Quantum computing and parallel data processing system and method

Abstract

The application provides a kind of quantum computation and intelligent calculation parallel data processing system and method, wherein the system includes: data benchmark computing processing module, for determining the benchmark calculation processing of historical data to obtain deterministic calculation result;Data quantum state vector model construction module, for determining the quantum state amplitude of historical data and uncertain evolution data, and constructs quantum state vector model;Quantum evolution operation module, for quantum evolution operation to quantum state vector model to obtain uncertain prediction result;Comprehensive processing result generation module, for fusing deterministic calculation result and uncertain prediction result, and generating comprehensive processing result.The system adopts the hybrid architecture of benchmark calculation+quantum evolution operation, breaks through the exponential algorithm power bottleneck when traditional computing processing high-dimensional data, solves the real-time processing problem of complex data in high-dimensional nonlinear scene, and considers the compliance of deterministic processing and the accuracy of uncertain prediction.

Description

Technical Field

[0001] This invention relates to the field of quantum computing and data processing technology, and in particular to a data processing system and method that combines quantum computing and intelligent computing. Background Technology

[0002] In the fields of artificial intelligence, complex system modeling, and scientific computing, efficient processing of high-dimensional heterogeneous data is one of the core challenges. Especially in scenarios involving multi-dimensional feature coupling, such as AI deep learning, complex system simulation, supply chain situational awareness, and meteorological forecasting, which are typical applications of processing uncertain and complex data, the data dimensions often reach tens of thousands (for example, the feedforward network data of the GPT-4 AI model has as many as 64,000 dimensions), and there are complex nonlinear relationships between the dimensions.

[0003] Currently, the processing of the aforementioned high-dimensional heterogeneous data generally relies on classical computing architectures. Classical algorithms require compiling all data and computational programs into binary code for processing on processors and memory. While this can run when processing linear or low-dimensional data, the computational complexity increases dramatically when the data dimension grows exponentially, resulting in processing times typically in the minutes or even hours, making it difficult to meet real-time requirements.

[0004] Furthermore, existing data processing systems lack a unified benchmark for high-dimensional data representation, and alignment difficulties arise when fusing data from different sources, limiting the accuracy of the processing results. Meanwhile, with the development of quantum computing technology, traditional encrypted transmission and storage schemes face potential security threats, and existing distributed storage architectures struggle to support the high-speed read and write requirements of high-dimensional data.

[0005] Therefore, there is an urgent need for a data processing solution that can break through the limitations of classical computing power and combine high efficiency, high accuracy, and high security when processing high-dimensional nonlinear data. Summary of the Invention

[0006] This invention provides a data processing system and method that combines quantum computing and intelligent computing to solve the problems of low processing efficiency, poor real-time performance, difficulty in predicting uncertainties, and insufficient accuracy caused by the exponential increase in computational complexity with the data dimension when processing high-dimensional heterogeneous data. This invention enables real-time and accurate processing of high-dimensional heterogeneous data.

[0007] On one hand, the present invention provides a data processing system that combines quantum computing and intelligent computing, comprising: a data acquisition and allocation module, used to acquire deterministic historical data and uncertain evolution data of a target object, and allocate the deterministic historical data and the uncertain evolution data to a data benchmark calculation processing module and a data quantum state vector model construction module; a data benchmark calculation processing module, used to perform benchmark calculation processing on the deterministic historical data to obtain deterministic calculation results; a data quantum state vector model construction module, which performs data processing in parallel with the data benchmark calculation processing module, used to encode the deterministic historical data and the uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation results, and its imaginary part corresponds to the prediction components calculated based on the uncertain evolution data; a quantum evolution operation module, used to perform quantum evolution operations on the quantum state vector model to obtain uncertain prediction results; and a comprehensive processing result generation module, used to fuse the deterministic calculation results and the uncertain prediction results to generate a comprehensive processing result.

[0008] Furthermore, the data quantum state vector model construction module is specifically used for: retrieving corresponding reference quantum state parameters from a pre-built quantum reference library based on the object attributes represented by the deterministic historical data and the uncertain evolution data; loading the deterministic historical data and the uncertain evolution data into a quantum register through amplitude encoding to construct an initial quantum state vector model; applying an entanglement initialization circuit to the initial quantum state vector model based on the historical correlation matrix to establish quantum entanglement relationships between different object attributes, thereby obtaining a correlated quantum state vector model; binding the reference quantum state parameters to the correlated quantum state vector model through a coupling operator, and corresponding the real part of the correlated quantum state vector model to the deterministic calculation result to construct the quantum state vector model.

[0009] Furthermore, the quantum evolution operation module is specifically used for: performing inner product operations on the quantum state vector model and the quantum state corresponding to the benchmark configuration based on a preset data processing scenario, quantizing the quantum state overlap of the two based on the obtained inner product modulus, and obtaining a relative value measurement value; performing tensor product spatial projection operations on the quantum state vector model, quantifying the synergistic integration effect between objects based on the obtained inner product projection value; performing joint operations on the quantum state vector models of multiple related objects, measuring the linear and nonlinear correlation of multiple object attributes based on the obtained inner product modulus and phase; applying quantum measurement operators to the quantum state vector model, and identifying the systemic risk transmission path based on the obtained operator eigenvalues ​​and spectral decomposition results.

[0010] Furthermore, the quantum evolution operation module is also used to: calculate the inner product modulus deviation between the quantum state vector model and the quantum state corresponding to the reference configuration; evaluate the degree of deviation of the object combination relative to the reference state based on the obtained deviation value, and realize the measurement of the dynamic changes of the object; map the preset object state transformation operation to a linear operator on the Hilbert space; obtain the estimated result of the object state transformation operation by executing the operation corresponding to the linear operator; set a complete transformation rule from the input object state to the output object state; and ensure the verifiability of the object state transformation operation by verifying the reproducibility of the complete transformation rule.

[0011] Furthermore, the quantum evolution operation module is also used to: encode at least one of the triggering conditions, payment functions, and conversion rights of the preset contract into a preset structural position of the operator matrix to obtain a quantum operator containing the logical features of the contract, so as to realize the quantum executable encoding of the contract terms; decompose the preset engineering process into a sequential product of basic quantum unitary operators, and generate a modular design quantum circuit by standardizing the combination of the basic quantum unitary operators, so as to improve the quantum measurement and verification efficiency of the engineering design.

[0012] Furthermore, the comprehensive processing result generation module is specifically used to: calculate the deviation between the benchmark net value in the deterministic calculation result and the corresponding item in the uncertain prediction result; if the deviation exceeds a preset threshold, trigger a review mechanism to control the data acquisition and allocation module to reacquire deterministic historical data and uncertain evolution data for processing; and if the deviation is within the preset threshold range, generate the comprehensive processing result.

[0013] Furthermore, it also includes a quantum benchmark library construction module, used to: obtain the public attributes of an object, the public attributes including at least one of a benchmark ratio, volatility parameter, tax parameter and price index; map the public attributes to benchmark quantum state parameters in Hilbert space, and assign a unique quantum identifier to each public attribute through quantum phase encoding, thereby constructing a multi-dimensional quantum benchmark library.

[0014] Furthermore, the quantum benchmark library construction module is also used to: dynamically generate benchmark quantum state parameters that adapt to data changes based on real-time data streams using the quantum Monte Carlo algorithm, and adjust the benchmark quantum state parameters in real time through the superposition evolution mechanism of quantum states to continuously update the quantum benchmark library.

[0015] Furthermore, the data benchmark calculation and processing module is specifically used to: perform double accounting processing on the deterministic historical data to obtain the deterministic calculation result; wherein, the deterministic calculation result includes at least one of asset and liability data, profit and loss data and cash flow data prepared in accordance with preset accounting standards.

[0016] Furthermore, it also includes a data preprocessing module, used to: perform quantum interpolation on missing values ​​in the deterministic historical data and / or the uncertain evolution data, and simultaneously evaluate multiple interpolation schemes using quantum parallelism; fill the missing values ​​using the optimal interpolation scheme, and send the filled data to the data quantum state vector model construction module.

[0017] Furthermore, it also includes a quantum state filtering module, used to: apply a noise filtering operator to the quantum state vector model, and sequentially perform quantum Fourier transform, frequency domain filtering and inverse quantum Fourier transform to filter out noise in the quantum state evolution process.

[0018] Furthermore, it also includes a heterogeneous computing power scheduling module, used to: call a quantum processor, a graphics processor, or a central processing unit to execute the quantum evolution operation according to the complexity of the current processing task; wherein, the task scheduled to the quantum processor has the highest complexity, the task scheduled to the graphics processor has the second highest complexity, and the task scheduled to the central processing unit has the lowest complexity.

[0019] Furthermore, it also includes a storage module for storing the deterministic calculation results, the uncertain prediction results, and the comprehensive processing results in a distributed database, which is constructed using a sharded consortium blockchain and zero-knowledge proofs.

[0020] Furthermore, the data acquisition and allocation module is specifically used to: in response to a received data processing service request, acquire the deterministic historical data and the uncertain evolution data from an external information system through a network data interface; wherein, the network data interface uses quantum security technology and post-quantum cryptography technology to encrypt the data transmission process.

[0021] Secondly, the present invention also provides a data processing method for parallel quantum computing and intelligent computing, applied to the data processing system for parallel quantum computing and intelligent computing described in any of the above-mentioned embodiments, characterized in that it includes: acquiring deterministic historical data and uncertain evolution data of a target object; performing benchmark calculation processing on the deterministic historical data to obtain a deterministic calculation result; encoding the deterministic historical data and the uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation result, and its imaginary part corresponds to the prediction component calculated based on the uncertain evolution data; performing quantum evolution operations on the quantum state vector model to obtain an uncertain prediction result; and fusing the deterministic calculation result and the uncertain prediction result to generate a comprehensive processing result.

