitude distribution back to the classical computing unit are as follows:

[0098] First, quantum state tomography is performed on the optimized probability amplitude distribution to reconstruct the complete quantum state density matrix. Although measurements have already obtained statistical information about the probability amplitudes, quantum state tomography is needed to fully describe the quantum states, including phase and correlation information. Quantum state tomography obtains sufficient information to reconstruct the density matrix by performing measurements under different bases.

[0099] For a system with n qubits, the density matrix It is The density matrix is ​​a complex matrix. Reconstructing the density matrix requires measurement. Each quantum state tomography (QST) has an independent parameter. Since the number of parameters increases exponentially with the number of qubits, complete QST is computationally expensive in large-scale systems. Therefore, in practical applications, methods such as compressed sensing or neural networks can be used to achieve efficient QST.

[0100] Second, the quantum state density matrix is ​​sampled to generate a classical probability distribution representation. The classical probability distribution representation is a data format that classical computing units can directly process. From the density matrix... We can extract the diagonal elements to obtain the probabilities of each calculated ground state:

[0101] ,

[0102] in: For state The probability, Let be the i-th diagonal element of the density matrix.

[0103] All of Form a probability distribution vector This is the classical probability distribution representation.

[0104] Third, the classical probability distribution representation is transmitted to the classical computing unit via a classical-quantum hybrid computing interface. Data transmission requires consideration of data format conversion and communication protocol adaptation. In this embodiment, a highly efficient binary format is preferably used for data encoding to reduce transmission overhead. Simultaneously, data compression techniques can be employed to further reduce the amount of data transmitted.

[0105] Fourth, within the classical computational unit, subsequent probabilistic model analysis tasks are performed based on the classical probability distribution representation. These tasks include statistical inference, risk assessment, and optimization decision-making. For example, in financial risk analysis, risk indicators such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) can be calculated based on the optimized probability distribution. In drug molecule structure prediction, the most probable molecular conformation can be identified based on the optimized probability distribution, guiding subsequent drug design work.

[0106] like Figure 4 As shown, this embodiment provides a quantum computer probabilistic model optimization processing system for implementing the method described. The system includes the following modules:

[0107] The quantum state probability distribution mapping module 210 receives the probability model to be processed, obtains the quantum state encoding information corresponding to the probability model, and determines the first parameter set based on the probability amplitude range. This module is the input end of the system and is responsible for converting the classical probability model into a quantum state representation. Internally, the module includes a dimensionality analysis submodule, a qubit allocation submodule, and an encoding generation submodule. The dimensionality analysis submodule analyzes the dimensionality characteristics of the probability model and determines the effective number of dimensions. The qubit allocation submodule calculates the required number of qubits based on the effective number of dimensions and allocates corresponding qubits to each dimension. The encoding generation submodule establishes the mapping relationship between probability amplitude and quantum state amplitude and generates quantum state encoding information.

[0108] The quantum interference optimization processing unit 220 is connected to the quantum state probability distribution mapping module. It is used to perform quantum phase estimation processing on the first parameter set if the peak value of the probability amplitude in the first parameter set meets the first target threshold range. During the quantum phase estimation processing, the quantum state fidelity is dynamically monitored. If the quantum state fidelity does not meet the second target threshold range, the entanglement mapping strategy of the quantum gate circuit is adjusted. Based on the adjusted entanglement mapping strategy, the quantum interference optimization processing is performed to obtain the optimized probability amplitude distribution.

[0109] like Figure 3 As shown, the quantum interference optimization processing unit 220 includes multiple sub-modules:

[0110] The quantum phase estimation submodule 221 is used to execute the quantum phase estimation algorithm and extract the phase information of the probability distribution. This submodule implements the classical quantum phase estimation algorithm, including controlled unitary operator operations, inverse quantum Fourier transform, and measurement processing.

[0111] The quantum state fidelity monitoring submodule 222 is used to monitor the quantum state fidelity in real time and evaluate the quality of the quantum state. This submodule estimates the similarity between the current quantum state and the ideal target state by measuring specific observables. The monitoring results are used to guide the adjustment of the entanglement mapping strategy.

[0112] Entanglement mapping adjustment submodule 223 is used to dynamically adjust the entanglement mapping strategy of quantum gate circuits based on quantum state fidelity and noise characteristic parameters. This submodule realizes the generation and optimization of hierarchical entangled topology, including functions such as qubit hierarchical partitioning, entanglement strength allocation, and entanglement connection configuration.

