Quantum cognitive simulation system and method based on classical calculation

Abstract

The invention discloses a quantum cognitive simulation system and method based on classical computation, and belongs to the technical field of quantum computation.A data processing module receives original data of external data input equipment, converts the original data into quantum state data after processing, and transmits the quantum state data to a quantum cognitive simulation computation module; the quantum cognitive simulation calculation module performs quantum cognitive simulation calculation by using a CPU cluster, a GPU array and a coprocessor, and stores a result in the storage module or directly transmits the result to the output module; and the output module performs format conversion on the calculation result and outputs the result to external display / control equipment. The quantum cognitive simulation system and method based on classical calculation aim at overcoming the defects of a traditional cognitive model in the aspects of processing decision fuzziness, context dependency and high-dimensional calculation efficiency, the effects of quantum superposition, interference, entanglement and the like can be efficiently simulated, quantitative calculation of related functions of human consciousness is achieved, and the quantum cognitive simulation system and method based on classical calculation can be applied to the field of quantum computing. And the performance in an actual application scene is optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quantum computing, in particular to a quantum cognitive simulation system and method based on classical computing. BACKGROUND

[0002] The traditional cognitive model has the following defects: 1. Insufficient processing of decision ambiguity: Traditional cognitive models (such as Bayesian networks) are based on classical probability theory and can only output a single probability result, which cannot effectively simulate the ambiguity and uncertainty that exist in human decision-making processes. For example, when faced with complex situations, humans may simultaneously have inclinations towards multiple options, while classical models can only output a single "optimal" decision, failing to express superposition state of cognition such as "simultaneously inclined to A and B", leading to distortion of the simulation of real decision-making behavior.

[0003] 2. Low efficiency in processing context dependency: Traditional cognitive models usually need to manually preset a large number of parameters or manually adjust the model structure to adapt to different contexts when dealing with context dependency problems. For example, in natural language processing, semantic understanding changes with changes in the context, and classical models need to rely on artificial rules or experience to adjust parameters to achieve certain context adaptation, which not only consumes a lot of manpower and time cost, but also is difficult to cope with dynamic changes in complex contexts, resulting in insufficient model generalization ability.

[0004] 3. Low computational efficiency for high-dimensional problems: When dealing with high-dimensional problems, the computational complexity of traditional cognitive models increases exponentially, leading to a sharp decline in computational efficiency. For example, in multi-factor decision-making scenarios, as the dimensionality of variables increases, the size of the conditional probability table of Bayesian networks rapidly expands, making inference calculations time-consuming and even impossible to complete, severely limiting the application of models in complex high-dimensional scenarios and failing to meet the requirements of real-time decision-making tasks.

[0005] Therefore, the present application designs a quantum cognitive simulation system and method based on classical computing to solve the above problems. SUMMARY

[0006] The present application aims to solve the deficiencies of traditional cognitive models in dealing with decision ambiguity, context dependency and high-dimensional computational efficiency, and proposes a quantum cognitive simulation system and method based on classical computing, which can efficiently simulate quantum superposition, interference and entanglement effects, realize the quantitative calculation of human consciousness-related functions, and optimize the performance in practical application scenarios.

[0007] To achieve the above purpose, the present application realizes the following technical solutions: A quantum cognitive simulation system based on classical computing includes a data processing module, a quantum cognitive simulation computing module, a storage module, and an output module, which work together to realize quantum cognitive simulation functions. The data processing module receives raw data from an external data input device, and after processing, converts the data into quantum state data and transmits it to the quantum cognitive simulation computing module. The quantum cognitive simulation computing module uses CPU clusters, GPU arrays, and coprocessors to perform interference term calculation, Monte Carlo sampling simulation entanglement, tensor network optimization, attention mechanism calculation, decision ambiguity processing, dynamic memory retrieval, and quantum state measurement. The quantum cognitive simulation computing module stores the results in the storage module or directly transmits them to the output module. The output module converts the calculation results into a format and outputs them to an external display / control device.

[0008] Further, the data processing module is composed of a data acquisition submodule and a data encoding submodule, and the data acquisition submodule is connected to the external data input device and the data encoding submodule. The data acquisition submodule is responsible for real-time data acquisition from different application scenarios, and the data encoding submodule converts the acquired data into quantum state format represented by complex probability amplitude.

[0009] Further, the quantum cognitive simulation computing module is composed of CPU clusters, GPU arrays, and coprocessors, and the CPU clusters, GPU arrays, and coprocessors are connected through a high-speed interconnection bus. The GPU array performs tensor network optimization and Monte Carlo sampling simulation entanglement tasks in parallel, and the coprocessor is used to accelerate interference term calculation, quantum state measurement algorithm, and consciousness function module calculation.