[0022] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a data processing method for parallel quantum computing and intelligent computing as described above.

[0023] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a data processing method for parallel quantum computing and intelligent computing as described above.

[0024] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a data processing method that combines quantum computing and intelligent computing as described above.

[0025] The present invention provides a data processing system that combines quantum computing and intelligent computing in parallel, comprising: a data acquisition and allocation module for acquiring deterministic historical data and uncertain evolution data of a target object, and allocating the deterministic historical data and uncertain evolution data to a data benchmark calculation and processing module and a data quantum state vector model construction module; a data benchmark calculation and processing module for performing benchmark calculation processing on the deterministic historical data to obtain deterministic calculation results; a data quantum state vector model construction module for performing data processing in parallel with the data benchmark calculation and processing module, for encoding the deterministic historical data and uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation results, and its imaginary part corresponds to the prediction components calculated based on the uncertain evolution data; a quantum evolution operation module for performing quantum evolution operations on the quantum state vector model to obtain uncertain prediction results; and a comprehensive processing result generation module for fusing the deterministic calculation results and uncertain prediction results to generate a comprehensive processing result. This system overcomes the exponential computing power bottleneck of classical computing when processing high-dimensional data by adopting a hybrid architecture of benchmark computing and quantum evolution operations. It reduces the processing time of 10,000-dimensional attribute datasets from minutes to seconds, solving the real-time processing problem in high-dimensional nonlinear scenarios. At the same time, by anchoring the real part of the quantum state vector model to deterministic calculation results and the imaginary part to correspond to uncertain prediction components, it balances the compliance of deterministic processing with the accuracy of uncertain prediction, and can adapt to the processing needs of high-dimensional heterogeneous data in multiple fields. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of a data processing system that combines quantum computing and intelligent computing, as provided in an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the overall system of the data processing system that combines quantum computing and intelligent computing in parallel, as provided in the embodiment of the present invention, in the context of financial asset measurement.

[0029] Figure 3 This is a schematic flowchart of the data processing method for parallel quantum computing and intelligent computing provided in the embodiments of the present invention.

[0030] Figure 4 This is a schematic diagram of the physical structure of the electronic device provided in the embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0032] This invention proposes a data processing system that combines quantum computing and intelligent computing. To address the processing needs of high-dimensional heterogeneous data, it adopts a hybrid architecture of benchmark computing and quantum evolution operations, which decomposes high-dimensional data into two types of heterogeneous data sources for separate processing, thus balancing processing efficiency and result accuracy.

[0033] Specifically, Figure 1 A schematic diagram of a data processing system that combines quantum computing and intelligent computing, as provided in an embodiment of the present invention, is shown.

[0034] like Figure 1As shown, the system includes: a data acquisition and allocation module 110, used to acquire deterministic historical data and uncertain evolution data of the target object, and allocate the deterministic historical data and the uncertain evolution data to a data benchmark calculation and processing module and a data quantum state vector model construction module; a data benchmark calculation and processing module 120, used to perform benchmark calculation processing on the deterministic historical data to obtain deterministic calculation results; a data quantum state vector model construction module 130, which performs data processing in parallel with the data benchmark calculation and processing module, used to encode the deterministic historical data and the uncertain evolution data into quantum state amplitudes and construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation results, and its imaginary part corresponds to the prediction components calculated based on the uncertain evolution data; a quantum evolution operation module 140, used to perform quantum evolution operations on the quantum state vector model to obtain uncertain prediction results; and a comprehensive processing result generation module 150, used to fuse the deterministic calculation results and the uncertain prediction results to generate a comprehensive processing result.

[0035] Regarding the data acquisition and distribution module 110: The data acquisition and distribution module 110 has a built-in network interface unit and a data verification and distribution unit. The network interface unit is configured with a dual-end interface. One end connects to external information systems such as enterprise production databases, IoT sensing platforms, bank operation information systems, and third-party data service providers via a standardized network API to receive data to be processed. The other end connects to the business request end via an intelligent agent interface to receive data processing requests initiated by AI agents, application services, and user terminals.

[0036] The data verification and allocation unit performs format verification, integrity verification, and deduplication on the received data, automatically distinguishing between two types of data: deterministic historical data and uncertain evolutionary data. Specifically, after receiving the data to be processed, it automatically splits it according to data attributes, classifying fixed historical observation data as deterministic historical data and real-time and expected data that are still dynamically changing as uncertain evolutionary data. Subsequently, the deterministic historical data is allocated to the data benchmark calculation and processing module for benchmark calculation processing, while the deterministic historical data and uncertain evolutionary data are simultaneously allocated to the data quantum state vector model construction module for amplitude encoding, real part anchoring, and vector model construction.

[0037] Deterministic historical data refers to historical observation data that has been solidified and has no subsequent changes, such as historical equipment operation logs in industrial production scenarios and historical observation records in meteorological scenarios. In financial asset measurement scenarios, this can be specifically reflected as book data. Uncertain evolutionary data refers to real-time data that is still in dynamic change, such as real-time operating condition flow data in industrial production scenarios and real-time atmospheric circulation data in meteorological scenarios. In financial asset measurement scenarios, this can be specifically reflected as multi-dimensional market data.

[0038] Regarding the data benchmark calculation and processing module 120: Running on a computer (CPU, GPU), the data benchmark calculation and processing module 120 has a built-in configurable accounting rule library. It first receives deterministic historical data output from the data acquisition and allocation module 110, performs outlier removal and missing value interpolation, and then calls rules from the accounting rule library adapted to the current scenario. For example, in an industrial production scenario, it calls cost accounting rules; in a meteorological scenario, it calls statistical accounting rules; and in a financial asset measurement scenario, it can call accounting standards. It performs batch calculations on the preprocessed data and outputs structured deterministic calculation results. The deterministic calculation results are floating-point numerical arrays, such as historical total cost values ​​in an industrial production scenario, historical average temperature and humidity values ​​in a meteorological scenario, and specifically, net book value in a financial asset measurement scenario.

[0039] The data benchmark calculation and processing module 120 also has a built-in cache unit to store the calculation results of the same input data locally, thus avoiding duplicate calculations.

[0040] Regarding the data quantum state vector model construction module 130: The data quantum state vector model construction module 130 runs on a computer (CPU, GPU) and includes an amplitude encoding unit, a real part anchoring unit, and an imaginary part mapping unit. The amplitude encoding unit employs the Grover-Rudolph algorithm to load normalized deterministic historical data and uncertain evolution data into a quantum random access memory (QRAM), mapping them to the amplitude of each qubit in the quantum register. This ensures that the entire quantum state satisfies the normalization condition, i.e., the sum of the squares of the moduli of all amplitudes is 1.

[0041] The real part anchoring unit converts the deterministic calculation results output by the data benchmark calculation and processing module 120 into the real components of the quantum state vector model. Specifically, it scales the deterministic calculation results to a value matching the magnitude of the quantum state amplitude and assigns it to the real part of the quantum state vector model. The imaginary part mapping unit uses a time-series prediction model based on a long short-term memory neural network to calculate the uncertain evolution data, obtains an initial prediction value, scales the initial prediction value, and assigns it to the imaginary part of the quantum state vector model as a prediction component.

[0042] Regarding the quantum evolution operation module 140: The quantum evolution operation module 140 runs on a quantum computer (QPU). First, it loads a pre-defined general quantum evolution circuit. This circuit is constructed using quantum algorithms from basic quantum logic gates such as Hadamard gates, controlled NOT gates, and phase rotation gates, enabling unitary evolution of quantum states and capturing nonlinear correlations between high-dimensional data. Subsequently, after performing calibration and error correction operations on the qubits, the evolution circuit is run to evolve the input quantum state vector model. After the evolution is complete, a measurement operation is performed on the quantum state, the expected measurement value is read, and the uncertainty prediction results are summarized.

[0043] The quantum evolution operation module 140 includes the probability distribution, expectation and confidence interval of the predicted value, such as the probability distribution of equipment failure in industrial production scenarios, the probability distribution of precipitation in meteorological scenarios, and in financial asset measurement scenarios, it can be specifically the predicted distribution of equity premium.

[0044] Regarding the comprehensive processing result generation module 150: This module incorporates a deviation verification unit, a result fusion unit, and an output unit. The deviation verification unit calculates the absolute deviation between corresponding items in the deterministic calculation results and the uncertain prediction results, comparing it to a preset threshold. If the deviation exceeds the threshold, a verification signal is triggered, controlling the data acquisition and allocation module 110 to reacquire data and execute the full-process calculation. If the deviation is within the threshold range, the result fusion unit encapsulates the two types of results into a structured message, including the deterministic calculation value, the uncertain prediction value, the confidence interval, the timestamp, and the data source identifier. The output unit writes the structured message to non-volatile memory and sends it to external business systems through a standardized interface. It can also generate visual charts, such as line charts and heatmaps, as needed for user viewing. In financial asset measurement scenarios, the structured message can specifically be a comprehensive financial asset measurement report.

[0045] It should be noted that the data processing system that combines quantum computing and intelligent computing in parallel, as provided in this embodiment of the invention, can be applied not only to financial asset measurement scenarios, but also to other scenarios that require processing high-dimensional heterogeneous data, such as complex system simulation, supply chain situation awareness, and weather evolution prediction. No specific limitations are made here.