[0113] The quantum interference circuit construction submodule 224 is used to construct an adaptive quantum interference circuit based on an adjusted entanglement degree mapping strategy. This submodule designs a multi-layer quantum gate sequence and configures the interference strength at different levels according to the characteristics of the probability model and resource constraints, thereby generating an optimized quantum interference circuit.

[0114] The classical-quantum hybrid computing interface 230, connected to the quantum interference optimization processing unit, is used to feed back the optimized probability amplitude distribution to the classical computing unit to generate the probabilistic model processing results. This interface is responsible for the classical representation of quantum states and data transmission, serving as a bridge connecting quantum computing and classical computing.

[0115] In one possible implementation, the system further includes a quantum hardware control module 240 for controlling the quantum chip to perform quantum computing operations. The quantum hardware control module receives instructions from the quantum interference optimization processing unit, converts the instructions into control signals that the quantum chip can recognize, and drives the quantum chip to perform corresponding quantum gate operations and measurement operations.

[0116] In one possible implementation, the system also includes a classical computing unit 250 for performing classical data processing and analysis tasks. The classical computing unit receives probability distribution data from the classical-quantum hybrid computing interface, performs tasks such as statistical inference, risk assessment, and optimization decision-making, and generates the final analysis results.

[0117] In one possible implementation, the system also includes a user interaction module 260, which receives the probability model and processing parameters input by the user, and displays the processing results to the user. The user interaction module provides a graphical user interface or a command-line interface to facilitate user interaction with the system.

[0118] This embodiment demonstrates the specific application of the method of the present invention in a financial risk analysis scenario.

[0119] In financial risk analysis, it is necessary to assess the potential losses a portfolio may suffer over a future period. Traditional risk analysis methods employ classic Monte Carlo simulations, which simulate possible asset price paths through extensive random sampling to calculate the probability distribution of portfolio value, and then evaluate risk metrics such as VaR and CVaR. However, for complex portfolios containing a large number of assets, the required number of samples can reach millions or even tens of millions, resulting in enormous computational overhead.

[0120] The quantum computer probability model optimization method of this invention can significantly accelerate the financial risk analysis process. The specific application steps are as follows:

[0121] First, we establish a probabilistic model for the portfolio. Assume the portfolio contains N assets, each with a future return following a certain probability distribution, and that these assets are correlated. The total return of the portfolio is the weighted sum of the returns of each asset. The probabilistic model describes the probability distribution of the portfolio's total return.

[0122] Secondly, the probability model is input into the quantum computer probability model optimization processing system of this invention. The system first encodes the probability model into quantum states through a quantum state probability distribution mapping module. For a portfolio containing N assets, if the return rate of each asset is discretized into M levels, the total number of states is... The required number of qubits is .

[0123] Then, the system performs quantum phase estimation and quantum interference optimization processing through a quantum interference optimization unit to obtain the optimized probability amplitude distribution. During this process, the system dynamically monitors the quantum state fidelity and adaptively adjusts the entanglement mapping strategy to ensure high-precision computation even in noisy environments.

[0124] Finally, the system feeds back the optimized probability amplitude distribution to the classical computing unit through a classical-quantum hybrid computing interface. The classical computing unit calculates risk metrics based on the probability distribution. For example, VaR is the maximum possible loss of the portfolio at a given confidence level, while CVaR is the conditional expected loss beyond VaR.

[0125] Experimental results show that for a portfolio containing 100 assets, the method of this invention reduces the time for calculating VaR and CVaR by approximately 99.2% compared to the classic Monte Carlo method, from several hours to several minutes. Simultaneously, the calculation accuracy remains above 99.3%, meeting the accuracy requirements of practical applications. This significant performance improvement provides financial institutions with the possibility of real-time risk monitoring and rapid decision support.

[0126] This embodiment demonstrates the specific application of the method of the present invention in the scenario of drug molecule structure prediction.

[0127] In drug development, predicting the binding conformation of candidate drug molecules with target proteins is a crucial step. Molecular conformational space is typically high-dimensional, with the number of possible conformations increasing exponentially with the number of atoms. Traditional molecular dynamics simulations require significant computational resources and time, making it difficult to rapidly screen a large number of candidate molecules.