[0010] Further, the consciousness function module calculation includes attention mechanism calculation, decision ambiguity processing, dynamic memory retrieval, and quantum state measurement.

[0011] Further, the storage module uses a storage architecture combining SSD arrays and RAM.

[0012] Further, the output module includes a data format conversion submodule and an output interface submodule. The data format conversion submodule is connected to the quantum cognitive simulation computing module through an internal data bus, and the output interface submodule is connected to the data format conversion submodule and an external device. The data format conversion submodule receives the result data output by the quantum cognitive simulation computing module and converts it into a format that meets the requirements of the application scenario. The output interface submodule outputs the processed data to an external device.

[0013] In order to better achieve the purpose of the present application, the present application also provides a quantum cognitive simulation method using the quantum cognitive simulation system based on classical computing, which includes the following steps: 1) Data input and preprocessing: receiving data from different application scenarios, converting the above data into a format suitable for quantum cognitive simulation, specifically encoding the data into a quantum state form represented by a complex probability amplitude; 2) Quantum cognitive model calculation, including quantum state initialization, interference term calculation using quantum effect simulation algorithms, Monte Carlo sampling simulation entanglement and tensor network optimization; consciousness function module calculation, including attention mechanism calculation, decision ambiguity processing, dynamic memory retrieval and quantum state measurement; 3) Result output and post-processing: measuring the quantum state results obtained by quantum cognitive model calculation, collapsing the quantum state into a classical probability distribution or specific decision result; post-processing the output results.

[0014] Further, interference term calculation: based on complex probability amplitude, the interference term between different decision paths or cognitive states is calculated by the following interference term formula: I=2·∣Ψ1∣·∣Ψ2∣·cos(θ) Where θ is the phase difference between the two path probability amplitudes, and Ψ1 and Ψ2 represent the complex probability amplitudes of the two paths.

[0015] Further, Monte Carlo sampling simulation entanglement: using Monte Carlo sampling algorithm to introduce non-local correlation, generating sample data with entanglement characteristics.

[0016] Further, tensor network optimization: for high-dimensional data processing, use matrix product state or tree-shaped tensor network to decompose and compress high-dimensional quantum states.

[0017] Compared with the prior art, the present application has the following advantages: 1. Breakthrough in decision ambiguity limitation, improve simulation fidelity. The present application represents the decision state as a superposition state through complex probability amplitude and interference term calculation, allowing the system to consider multiple option tendencies at the same time (such as "simultaneously inclined to A and B"), and dynamically adjusting the weight of each option through the interference term. For example, in a complex investment decision scenario, traditional models can only give a single investment plan, while the present application can simulate the simultaneous consideration of multiple risk-reward combinations by the decision maker, and through the interference term to simulate the mutual influence between different decision paths, making the simulation result closer to human real decision behavior.

[0018] 2. Automatically adapt to context changes and reduce labor costs. The present application uses quantum interference effect to automatically correct context dependence: by encoding context information as quantum state phase difference, the interference term automatically adjusts the decision path weight according to the phase difference, without human intervention. For example, in natural language processing, when faced with sentences whose semantics change dynamically with context, the system can automatically adjust the semantic understanding path according to the context, avoiding the need for manual pre-setting of a large number of rules.

[0019] 3. High-dimensional calculation efficiency is significantly improved, and the application boundary is expanded. The application adopts tensor network compression technology, decomposes high-dimensional quantum states into low-rank tensor chains, and reduces the calculation complexity from exponential order to polynomial order.

[0020] 4. Realize the quantitative calculation of consciousness function. The application innovatively disassembles consciousness into computable modules such as attention, decision fuzziness, memory retrieval, etc.: the focus switching of attention is simulated through quantum measurement, the decision fuzziness is represented by quantum superposition state, and the memory correlation retrieval is realized based on quantum entanglement. For example, in a psychology experiment simulation, the system can quantitatively analyze the differences in attention allocation of individuals in different emotional states, providing a reproducible calculation model for cognitive science research and filling the gap in the quantitative research of consciousness function by traditional methods.