[0046] Example 1: In the context of financial asset measurement, deterministic historical data specifically refers to the book value of financial assets, including statutory financial data such as net assets, cash balance, bond face value, and loan principal in the balance sheet; uncertain evolutionary data specifically refers to multi-dimensional market data, including macroeconomic indicators, industry prosperity data, enterprise growth indicators, public opinion semantic data, supply chain location parameters, ESG scores, etc.; the predictive component specifically refers to the equity premium component calculated based on market expectations; and the comprehensive processing result specifically refers to the comprehensive financial asset measurement report.

[0047] Example 2: In the context of supply chain situational awareness, deterministic historical data specifically includes historical logistics, inventory, and order data; uncertain evolutionary data specifically includes market demand forecasts, weather disturbances, and risk data; benchmark calculation processing performs accounting on historical supply chain data, quantum evolutionary operations predict supply chain disruption risks and delivery delay probabilities, and the comprehensive processing result is a supply chain risk early warning report.

[0048] Example 3: In the scenario of meteorological evolution prediction, deterministic historical data specifically refers to historical meteorological observation data, such as temperature, precipitation, and wind speed, while uncertain evolution data specifically refers to atmospheric circulation and ocean current evolution data. The baseline calculation process performs statistical analysis on the historical meteorological data, and the quantum evolution operation predicts the probability of occurrence and the scope of impact of extreme weather events. The comprehensive processing result is a meteorological disaster early warning report.

[0049] Example 4: In a complex system simulation scenario, deterministic historical data specifically includes system historical operation logs and component parameter data, while uncertain evolutionary data specifically includes external environmental disturbances and component aging data. The benchmark calculation process verifies the system's historical operating status, and the quantum evolution operation predicts the system's fault propagation path and failure probability. The comprehensive processing result is a system reliability assessment report.

[0050] In this embodiment, the data processing system that combines quantum computing and intelligent computing includes: a data acquisition and distribution module 110, used to acquire deterministic historical data and uncertain evolution data of the target object; a data benchmark calculation and processing module 120, used to perform benchmark calculation processing on the deterministic historical data to obtain deterministic calculation results; a data quantum state vector model construction module 130, used to encode the deterministic historical data and uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation results, and its imaginary part corresponds to the prediction components calculated based on the uncertain evolution data; a quantum evolution operation module 140, used to perform quantum evolution operations on the quantum state vector model to obtain uncertain prediction results; and a comprehensive processing result generation module 150, used to fuse the deterministic calculation results and uncertain prediction results to generate a comprehensive processing result. This system overcomes the exponential computing power bottleneck of classical computing when processing high-dimensional data by adopting a hybrid architecture of benchmark computing and quantum evolution operations. It reduces the processing time of 10,000-dimensional attribute datasets from minutes to seconds, solving the real-time processing problem in high-dimensional nonlinear scenarios. At the same time, by anchoring the real part of the quantum state vector model to deterministic calculation results and the imaginary part to correspond to uncertain prediction components, it balances the compliance of deterministic processing with the accuracy of uncertain prediction, and can adapt to the processing needs of high-dimensional heterogeneous data in multiple fields.

[0051] The following describes in detail the data processing system for parallel quantum computing and intelligent computing provided by this invention, using the financial asset measurement scenario as a typical application example. Those skilled in the art will understand that the technical solution of this invention can also be applied to other scenarios that require processing high-dimensional heterogeneous data, such as complex system simulation, supply chain situation awareness, and weather evolution prediction. The specific implementation logic can be deduced by referring to the financial asset measurement scenario example below.

[0052] Specifically, the data processing system for parallel quantum computing and intelligent computing provided in this embodiment of the invention includes: a data acquisition and allocation module 110, used to acquire book data and multidimensional market data of a target financial asset; a data benchmark calculation and processing module 120, used to perform classical state measurement on the book data to obtain classical measurement results; a data quantum state vector model construction module 130, used to encode the book data and the multidimensional market data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the classical measurement results, and its imaginary part corresponds to the equity premium component calculated based on market expectations; a quantum evolution operation module 140, used to perform quantum operations on the quantum state vector model to obtain quantum measurement results; and a comprehensive processing result generation module 150, used to fuse the classical measurement results and the quantum measurement results to generate a comprehensive financial asset measurement report.

[0053] The data processing system that combines quantum computing and intelligent computing provided in this embodiment is applied to the intelligent quantum financial asset measurement scenario. It utilizes a quantum electronic computing power operation controller to schedule heterogeneous computing power resources, thereby realizing intelligent measurement of financial assets throughout the entire process from data acquisition to report generation.

[0054] The following section will first provide a detailed description of each module included in the system and the technical steps involved in their implementation.

[0055] The data acquisition and distribution module 110 is used to acquire the book data and multi-dimensional market data of the target financial asset, and forward the book data to the data benchmark calculation and processing module 120, and forward the book data and multi-dimensional market data together to the data quantum state vector model construction module 130.

[0056] Book data refers to the statutory financial data recorded by the issuer or holder of financial assets in accordance with accounting standards, reflecting the definite value of claims formed from historical transactions. Specifically, it includes data on items such as net assets, cash balance, bond face value, and loan principal in the balance sheet.

[0057] Multidimensional market data refers to various unstructured or semi-structured data that, in addition to book data, can reflect the future value drivers of financial assets. It covers multiple dimensions such as macroeconomic indicators, industry prosperity data, corporate growth indicators, public opinion semantic data, supply chain location parameters, and ESG scores.

[0058] It is easy to understand that the data acquisition and distribution module 110 responds to the financial measurement business requests sent by the financial intelligent agent system. By configuring a network data interface with quantum security technology and post-quantum cryptography technology, it obtains accounting data and multi-dimensional market data from external financial information systems such as enterprise resource planning systems, stock exchange market systems, and third-party data service provider platforms, ensuring the security of data transmission and resisting the encryption cracking risks brought by quantum computing.

[0059] The data benchmark calculation and processing module 120 is used to perform classical state measurement on the book data to obtain classical measurement results.

[0060] After obtaining the book data, the data benchmark calculation and processing module 120 performs classical measurement on the book data to obtain classical measurement results. Classical measurement is an accounting method that follows the traditional double-entry bookkeeping logic and uses legal tender as the unit of measurement, and is fully compatible with existing accounting standards and regulatory requirements.

[0061] Specifically, the accounting data is first preprocessed, including steps such as account alignment, outlier verification, and format standardization. Then, the preprocessed accounting data is balanced by debit and credit accounting to generate classic measurement results that meet the requirements of accounting standards, including the balance sheet, income statement, cash flow statement, and the net book value of each type of debt asset.

[0062] It should be noted that the classical econometric results form the compliance basis for the construction of the data quantum state vector model in subsequent steps, corresponding to the deterministic historical value portion of the financial asset value.

[0063] The quantum evolution operation module 140 is used to encode the book data and the multidimensional market data into quantum state amplitudes and construct a quantum state vector model; the real part of the quantum state vector model corresponds to the classical econometric result, and its imaginary part corresponds to the equity premium component calculated based on market expectations.

[0064] The quantum evolution operation module 140 encodes book data and multidimensional market data into quantum state amplitudes, constructing a quantum state vector model. A quantum state amplitude is a complex parameter in quantum mechanics describing the projection probability of a quantum state onto a basis vector. Encoding it as a quantum state amplitude maps classical financial data into the probability amplitude of a qubit, thus representing the state of financial assets in Hilbert space. The quantum state vector model is a complex vector representing the overall value of financial assets in Hilbert space, with its magnitude representing the total value of the financial assets.

[0065] When constructing a quantum state vector model, the benchmark quantum state parameters that match the target financial asset are first retrieved from a pre-built financial quantum benchmark library. The benchmark quantum state parameters are obtained by mapping public attributes such as benchmark interest rates, exchange rates, tax rates, and consumer price indices into standardized quantum states in Hilbert space. Each benchmark quantum state parameter is given a unique quantum identifier through quantum phase encoding technology, which can quickly match the benchmark requirements of the target financial asset.

[0066] Then, the fully populated book data and multidimensional market data are loaded into the quantum register using the Grover-Rudolph algorithm or QRAM to construct an initial quantum state vector model. Next, an entanglement initialization circuit is applied to the initial quantum state vector model based on the historical correlation matrix to establish quantum entanglement relationships between different financial assets, resulting in a correlated quantum state vector model. The historical correlation matrix is ​​a statistical matrix calculated based on the book data and multidimensional market data of the target financial asset set within a preset historical window. It is used to characterize the linear and nonlinear correlation strength between different financial assets or between different attribute dimensions of the same financial asset.

[0067] Finally, the retrieved benchmark quantum state parameters are bound to the associated quantum state vector model through a coupling operator, and the real part of the quantum state vector model is matched with the previously obtained classical econometric results, while the imaginary part is set as the equity premium component calculated based on future market expectations, thus obtaining the final quantum state vector model.

[0068] In this model, the real part of the quantum state vector model corresponds to the deterministic historical value of the target financial asset, while the imaginary part corresponds to the equity premium component of the target financial asset. Together, they constitute the complete value representation of the target financial asset.

[0069] The quantum evolution operation module 140 is used to perform quantum operations on the quantum state vector model to obtain quantum measurement results.