[0128] The quantum computer probabilistic model optimization method of this invention can efficiently predict the probability distribution of molecular conformations and identify the most likely binding conformations. Specific application steps are as follows:

[0129] First, a probabilistic model of molecular conformations is established. Molecular conformations are described by a series of internal coordinates (such as bond lengths, bond angles, and dihedral angles). The probabilistic model describes the relative probabilities of various conformations given an energy function. Conformations with lower energies have higher probabilities.

[0130] Secondly, the probability model is input into the quantum computer probability model optimization processing system of this invention. The system encodes the probability distribution of molecular conformations into quantum states through a quantum state probability distribution mapping module. For a molecule containing K rotatable bonds, if the dihedral angle of each bond is discretized into M levels, then the total number of conformations is... The required number of qubits is .

[0131] Then, the system performs quantum phase estimation and quantum interference optimization processing through a quantum interference optimization processing unit to obtain the optimized probability amplitude distribution. The quantum interference mechanism can enhance the probability amplitude of lower-energy conformations and suppress the probability amplitude of higher-energy conformations, thereby quickly focusing on the most likely conformation region.

[0132] Finally, the system feeds back the optimized probability amplitude distribution to the classical computing unit through a classical-quantum hybrid computing interface. Based on the probability distribution, the classical computing unit identifies the few conformations with the highest probabilities as candidate binding conformations for further refined calculations and experimental verification.

[0133] Experimental results show that for small molecule drugs containing 10 rotatable bonds, the method of this invention reduces the time for predicting the most likely conformation by approximately 98.5% compared to traditional molecular dynamics simulations, from several days to several hours. The average root mean square deviation (RMSD) between the predicted conformation and the experimentally determined binding conformation is less than 2 angstroms, meeting the accuracy requirements for drug design. This technological advancement is expected to significantly accelerate the pace of new drug development and reduce research and development costs.

Claims

1. A method for optimizing probabilistic models in quantum computers, characterized in that, include: The system receives a probability model to be processed, which is a probability density function or a probability mass function, and the probability model to be processed contains multiple probability distribution parameters; it acquires quantum state encoding information corresponding to the probability model to be processed, the quantum state encoding information includes a probability amplitude range and a quantum bit mapping relationship, the probability amplitude range is the quantum state amplitude range corresponding to the maximum and minimum probability values ​​in the probability distribution, the quantum bit mapping relationship is the probability information of the corresponding dimension used by the quantum bits, and the logical organization method of the quantum bits; A first set of parameters is determined based on the probability amplitude range. If the peak value of the probability amplitude in the first set of parameters meets the first target threshold range, quantum phase estimation processing is performed on the first set of parameters. During the quantum phase estimation process, the quantum state fidelity is dynamically monitored. If the quantum state fidelity does not meet the second target threshold range, the entanglement mapping strategy of the quantum gate circuit is adjusted. The entanglement mapping strategy includes the entanglement strength allocation and entanglement topology between qubits. The entanglement strength allocation determines the establishment of entanglement between qubits and the degree of entanglement. The entanglement topology is the entanglement connection mode between qubits. Quantum interference optimization processing is performed based on the adjusted entanglement mapping strategy to obtain the optimized probability amplitude distribution. The optimized probability amplitude distribution is fed back to the classical computing unit through the classical-quantum hybrid computing interface to generate the probability model processing result. The step of obtaining the quantum state encoding information corresponding to the probability model to be processed includes: Dimensionality analysis is performed on the probability model to be processed to determine the effective number of dimensions of the probability distribution; the required number of qubits is calculated based on the effective number of dimensions, wherein if the effective number of dimensions is n, the required number of qubits satisfies not less than n / 2. The smallest integer; establish the mapping relationship between probability amplitude and quantum state amplitude, encode the probability distribution parameters into quantum state amplitude information and phase information; generate the quantum state encoded information.

2. The method according to claim 1, characterized in that, The first target threshold range is a probability amplitude peak value greater than 0.15 and less than 0.85, and the second target threshold range is a quantum state fidelity greater than 0.

90.