[0070] After receiving the quantum state vector model forwarded by the data quantum state vector model construction module 130, the quantum evolution operation module 140 performs quantum operations on the quantum state vector model to obtain quantum measurement results. Here, the quantum operations on financial assets are a series of linear operator operations carried out in Hilbert space based on the characteristics of quantum superposition and entanglement. Its core is to map the nonlinear linkage between financial assets, the risk contagion path and the market evolution logic into unitary operators, and to achieve parallel processing of high-dimensional financial systems through the evolution of quantum states.

[0071] Specifically, the constructed quantum state vector model is first imported into a pre-built quantum circuit as the input state. The quantum circuit integrates multiple functional operators, including asset relative value assessment, portfolio risk correlation analysis, synergistic effect calculation, and verification of complex financial operations. During the operation, the quantum state vector model interacts with each functional operator sequentially, utilizing quantum parallelism to synchronously traverse all possible state combinations in the multidimensional feature space. This allows for the accurate capture of implicit collinearity and higher-order entanglement relationships between different financial assets without traversing the classical solution space.

[0072] After the computation, the evolved quantum state is measured, and the expected values ​​corresponding to each functional operator are read. These expected values, in the form of probability amplitudes, reflect the value distribution and risk exposure of financial assets under different market scenarios. By further summarizing these measurement results, multi-dimensional quantum metrology results including asset valuation, risk exposure, synergistic gains, and operational feasibility can be generated.

[0073] The quantum metrology results obtained in this step not only quantify the current state of financial assets, but also reveal the potential path of future market evolution through the phase information of quantum states.

[0074] It is worth mentioning that the entire quantum computation process for financial assets is calibrated in real time using dynamic benchmark quantum state parameters provided by a pre-built financial quantum benchmark library, ensuring the timeliness and accuracy of quantum metrology results in volatile market environments. The construction process of the financial quantum benchmark library will be detailed in the embodiments below.

[0075] The comprehensive processing result generation module 150 is used to integrate the classical measurement results and the quantum measurement results to generate a comprehensive financial asset measurement report.

[0076] Specifically, the comprehensive processing result generation module 150 calculates the deviation between the book net value in the classical measurement result and the corresponding item in the quantum measurement result (i.e., the real part of the historical cost value in the quantum measurement result). If the deviation exceeds a preset threshold, a review mechanism is triggered to reacquire the book data and multi-dimensional market data and start the full-process recalculation until the deviation meets the requirements. If the deviation is within the preset threshold range, the classical measurement result, the quantum measurement result, and the deviation explanation of the two are integrated to generate a comprehensive financial asset measurement report.

[0077] The comprehensive financial asset measurement report generated in this step will be synchronously stored in a distributed database built using a sharded consortium blockchain and zero-knowledge proofs to ensure that the data is tamper-proof and fully traceable. At the same time, the report results will be fed back to the financial intelligent agent system through a data interface to support subsequent intelligent financial decision-making.

[0078] In this embodiment, the book value and multidimensional market data of the target financial asset are acquired, and the book value is measured using classical methods to obtain classical measurement results. The book value and multidimensional market data are encoded into quantum state amplitudes to construct a quantum state vector model. The real part of the quantum state vector model corresponds to the classical measurement results, and its imaginary part corresponds to the equity premium component calculated based on market expectations. Then, quantum operations on the financial asset are performed on the quantum state vector model to obtain quantum measurement results. Thus, the classical measurement results and quantum measurement results are integrated to generate a comprehensive financial asset measurement report. This method, through a classical-quantum dual-state co-measurement architecture, integrates the book value of debt and equity premium into complex vector measurement, fundamentally eliminating the value measurement blind spot of traditional double-entry bookkeeping, and controlling the valuation error within 0.1%. Through the quantum superposition and entanglement characteristics and heterogeneous computing power scheduling, the measurement time of multidimensional attribute financial asset portfolios can be compressed from minutes to seconds in classical calculations, supporting real-time dynamic updates of valuations. This breaks through the exponential computing bottleneck of high-dimensional nonlinear scenarios, ultimately achieving comprehensive, accurate, and efficient measurement of overall financial assets.

[0079] Based on the above embodiments, the following will take the financial asset measurement scenario as an example to describe the data quantum state vector model construction module 130 in detail.

[0080] The data quantum state vector model construction module 130 is specifically used for: retrieving corresponding reference quantum state parameters from a pre-built quantum reference library based on the object attributes characterized by deterministic historical data and uncertain evolution data; loading deterministic historical data and uncertain evolution data into a quantum register through amplitude encoding to construct an initial quantum state vector model; applying an entanglement initialization circuit to the initial quantum state vector model based on the historical correlation matrix to establish quantum entanglement relationships between different object attributes, thereby obtaining a correlated quantum state vector model; binding the reference quantum state parameters to the correlated quantum state vector model through coupling operators, and corresponding the real part of the correlated quantum state vector model to the deterministic calculation results to construct the quantum state vector model.

[0081] In the context of financial asset measurement, the data quantum state vector model construction module 130 is specifically used to: encode book data and multidimensional market data into quantum state amplitudes and construct a quantum state vector model, including: retrieving corresponding benchmark quantum state parameters from a pre-built financial quantum benchmark library based on the asset attributes represented by the book data and multidimensional market data; loading the book data and multidimensional market data into a quantum register through amplitude encoding to construct an initial quantum state vector model; applying an entanglement initialization circuit to the initial quantum state vector model based on the historical correlation matrix to establish quantum entanglement relationships between different financial assets and obtain a correlated quantum state vector model; binding the benchmark quantum state parameters to the correlated quantum state vector model through coupling operators, and anchoring the real part of the correlated quantum state vector model based on classical measurement results to construct the quantum state vector model.

[0082] It is easy to understand that, in the process of constructing the quantum state vector model, the data quantum state vector model construction module 130 first matches and retrieves the corresponding benchmark quantum state parameters from the financial quantum benchmark library based on the asset attributes represented by the book data and multi-dimensional market data, such as the industry affiliation, asset type, and risk level of the enterprise to be measured, so as to provide a unified reference benchmark for subsequent quantum state calibration.

[0083] Among them, the Financial Quantum Benchmark Library is a pre-constructed distributed storage carrier that stores the quantum mapping results of the public attributes of various financial assets. It integrates standardized benchmark quantum state parameters corresponding to public attributes such as benchmark interest rates, exchange rates, tax rates, and consumer price indices. Each parameter is marked with a unique quantum identifier through quantum phase encoding technology, supporting high-speed retrieval based on quantum entanglement characteristics.

[0084] If the acquired book data or multidimensional market data has missing data, quantum interpolation is performed on the missing data before retrieving the benchmark quantum state parameters. Quantum parallelism is used to evaluate multiple interpolation schemes simultaneously, and the optimal interpolation scheme is quickly selected from multiple candidate interpolation schemes to fill the missing values, thus ensuring the integrity of the book data and multidimensional market data.

[0085] Next, amplitude encoding loading is performed. Amplitude encoding is a quantum data loading technique that maps classical high-dimensional data into the projected probability amplitude of quantum states under Hilbert space basis vectors, enabling efficient embedding of classical information into quantum states. In this embodiment, the filled full book data and multi-dimensional market data are loaded into the quantum register using the Grover-Rudolph algorithm or quantum random access memory. After loading, the initial quantum state vector model is obtained.

[0086] Quantum random access memory (QR) is a quantum hardware component that supports efficient conversion of classical data to quantum states, capable of loading large-scale classical data into quantum amplitudes in polynomial time complexity. A quantum register is a fundamental quantum computing unit composed of multiple qubits used to store quantum states, whose states can be fully described by vectors in Hilbert space. The initial quantum state vector model is a direct product of the asset attributes; the correlations between different attributes or assets have not yet been established.

[0087] Entanglement initialization is then performed. First, the pre-calculated historical correlation matrix is ​​obtained. Then, the historical correlation matrix is ​​decomposed into eigenvalues. The parameters obtained from the decomposition are mapped to the gating parameters of the entanglement initialization circuit. By applying the entanglement initialization circuit to the initial quantum state vector model, the historical statistical correlation between financial assets is transformed into the quantum entanglement relationship between qubits, thus obtaining the correlated quantum state vector model.

[0088] The historical correlation matrix is ​​a statistical matrix calculated based on the book data and multidimensional market data of the target financial asset set within a preset historical window. It characterizes the linear and nonlinear correlation strength between different financial assets or between different attribute dimensions of the same financial asset, and can take the form of a Pearson correlation coefficient matrix, covariance matrix, or mutual information matrix. The entanglement initialization circuit is a quantum circuit composed of a series of parameterized quantum logic gates used to establish entanglement relationships between qubits. The correlated quantum state vector model can effectively capture the implicit collinearity and high-order entanglement characteristics of high-dimensional data.

[0089] Finally, benchmark binding and real part anchoring are performed. The retrieved benchmark quantum state parameters are bound to the associated quantum state vector model through coupling operators, so that the quantum state has a built-in unified public attribute reference benchmark. Then, the real part of the associated quantum state vector model is anchored based on the classical measurement results obtained from classical state measurement, and the real components of the associated quantum state vector model are strictly aligned with the classical measurement results to ensure that the quantum measurement results comply with the requirements of existing accounting standards, and finally the quantum state vector model is obtained.

[0090] The coupling operator, defined on Hilbert space, is a linear operator used to realize the interaction between different quantum states, capable of coupling and superimposing the parameters of two independent quantum states. The quantum state vector model adopts complex form. The unified representation is used, where the real part D corresponds to the debt attribute and is used to measure and record the historical contractual value of the target financial asset, and the imaginary part E corresponds to the equity attribute and is used to measure and predict the present value of the future income of the target financial asset.

[0091] Preferably, after constructing the quantum state vector model, a noise filtering operator is applied to the quantum state vector model, and quantum Fourier transform, frequency domain filtering, and inverse quantum Fourier transform are executed in sequence to filter out noise in the quantum state evolution process.

[0092] It is worth mentioning that each dimension of the quantum state vector model in this embodiment corresponds to an asset attribute, which can accurately model financial assets with multiple categories and attributes. When the attribute dimensions reach tens of thousands, the quantum representation based on amplitude encoding can show quantum computing advantages that classical methods cannot match in terms of information density and computational complexity.

[0093] Based on the above embodiments, the following will take the financial asset measurement scenario as an example to describe the quantum evolution operation module 140 in detail.

[0094] After the quantum state vector model is constructed by the data quantum state vector model construction module 130, the quantum evolution operation module 140 performs quantum operations on the quantum state vector model to obtain quantum measurement results. The quantum operations on financial assets here include, but are not limited to, the following nine items.

[0095] (1) Based on the financial asset measurement scenario, the quantum state vector model and the quantum state corresponding to the benchmark configuration are subjected to inner product operation. The quantum state overlap of the two is quantized according to the inner product modulus, and the relative value measurement value of the financial asset is obtained.

[0096] Specifically, in the context of financial asset measurement, the similarity measurement of asset configuration is carried out by performing an inner product operation on the quantum state vector model representing the target portfolio and the quantum state vector model representing the benchmark configuration. The quantum state overlap of the two is quantified based on the inner product modulus, thereby obtaining the relative value measurement value of financial assets and providing a quantitative basis for asset allocation optimization.

[0097] Among them, quantum state overlap refers to the square of the cosine of the angle between two quantum state vector models in Hilbert space. Its value is quantitatively represented by the inner product modulus obtained by performing an inner product operation on the two quantum states. The larger the inner product modulus, the higher the quantum state overlap, which represents the higher the similarity between the two financial assets in the multidimensional attribute space.

[0098] (2) Perform tensor product spatial projection operation on the quantum state vector model, and quantify the synergistic integration effect of financial assets based on the obtained inner product projection value.

[0099] Specifically, in collaborative scenarios such as corporate mergers and acquisitions or asset restructuring, the quantum state vector models of both parties to the merger are used to construct a tensor product space. The quantified value of the synergistic integration effect is extracted through inner product projection operation. This value can objectively evaluate the potential synergistic value of the merged enterprise in terms of cost control, revenue growth, etc., and provide accurate quantum metrological support for transaction price negotiation and value assessment.

[0100] The tensor product space is a higher-dimensional composite space constructed from two or more Hilbert spaces through tensor product operations, capable of fully accommodating all state information of a multi-asset joint system. Inner product projection refers to the scalar value obtained by projecting a specific joint quantum state vector model onto a target subspace within the aforementioned tensor product space; its magnitude directly reflects the component strength of the joint state in a specific dimension.

[0101] (3) Perform joint calculations on the quantum state vector models of multiple related financial assets, and measure the linear and nonlinear correlations of multiple financial assets based on the obtained inner product modulus and phase.

[0102] Specifically, when performing joint operations, it not only measures the strength of linear correlation between assets by the inner product modulus of the quantum state vector model, but also captures the nonlinear coupling relationship and implicit collinearity between price changes of different assets by extracting and analyzing the phase difference between different quantum state vectors. This overcomes the limitation of traditional correlation analysis, which can only deal with linear problems, and fully reveals the complex linkage mechanism and risk contagion path in the financial market.

[0103] Among them, phase is the argument parameter of the quantum state vector model in high-dimensional Hilbert space, which determines the interference characteristics of the quantum state vector model and is a key physical quantity characterizing the deep coupling relationship inside the quantum system.

[0104] (4) Apply quantum metrology operators to the quantum state vector model and identify the systemic risk transmission path based on the obtained operator eigenvalues ​​and spectral decomposition results.

[0105] Specifically, by applying quantum metrology operators to the quantum state vector model, solving for its operator eigenvalues ​​and performing spectral decomposition, the key eigenmodes that have the greatest impact on system stability are identified, thereby quantifying the amplification effect of single asset risk on the entire financial network and accurately locating the transmission path and weak links of systemic risk.

[0106] Quantum metrology operators are linear operators defined on Hilbert space to describe the evolution of financial asset values ​​or the propagation of risk shocks. Operator eigenvalues ​​are scalar multiples obtained when the operator acts on its eigenstates, representing the observed value of the operator in a specific state. Spectral decomposition is a mathematical method that decomposes an operator into the superposition of the outer products of all its eigenvalues ​​and eigenstates, fully revealing the operator's intrinsic structure.

[0107] (5) Calculate the inner product modulus deviation between the quantum state vector model and the quantum state corresponding to the benchmark configuration, and evaluate the degree of deviation of the financial asset portfolio relative to the benchmark index based on the obtained deviation value, so as to realize the measurement of the dynamic changes of financial assets.

[0108] Specifically, in dynamic asset management scenarios, the quantum state vector model of the target financial asset is continuously monitored, and the inner product modulus deviation between the target financial asset and the quantum state corresponding to the benchmark index is calculated periodically. This deviation value can effectively capture the subtle changes in the portfolio caused by market fluctuations. When the deviation exceeds the preset risk threshold, a rebalancing warning is automatically triggered, thereby achieving accurate monitoring and real-time measurement of the dynamic risk of financial assets.

[0109] The inner product modulus deviation refers to the difference between the actual modulus obtained by performing an inner product operation on the target quantum state vector model and the theoretical modulus obtained by performing an inner product operation on the reference configuration quantum state.

[0110] (6) Map at least one of the financial operations, such as debt-to-equity swap, asset securitization, and derivative pricing, to a linear operator on Hilbert space, and obtain the quantum quantitative valuation result of the financial operation by executing the operation corresponding to the linear operator.

[0111] Specifically, for complex financial engineering operations, the debt restructuring process of debt-to-equity swaps is mapped to a conversion operator from debt quantum state to equity quantum state; the cash flow restructuring process of asset securitization is mapped to a mapping operator from the underlying asset pool quantum state to the securitized product quantum state; and the derivative pricing model is mapped to an operator representing the expected value of the underlying asset quantum state's maturity yield. By executing these linear operators on the quantum state vector model, the quantum quantitative valuation results of financial operations can be quickly solved, thereby significantly improving the computational efficiency and pricing accuracy of complex financial engineering problems.

[0112] Among them, linear operators are quantum evolution operators that satisfy the homogeneity of addition and the superposition of multiplication, and can describe the deterministic transition of a closed quantum system from one state to another.

[0113] (7) Set complete transformation rules from input asset state to output asset state, and ensure the verifiability of financial operations by verifying the reproducibility of the complete transformation rules.

[0114] Specifically, to ensure the traceability and compliance of financial operations, after the financial operation is executed, the output asset status is reverse-derived and reproducible according to the preset conversion rules to verify whether the actual output asset status is completely consistent with the rule expectation. If the verification passes, the operation is confirmed to be valid; if the verification fails, an abnormal alarm is triggered. This ensures the verifiability of the entire life cycle of the financial operation at the technical level and prevents irreversible logical deviations in the measurement process.

[0115] The conversion rules define the mathematical logic and constraints of asset status changes before and after financial operations, covering multiple dimensions such as accounting treatment, tax impact, and changes in legal ownership.

[0116] (8) Encode at least one of the triggering conditions, payment functions and conversion rights of the financial contract into a preset structural position of the operator matrix to obtain a quantum operator containing the logical features of the contract, so as to realize the quantum executable encoding of the financial contract terms.

[0117] Specifically, when processing financial contracts, the triggering conditions of the contract are encoded in the diagonal element distribution of the operator matrix to define the state activation boundary, the payment function is encoded in the off-diagonal element distribution to quantify the value exchange of state transitions, and the conversion rights are encoded in the phase parameters of the operator matrix to reflect the time value and choice characteristics of the rights. This generates a quantum operator containing complete contract logic features, enabling complex legal clauses to be directly parsed and executed by quantum circuits, thus realizing the digitalization and automation of financial contracts.

[0118] The operator matrix is ​​the matrix representation of a quantum operator under selected basis vectors. Its diagonal elements correspond to the eigenvalue components of the operator, and the off-diagonal elements correspond to the coupling strength between different basis vectors.

[0119] (9) The financial engineering process is decomposed into the sequential product of basic quantum unitary operators. By standardizing the combination of the basic quantum unitary operators, modular quantum circuits for financial asset design are generated to improve the efficiency of quantum measurement and verification in financial engineering.

[0120] Specifically, complex financial engineering processes, such as structured product design or risk management strategies, are broken down into an ordered combination of a series of basic quantum unitary operators. These basic operators are then assembled into complete quantum circuits for financial asset design through standardized interfaces. This modular design approach not only significantly improves the innovation efficiency of financial products but also enables complex financial engineering solutions to undergo efficient quantitative verification and stress testing through quantum circuits.

[0121] Among them, quantum unitary operators are linear operators that satisfy the unitary condition and can describe the reversible evolution of a closed quantum system, ensuring the normalization and probability conservation of the quantum state vector model during the evolution process. Sequential product refers to the product operation of multiple operators acting sequentially in time, reflecting the continuous evolution process of the quantum system over time.

[0122] After performing the aforementioned quantum operations on various financial assets on the quantum state vector model, quantum metrology results can be generated. Unlike classical metrology, which can only output a single definite value, quantum metrology results are displayed as probability amplitude distributions. The probability amplitude is a complex parameter in quantum mechanics that describes the likelihood of a quantum state appearing under a specific basis vector. The square of its magnitude directly corresponds to the probability of the financial asset taking a value under a specific future market scenario.