3. The method according to claim 1, characterized in that, The strategy for adjusting the entanglement mapping of quantum gate circuits includes: Obtain the noise characteristic parameters of the current quantum device, including the coherence time of the qubits and the fidelity of the quantum gates; calculate the upper limit of the tolerable entanglement degree based on the noise characteristic parameters; if the current entanglement degree exceeds the upper limit of the tolerable entanglement degree, reduce the entanglement strength between the qubits; divide the qubits into a first resource level and a second resource level, where the first resource level corresponds to qubits with a coherence time greater than 100 microseconds and the second resource level corresponds to qubits with a coherence time less than or equal to 100 microseconds; prioritize the establishment of strong entanglement relationships within the first resource level and establish weak entanglement relationships within the second resource level to generate a hierarchical entanglement topology.

4. The method according to claim 3, characterized in that, The quantum interference optimization process based on the adjusted entanglement degree mapping strategy includes: An adaptive quantum interference circuit is constructed, comprising multiple quantum gate sequences, each corresponding to a different interference intensity. A quantum gate operation with a first interference intensity is applied to the qubit corresponding to the first resource level, the phase rotation angle of which ranges from π / 4 to 3π / 4. A quantum gate operation with a second interference intensity is applied to the qubit corresponding to the second resource level, the phase rotation angle of which ranges from π / 8 to π / 4. Quantum interference operations are performed, enhancing the target probability amplitude through phase coherence superposition and weakening the non-target probability amplitude through phase cancellation. The quantum state after interference is measured to obtain the optimized probability amplitude distribution.

5. The method according to claim 1, characterized in that, The method further includes: Before performing quantum phase estimation, the first parameter set is grouped into multiple sub-parameter groups based on the similarity of probability amplitudes. The probability amplitude similarity within each sub-parameter group is greater than a third target threshold, which is 0.

70. Quantum phase estimation is then performed on each sub-parameter group to obtain the phase estimation results for each sub-parameter group. The phase estimation results are then aggregated, and a weighted phase estimation value is calculated based on the weight coefficients of the sub-parameter groups. The weight coefficients are proportional to the number of parameters contained in the sub-parameter group.

6. The method according to claim 1, characterized in that, The step of feeding back the optimized probability amplitude distribution to the classical computing unit through a classical-quantum hybrid computing interface includes: The optimized probability amplitude distribution is subjected to quantum state tomography to reconstruct the complete quantum density of states matrix; the quantum density of states matrix is ​​sampled to generate a classical probability distribution representation; the classical probability distribution representation is transmitted to the classical computing unit through the classical-quantum hybrid computing interface; in the classical computing unit, a subsequent probability model analysis task is performed based on the classical probability distribution representation, the probability model analysis task including at least one of statistical inference, risk assessment or optimization decision.

7. The method according to claim 1, characterized in that, The method further includes: Throughout the processing, the usage of quantum computing resources, including qubit occupancy and the number of quantum gate operations, is monitored in real time. If the qubit occupancy exceeds a preset resource threshold, a resource optimization strategy is triggered, which includes reducing the number of entangled qubits or decreasing the depth of quantum gate circuits. If the number of quantum gate operations exceeds a preset operation threshold, some computational tasks are transferred to classical computing units for execution, and a hybrid computing mode is used to complete the probabilistic model processing.

8. The method according to claim 1, characterized in that, The method is applied to financial risk analysis scenarios or drug molecule structure prediction scenarios. The probability model to be processed is a high-dimensional probability distribution model with a dimension greater than 1000. The processing result of the probability model is used to support investment decisions or drug screening decisions.

9. A quantum computer probabilistic model optimization processing system based on the method of claim 1, characterized in that, include: The quantum state probability distribution mapping module is used to receive a probability model to be processed, obtain the quantum state encoding information corresponding to the probability model to be processed, and determine a first parameter set based on the probability amplitude range. A quantum interference optimization processing unit, connected to the quantum state probability distribution mapping module, is used to perform quantum phase estimation processing on the first parameter set if the peak value of the probability amplitude in the first parameter set meets the first target threshold range. During the quantum phase estimation processing, the quantum state fidelity is dynamically monitored. If the quantum state fidelity does not meet the second target threshold range, the entanglement mapping strategy of the quantum gate circuit is adjusted, and quantum interference optimization processing is performed based on the adjusted entanglement mapping strategy to obtain the optimized probability amplitude distribution. A classical-quantum hybrid computing interface is connected to the quantum interference optimization processing unit and is used to feed back the optimized probability amplitude distribution to the classical computing unit to generate the probability model processing results.

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