[0123] Specifically, quantum state measurement is performed on the evolved quantum state vector model. Quantum state measurement is the physical process of collapsing the superposition of quantum states into a certain eigenstate and reading the corresponding eigenvalue. Through measurement operations, the expected value and full probability amplitude distribution of the quantum measurement operator in each dimension are read. For example, when valuing a company, the quantum state measurement result does not only give a fixed market value, but also the probability amplitude distribution of the company's market value in different ranges. For example, the probability amplitude is highest when the market value is in the range of 300 billion to 350 billion US dollars, and the probability amplitude is second when it is above 400 billion US dollars. This can accurately reflect the inherent uncertainty and future expected characteristics of equity value.

[0124] By aggregating these multi-dimensional probability amplitude data, expected values, and risk feature vectors obtained through operator spectral decomposition, they are structured into a comprehensive dataset containing asset valuation, risk exposure, synergistic effect quantification, and confidence intervals, thereby generating a quantum financial asset report as a quantum econometric result. The quantum financial asset report not only records the current quantitative state of the target financial asset but also reveals the potential evolutionary path of asset value through probability amplitude distribution.

[0125] Subsequently, the generated quantum financial asset report and the original measurement results data are written into a distributed database or blockchain network, and the aforementioned measurement results are returned to the financial intelligent agent system through a standardized data interface API. Upon receiving the quantum measurement results containing the probability amplitude distribution, the financial intelligent agent system can perform more refined risk management and automated business execution based on the probability distribution characteristics. For example, it can automatically trigger hedging strategies when the probability amplitude indicates high risk, thereby realizing closed-loop control from quantum measurement to intelligent decision-making.

[0126] Based on the above embodiments, the following will take the financial asset measurement scenario as an example to describe the comprehensive processing result generation module 150 in detail.

[0127] The comprehensive processing result generation module 150 is specifically used to: calculate the deviation between the benchmark net value in the deterministic calculation result and the corresponding item in the uncertain prediction result; if the deviation exceeds a preset threshold, trigger the review mechanism and control the data acquisition and allocation module 110 to reacquire deterministic historical data and uncertain evolution data for processing; and generate comprehensive processing results if the deviation is within the preset threshold range.

[0128] In the context of financial asset measurement, the comprehensive processing result generation module 150 is specifically used to: calculate the deviation between the net book value in the classical measurement result and the corresponding item in the quantum measurement result; if the deviation exceeds a preset threshold, trigger a review mechanism to reacquire the book data and multidimensional market data for measurement; and if the deviation is within the preset threshold range, generate a comprehensive financial asset measurement report.

[0129] The process is straightforward: classical and quantum measurement results are retrieved from the overall measurement database, and the book value from the classical measurement results is extracted as a comparison benchmark. Book value refers to the residual value of a company's total assets minus total liabilities at a specific point in time, calculated according to accounting standards. It represents the real value of historically deterministic costs in the classical measurement results. Subsequently, the corresponding item in the quantum measurement results is extracted—the real component obtained after measurement from the quantum state vector model. Numerically, it, like book value, represents the measurement of the historical contractual value of assets. The deviation between book value and the corresponding item is calculated—the absolute value of the difference—to quantify the degree of agreement between classical and quantum measurement in the dimension of deterministic value.

[0130] The calculated deviation is compared with a preset threshold. If the deviation exceeds the preset threshold, it is determined that the real part anchoring of the quantum state vector model is abnormal. This triggers a verification mechanism, which involves re-initiating a request to the external financial information system to obtain updated book data and multidimensional market data. The entire process from data quantum state preparation to quantum operation is then re-executed until the deviation falls back to the preset threshold range, thereby ensuring the robustness of the measurement results.

[0131] If the deviation is within a preset threshold range, the two-state measurement results are considered consistent. The classical measurement results, quantum measurement results, and their deviation explanations are then structurally integrated to generate a comprehensive financial asset measurement report. The preset threshold is a tolerance limit pre-set based on financial regulatory requirements and historical measurement error distribution, used to determine the validity of the measurement results.

[0132] It is worth mentioning that, in the report generation process, this embodiment specifically introduces an equity premium account, a separate accounting item dedicated to recording the portion of asset market value exceeding net book value. Its value is calculated in real-time by the quantum state measurement module using a hybrid artificial intelligence model. This hybrid artificial intelligence model is based on a fusion of Transformer time-series modeling and Long Short-Term Memory (LSTM) networks. Specifically, by integrating multi-source information such as enterprise growth indicators, market transaction data, and industry trend factors, the model utilizes the LTM network in the hybrid artificial intelligence model to extract deep features from historical data, and employs Transformer time-series modeling to capture the real-time evolution of market dynamics, thereby accurately calculating the dynamic market value premium of equity, i.e., the value corresponding to the imaginary component in the quantum state vector model.

[0133] Finally, the generated comprehensive financial asset measurement report is written into a distributed database or blockchain network and returned to the financial intelligent agent system through a standardized data interface API.

[0134] This embodiment incorporates dynamic market value premium as an independent equity premium item into the comprehensive financial asset measurement report. This allows the report to reflect historical costs while explicitly presenting the equity value based on future expectations. This breaks the limitation of traditional accounting statements that can only reflect static book value and achieves a precise depiction of the full-dimensional value of financial assets.

[0135] Based on the above embodiments, the data processing system that combines quantum computing and intelligent computing further includes a quantum benchmark library construction module. The following will take the financial asset measurement scenario as an example to describe the quantum benchmark library construction module in detail.

[0136] The quantum benchmark library construction module is used to: obtain the public attributes of objects, including at least one of benchmark ratio, volatility parameter, tax parameter and price index; map the public attributes to benchmark quantum state parameters in Hilbert space, and assign a unique quantum identifier to each public attribute through quantum phase encoding, thereby constructing a multi-dimensional quantum benchmark library.

[0137] In the context of financial asset measurement, the quantum benchmark library construction module is used to: acquire the public attributes of financial assets, including at least one of benchmark interest rates, exchange rates, tax rates, and consumer price indices; map the public attributes to benchmark quantum state parameters in Hilbert space, and assign a unique quantum identifier to each public attribute through quantum phase encoding, thereby constructing a multi-dimensional financial quantum benchmark library; based on real-time market data streams, dynamically generate benchmark quantum state parameters that adapt to market changes using the quantum Monte Carlo algorithm, and adjust the benchmark quantum state parameters in real time through the superposition evolution mechanism of quantum states to continuously update the financial quantum benchmark library.

[0138] The first step is to obtain the public attributes of the target financial asset, which are fundamental macroeconomic variables that are universally applicable within the financial system and can influence the value of the vast majority of financial assets. These include, but are not limited to, benchmark interest rates reflecting the cost of capital, exchange rates determining cross-border asset value conversion rates, tax rates affecting corporate profitability, and consumer price indices representing inflation. These public attributes constitute the underlying reference system for financial asset valuation, and the accuracy and timeliness of this data directly determine the precision of subsequent quantum metrology.

[0139] Subsequently, the acquired public attributes are mapped one by one to reference quantum state parameters in Hilbert space. Hilbert space is a mathematical concept in quantum mechanics that describes the infinite-dimensional abstract space in which a quantum state resides. The reference quantum state parameters are vector representations that transform classical macroscopic variables into quantum superposition and entanglement operations within this space. In this process, quantum phase encoding technology is used to assign a unique quantum identifier to each reference attribute. Quantum phase encoding is a quantum encoding technique that uses the phase degree of freedom of a quantum state to store and distinguish information. By embedding specific identifier information in the phase dimension of the quantum state, each reference quantum state parameter has unique characteristics in the phase space, thereby establishing a multi-dimensional financial quantum reference library.

[0140] It should be noted that the financial quantum benchmark library in this embodiment is not a classical database stored in tabular form, but a quantized dataset consisting of a series of interrelated benchmark quantum state vector models with specific phase identifiers, which can support subsequent quantum parallel retrieval and computation.

[0141] Preferably, to address the dynamic changes in the financial market, this embodiment continuously updates the financial quantum benchmark library based on real-time market data streams. Specifically, it uses a quantum Monte Carlo algorithm to dynamically generate new benchmark quantum state parameters that adapt to market changes, and adjusts existing benchmark quantum state parameters in real time through the superposition evolution mechanism of quantum states, thereby achieving continuous updates to the financial quantum benchmark library.

[0142] Real-time market data streams refer to various market conditions and policy data that are continuously acquired through financial data interfaces and reflect the latest state of the current market.

[0143] The quantum Monte Carlo algorithm is an advanced numerical computation algorithm based on the characteristics of quantum random walk and quantum parallel sampling. Compared with classical Monte Carlo simulation, it can complete the estimation of high-dimensional integrals and complex probability distributions with a quadratic speed advantage, thereby quickly generating new benchmark quantum states that cover unstructured factors not included in traditional benchmarks, such as interest rate externalities and monetary and fiscal policy effects.

[0144] The superposition state evolution mechanism refers to the physical process in which a quantum state undergoes a continuous and smooth evolution in Hilbert space under the drive of the Hamiltonian, according to the Schrödinger equation. In this embodiment, the real-time market fluctuation information is encoded into a driving Hamiltonian, which causes the reference quantum state parameters to converge to the latest market state along the optimal path, and automatically completes the fine-tuning and correction of the parameters.

[0145] Based on the above, the financial quantum benchmark library constructed in this embodiment can always maintain a high degree of timeliness and accuracy, providing a stable and reliable unified reference benchmark for quantum state measurement, and effectively solving the problem of valuation deviation caused by data lag in traditional measurement systems.

[0146] Based on the above embodiments, the following will take the financial asset measurement scenario as an example to describe the data benchmark calculation and processing module 120 in detail.

[0147] The data benchmark calculation and processing module 120 is specifically used to: perform double accounting processing on deterministic historical data to obtain deterministic calculation results; wherein, the deterministic calculation results include at least one of asset and liability data, profit and loss data and cash flow data prepared in accordance with preset accounting standards.

[0148] In the context of financial asset measurement, the data benchmark calculation and processing module 120 is specifically used to: perform double-entry bookkeeping processing on the book data to obtain classical measurement results; wherein, the classical measurement results include at least one of the balance sheet, income statement and cash flow statement prepared in accordance with accounting standards.

[0149] Specifically, the system automatically identifies transaction categories from accounting data, analyzes the corresponding debit and credit directions and amounts, generates standardized accounting entries containing debit accounts, credit accounts and amounts, and constructs a trial balance based on the accounting equation to ultimately obtain classic measurement results.

[0150] Classical measurement results strictly adhere to the recognition, measurement, and reporting requirements of current corporate accounting standards. Specifically, they are manifested in at least one of the balance sheet, income statement, and cash flow statement prepared in accordance with accounting standards. The balance sheet reflects the financial position of an enterprise at a specific point in time, the income statement reflects the operating results of an enterprise during a certain accounting period, and the cash flow statement reflects the inflow and outflow of cash and cash equivalents of an enterprise during a certain accounting period.

[0151] The generated classical measurement results not only include the net book value of all traditional accounting items, including cash, deposits, bonds, and loans, measured in legal tender, but also correspond to the eigenstate vector of claims in the overall quantum measurement value state. Each component of this vector corresponds to the balance of a debt account.

[0152] Ultimately, classical metrology results are integrated into a distributed database or blockchain network built using sharded consortium blockchains and zero-knowledge proofs, ensuring full data traceability and compliance with the stringent requirements of financial regulators for data integrity. At the same time, classical metrology results are provided to quantum state metrology in real time through a shared overall metrology database.

[0153] Based on the above embodiments, the data processing system that combines quantum computing and intelligent computing further includes a heterogeneous computing power scheduling module. The following will take the financial asset measurement scenario as an example to describe the data benchmark calculation and processing module 120 in detail.

[0154] The heterogeneous computing power scheduling module is used to: call quantum processors, graphics processors or central processing units to perform quantum evolution operations according to the complexity of the current processing task; among them, the task scheduled to the quantum processor has the highest complexity, the task scheduled to the graphics processor has the second highest complexity, and the task scheduled to the central processing unit has the lowest complexity.

[0155] In the context of financial asset measurement, the heterogeneous computing power scheduling module is used to: call quantum processors, graphics processors, or central processing units to perform quantum operations on financial assets based on the complexity of the current measurement task; among them, the task scheduled to the quantum processor has the highest complexity, the task scheduled to the graphics processor has the second highest complexity, and the task scheduled to the central processing unit has the lowest complexity.

[0156] Specifically, task complexity is a metric derived from a comprehensive quantization of the number of qubits in the quantum circuit to be processed, the total number of quantum gate operations, the required coherence time, and the scale of the data dimension. It is used to characterize the intensity of computational resource consumption and the degree of dependence on quantum hardware performance for the current metrology task. Based on the evaluated complexity value, the optimal execution platform is intelligently selected from the preset hierarchical heterogeneous computing architecture.

[0157] For the most complex tasks, such as large-scale portfolio optimization involving thousands of qubits, full-stack risk stress testing requiring deep quantum circuits, or solving complex derivative pricing models, quantum processors are prioritized for computation. Quantum processors are specialized hardware that utilizes the properties of quantum superposition and entanglement to achieve quantum computing, enabling them to handle combinatorial optimization and high-dimensional integral problems that classical computers struggle with, offering exponential speedup advantages.

[0158] For tasks of moderate complexity, such as medium-scale quantum state simulations, training and inference of quantum machine learning models, or prototyping of quantum algorithms, the graphics processing unit (GPU) is prioritized for computation. GPUs are parallel computing chips with tens of thousands of stream processor cores. While they lack the physical quantum advantage in handling quantum logic, their powerful floating-point arithmetic capabilities and high-bandwidth memory enable efficient simulation of small- to medium-scale quantum circuits, providing a viable supplement to computing power in the current stage where quantum hardware is not yet widespread.

[0159] For tasks with the lowest complexity, such as routine quantum state data preprocessing, deviation verification between classical and quantum metrology results, report formatting generation, or system logic control, the central processing unit (CPU) is prioritized for execution. The CPU is the general-purpose computing core of a computer system, adept at handling logically complex but computationally relatively small sequential tasks, ensuring the stable operation of the system's basic business processes.

[0160] This embodiment uses a complexity-based dynamic scheduling mechanism to accurately offload high-value and high-difficulty computing loads to the most suitable computing power units, thereby significantly reducing the overall operating cost of the system while ensuring efficient and accurate measurement of financial assets.

[0161] In some other embodiments, the following will take the financial asset measurement scenario as an example to provide a detailed description of the overall data processing system for parallel quantum computing and intelligent computing provided by the present invention.

[0162] Figure 2 This diagram illustrates the overall system schematic of the data processing system combining quantum computing and intelligent computing, as provided in an embodiment of the present invention, in a financial asset measurement scenario. Figure 2 As shown, in practical applications, the financial intelligent agent system on the right first receives metering requests or automatic business instructions from external users. The financial intelligent agent system parses the requests through its built-in user request acquisition module and automatic business execution module, and then distributes the computational tasks to the intelligent data allocation module via the intelligent agent interface API.

[0163] After receiving the task instruction, the intelligent data allocation module, based on the priority of the business scenario and resource availability, acquires the book data and multi-dimensional market data of the target financial asset from the financial information system on the left via the network data interface API. This high-dimensional book data and market data are then diverted to two parallel processing paths: one enters the classical state measurement module on the left, and the other enters the quantum state measurement module on the right.

[0164] In the classical measurement module, the raw book data undergoes preprocessing of transaction accounting data and standardization processing by the double-entry bookkeeping module, ultimately generating a classical financial asset report reflecting the value of deterministic claims. Simultaneously, in the quantum measurement module, the input data is first calibrated by obtaining real-time quantum benchmark parameters from the quantum financial benchmark server on the left. Subsequently, under the scheduling of the underlying quantum / electronic computing power operation controller, QPU, GPU, or CPU resources are dynamically allocated to execute highly complex quantum algorithm calculations, generating the quantum financial asset report.

[0165] Ultimately, the classic financial asset reports and quantum financial asset reports generated by the two paths are written into the distributed database / blockchain at the bottom, and then logically merged. Through cross-validation, a final comprehensive financial asset measurement report is generated. This final comprehensive financial asset measurement report is then sent back to the financial intelligent agent system on the right via an interface, where it provides visual output to the user or triggers subsequent automated business processes, thus forming a complete intelligent service loop.

[0166] Furthermore, corresponding to the data processing system that combines quantum computing and intelligent computing as described in the above embodiments, the present invention also provides a data processing method that combines quantum computing and intelligent computing, applied to the data processing system that combines quantum computing and intelligent computing.

[0167] Specifically, Figure 3 A schematic flowchart of the data processing method for parallel quantum computing and intelligent computing provided in an embodiment of the present invention is shown.

[0168] like Figure 3 As shown, the method includes: S310, acquiring deterministic historical data and uncertain evolution data of the target object; S320, performing benchmark calculation processing on the deterministic historical data to obtain a deterministic calculation result; S330, encoding the deterministic historical data and the uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation result, and its imaginary part corresponds to the prediction component calculated based on the uncertain evolution data; S340, performing quantum evolution operations on the quantum state vector model to obtain an uncertain prediction result; S350, fusing the deterministic calculation result and the uncertain prediction result to generate a comprehensive processing result.

[0169] In this embodiment, deterministic historical data and uncertain evolution data of the target object are acquired, and the deterministic historical data is processed by benchmark calculation to obtain deterministic calculation results. Simultaneously, the deterministic historical data and uncertain evolution data are encoded into quantum state amplitudes to construct a quantum state vector model. The real part of the quantum state vector model corresponds to the deterministic calculation results, and its imaginary part corresponds to the predicted components calculated based on the uncertain evolution data. Furthermore, quantum evolution operations are performed on the quantum state vector model to obtain uncertain prediction results. Thus, the deterministic calculation results and uncertain prediction results are fused to generate a comprehensive processing result. This method, by employing a hybrid architecture of benchmark calculation and quantum evolution operations, overcomes the exponential computing power bottleneck of classical computing when processing high-dimensional data, compressing the processing time of 10,000-dimensional attribute datasets from minutes to seconds, solving the real-time processing problem in high-dimensional nonlinear scenarios. At the same time, by anchoring the deterministic calculation results to the real part of the quantum state vector model and corresponding the uncertain prediction components to the imaginary part, it balances the compliance of deterministic processing with the accuracy of uncertain prediction, and can adapt to the processing needs of high-dimensional heterogeneous data in multiple fields.

[0170] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a data processing method that combines quantum computing and intelligent computing. This method includes: acquiring deterministic historical data and uncertain evolution data of a target object; performing benchmark calculation processing on the deterministic historical data to obtain a deterministic calculation result; encoding the deterministic historical data and the uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation result, and its imaginary part corresponds to the prediction component calculated based on the uncertain evolution data; performing quantum evolution operations on the quantum state vector model to obtain an uncertain prediction result; and fusing the deterministic calculation result and the uncertain prediction result to generate a comprehensive processing result.

[0171] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the data processing method for parallel quantum computing and intelligent computing provided by the above methods. The method includes: acquiring deterministic historical data and uncertain evolution data of a target object; performing benchmark calculation processing on the deterministic historical data to obtain a deterministic calculation result; encoding the deterministic historical data and the uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation result, and its imaginary part corresponds to the prediction component calculated based on the uncertain evolution data; performing quantum evolution operations on the quantum state vector model to obtain an uncertain prediction result; and fusing the deterministic calculation result and the uncertain prediction result to generate a comprehensive processing result.

[0173] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a data processing method for parallel quantum computing and intelligent computing provided by the methods described above. The method includes: acquiring deterministic historical data and uncertain evolution data of a target object; performing benchmark calculation processing on the deterministic historical data to obtain a deterministic calculation result; encoding the deterministic historical data and the uncertain evolution data into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation result, and its imaginary part corresponds to a prediction component calculated based on the uncertain evolution data; performing quantum evolution operations on the quantum state vector model to obtain an uncertain prediction result; and fusing the deterministic calculation result and the uncertain prediction result to generate a comprehensive processing result.

[0174] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0175] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing system that combines quantum computing and intelligent computing, characterized in that, include: The data acquisition and allocation module is used to acquire deterministic historical data and uncertain evolution data of the target object, and allocate the deterministic historical data and uncertain evolution data to the data benchmark calculation and processing module and the data quantum state vector model construction module; The data benchmark calculation and processing module is used to perform benchmark calculation processing on the deterministic historical data to obtain deterministic calculation results; The data quantum state vector model construction module processes data in parallel with the data benchmark calculation and processing module. It encodes the deterministic historical data and the uncertain evolution data into quantum state amplitudes to construct a quantum state vector model. The real part of the quantum state vector model corresponds to the deterministic calculation result, and its imaginary part corresponds to the prediction component calculated based on the uncertain evolution data. The quantum evolution operation module is used to perform quantum evolution operations on the quantum state vector model to obtain uncertainty prediction results; The comprehensive processing result generation module is used to fuse the deterministic calculation results and the uncertain prediction results to generate a comprehensive processing result.

2. The data processing system for parallel quantum computing and intelligent computing according to claim 1, characterized in that, The data quantum state vector model construction module is specifically used for: Based on the object attributes characterized by the deterministic historical data and the uncertain evolution data, the corresponding reference quantum state parameters are retrieved from the pre-constructed quantum reference library; The deterministic historical data and the uncertain evolution data are loaded into a quantum register by amplitude encoding to construct an initial quantum state vector model; Based on the historical correlation matrix, an entanglement initialization circuit is applied to the initial quantum state vector model to establish quantum entanglement relationships between different object attributes, thereby obtaining the correlated quantum state vector. The reference quantum state parameters are bound to the associated quantum state vector model by coupling operators, and the real part of the associated quantum state vector model is mapped to the deterministic calculation result to construct the quantum state vector model.

3. The data processing system for parallel quantum computing and intelligent computing according to claim 1, characterized in that, The quantum evolution operation module is specifically used for: Based on a preset data processing scenario, an inner product operation is performed on the quantum state vector model and the quantum state corresponding to the benchmark configuration. The quantum state overlap between the two is quantized according to the obtained inner product modulus to obtain a relative value measurement value. Perform tensor product spatial projection operation on the quantum state vector model, and quantify the synergistic integration effect between objects based on the obtained inner product projection value; Joint operations are performed on the quantum state vector models of multiple related objects, and the linear and nonlinear correlations of the attributes of multiple object classes are measured based on the obtained inner product magnitude and phase. A quantum metrology operator is applied to the quantum state vector model, and the systemic risk transmission path is identified based on the obtained operator eigenvalues ​​and spectral decomposition results.

4. The data processing system for parallel quantum computing and intelligent computing according to claim 3, characterized in that, The quantum evolution operation module is also used for: The inner product modulus deviation between the quantum state vector model and the quantum state corresponding to the reference configuration is calculated. Based on the obtained deviation value, the degree of deviation of the object combination relative to the reference state is evaluated, thereby realizing the measurement of the dynamic changes of the object. The preset object state transition operation is mapped to a linear operator on the Hilbert space, and the estimated result of the object state transition operation is obtained by executing the operation corresponding to the linear operator. Define a complete transition rule from the input object state to the output object state, and ensure the verifiability of the object state transition operation by verifying the reproducibility of the complete transition rule.

5. The data processing system for parallel quantum computing and intelligent computing according to claim 3, characterized in that, The quantum evolution operation module is also used for: Encode at least one of the following terms in the preset contract: triggering conditions, payment function, and conversion rights, into a preset structural position in the operator matrix to obtain a quantum operator containing the contract's logical features, thereby achieving quantum executable encoding of contract terms. The pre-defined engineering process is broken down into a sequential product of basic quantum unitary operators. By standardizing the combination of these basic quantum unitary operators, modular quantum circuits are generated to improve the efficiency of quantum measurement and verification in engineering design.

6. The data processing system for parallel quantum computing and intelligent computing according to claim 1, characterized in that, The comprehensive processing result generation module is specifically used for: Calculate the deviation between the benchmark net value in the deterministic calculation result and the corresponding item in the uncertain prediction result; If the deviation exceeds a preset threshold, a review mechanism is triggered, controlling the data acquisition and allocation module to reacquire deterministic historical data and uncertain evolution data for processing. The comprehensive processing result is generated when the deviation is within a preset threshold range.

7. The data processing system for parallel quantum computing and intelligent computing according to claim 2, characterized in that, It also includes a quantum benchmark library building block for: Obtain the public attributes of the object, which include at least one of the following: benchmark ratio, volatility parameter, tax parameter, and price index; The common attributes are mapped to reference quantum state parameters in Hilbert space, and each common attribute is given a unique quantum identifier through quantum phase encoding, thus constructing a multi-dimensional quantum reference library.

8. The data processing system for parallel quantum computing and intelligent computing according to claim 7, characterized in that, The quantum benchmark library building module is also used for: Based on real-time data streams, a reference quantum state parameter adapted to data changes is dynamically generated using a quantum Monte Carlo algorithm. The reference quantum state parameter is then adjusted in real time through a superposition evolution mechanism of quantum states to continuously update the quantum reference library.

9. The data processing system for parallel quantum computing and intelligent computing according to claim 1, characterized in that, The data benchmark calculation and processing module is specifically used for: The deterministic historical data is subjected to complex accounting processing to obtain the deterministic calculation result; The deterministic calculation results include at least one of the following: asset and liability data, profit and loss data, and cash flow data prepared in accordance with preset accounting standards.

10. The data processing system for parallel quantum computing and intelligent computing according to claim 2, characterized in that, It also includes a data preprocessing module, used for: In the case of missing values ​​in the deterministic historical data and / or the uncertain evolution data, quantum interpolation is performed on the missing values, and multiple interpolation schemes are evaluated simultaneously using quantum parallelism; The missing values ​​are filled using the optimal interpolation scheme, and the filled data is sent to the data quantum state vector model construction module.

11. The data processing system for parallel quantum computing and intelligent computing according to claim 2, characterized in that, It also includes a quantum state filtering module, used for: A noise filtering operator is applied to the quantum state vector model, and quantum Fourier transform, frequency domain filtering, and inverse quantum Fourier transform are performed sequentially to filter out noise in the quantum state evolution process.

12. The data processing system for parallel quantum computing and intelligent computing according to claim 1, characterized in that, It also includes a heterogeneous computing power scheduling module, used for: Depending on the complexity of the current processing task, the quantum processor, graphics processor, or central processing unit is invoked to execute the quantum evolution operation; Among them, the task scheduled to the quantum processor has the highest complexity, the task scheduled to the graphics processor has the second highest complexity, and the task scheduled to the central processing unit has the lowest complexity.

13. The data processing system for parallel quantum computing and intelligent computing according to claim 1, characterized in that, It also includes a storage module for storing the deterministic calculation results, the uncertain prediction results, and the comprehensive processing results in a distributed database, which is constructed using a sharded consortium blockchain and zero-knowledge proofs.

14. The data processing system for parallel quantum computing and intelligent computing according to claim 1, characterized in that, The data acquisition and allocation module is specifically used for: In response to a received data processing service request, the deterministic historical data and the uncertain evolution data are obtained from an external information system through a network data interface; The network data interface employs quantum security technology and post-quantum cryptography to encrypt the data transmission process.

15. A data processing method for parallel quantum computing and intelligent computing, applied to the data processing system for parallel quantum computing and intelligent computing as described in any one of claims 1-14, characterized in that, include: Obtain deterministic historical data and uncertain evolution data of the target object; The deterministic historical data is subjected to benchmark calculation processing to obtain deterministic calculation results; The deterministic historical data and the uncertain evolution data are encoded into quantum state amplitudes to construct a quantum state vector model; the real part of the quantum state vector model corresponds to the deterministic calculation result, and its imaginary part corresponds to the prediction component calculated based on the uncertain evolution data; Quantum evolution operations are performed on the quantum state vector model to obtain uncertainty prediction results; The deterministic calculation results and the uncertain prediction results are combined to generate a comprehensive processing result.

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