[0021] 5. Compatible with classical computing platform, reduce deployment threshold. The application simulates quantum effects through classical algorithms (such as Monte Carlo sampling simulation of entanglement, complex probability amplitude instead of quantum state), which can realize efficient calculation on ordinary CPU / GPU clusters without relying on quantum hardware. Taking the deployment of an AI dialogue system as an example, the hardware cost of the application scheme is only 1 / 10 of the quantum computing scheme, and the deployment period is shortened from several months to several weeks, significantly reducing the technical landing threshold and accelerating the commercialization of quantum cognitive technology in multiple fields. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 is a structural diagram of a quantum cognitive simulation system based on classical computing of the present application; Figure 2 is a flowchart of a quantum cognitive simulation method based on classical computing of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] Embodiment one: please refer to the drawings of the specification Figure 1The application discloses a quantum cognitive simulation system based on classical calculation, which comprises a data processing module, a quantum cognitive simulation calculation module, a storage module and an output module. The data processing module is modularly designed and comprises a data acquisition submodule and a data coding submodule. The data acquisition submodule is equipped with various data interfaces, such as a USB interface, an Ethernet interface and a wireless communication interface, and is used for connecting different types of data input devices; the data coding submodule is constructed based on an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit) chip and realizes an efficient data coding algorithm. The data acquisition submodule is connected with external data input devices through corresponding interfaces; and the data acquisition submodule and the data coding submodule are connected through an internal high-speed data bus and are integrated on a same printed circuit board (PCB) to reduce data transmission delay.

[0026] The data acquisition submodule is responsible for real-time acquisition of data from different application scenarios, such as individual behavior data and questionnaire score data in a psychological state prediction scenario, text data input by a user in an AI dialogue system, and image, radar and laser radar data collected by sensors in an automatic driving scenario; and the data coding submodule converts the collected data into a quantum state format represented by a complex probability amplitude to provide basic data for subsequent quantum cognitive simulation calculation. For example, for text data, each word is mapped to a high-dimensional vector through a word vector model, and then the vector elements are converted into complex probability amplitudes; and for sensor data, the measurement values of different types of sensors are respectively coded into real and imaginary part parameters of quantum states.

[0027] The quantum cognitive simulation calculation module comprises a CPU cluster, a GPU array and a coprocessor. According to the quantum state data output by the data processing module, the CPU cluster coordinates the work flow of each computing resource and is used for executing logical control and general computing tasks; the GPU array performs parallel tensor network optimization, Monte Carlo sampling simulation entanglement tasks and quickly processes high-dimensional data; and the coprocessor (such as an FPGA accelerator) is used for accelerating interference term calculation, quantum state measurement and other algorithms, realizing efficient simulation of quantum superposition, interference and entanglement effects, and performing consciousness function module calculation. The CPU cluster, the GPU array and the coprocessor are connected through a high-speed interconnection bus (such as a PCI-Express bus), are arranged in a same computing node case and are guaranteed to stably operate through a heat dissipation system.

[0028] The storage module employs a storage architecture combining solid-state drive (SSD) arrays and random access memory (RAM). The SSD arrays are used for long-term storage of system programs, training data, and historical simulation results; the RAM is used for temporary storage of intermediate data during computation, such as quantum state parameters and tensor network node data. The SSD arrays connect to the system motherboard via SATA or NVMe interfaces; the RAM is directly installed in the motherboard's memory slots and communicates at high speed with computing modules such as the CPU and GPU via the memory bus.

[0029] During system operation, the storage module provides data storage support for quantum cognitive simulation calculations, ensuring rapid data reading and writing, and guaranteeing the continuity and efficiency of the computation process. For example, in tensor network calculations, it enables rapid reading and storage of tensor node data, avoiding the impact of data transmission latency on computational performance.

[0030] The output module comprises a data format conversion submodule and an output interface submodule. The data format conversion submodule, implemented using software algorithms, can convert quantum cognitive simulation results into different formats. The output interface submodule is equipped with various output interfaces, such as a display interface, a network interface, and a control signal output interface. The data format conversion submodule is connected to the quantum cognitive simulation calculation module via an internal data bus. The output interface submodule is connected to the data format conversion submodule and also connects to external devices through corresponding interfaces, such as connecting to a display to output visual results or transmitting data to other systems via a network interface.

[0031] The data format conversion submodule receives the output data from the quantum cognitive simulation calculation module and converts it into a format that meets the requirements of the application scenario, such as converting the probability distribution results into visual chart data; the output interface submodule outputs the processed data to external devices to realize the display, transmission or control functions of the results.

[0032] Example 2: Figure 2 As shown, a quantum cognition simulation method based on classical computing, using a classical computing platform, simulates quantum cognitive effects through specific algorithms and data processing procedures, specifically including the following steps: 1) Data input and preprocessing: Receive data from different application scenarios, such as individual behavior data and questionnaire score data in the mental state prediction scenario; user input text data in the AI dialogue system; image, radar, lidar, etc. data collected by sensors in the autonomous driving scenario; convert the above data into a format suitable for quantum cognitive simulation, specifically encode the data into a quantum state form represented by a complex probability amplitude. For example, for text data, map each word to a high-dimensional vector through a word vector model, and then convert the vector elements to complex probability amplitudes; for sensor data, encode the measurement values of different types of sensors into the real and imaginary parts of the quantum state parameters.

[0033] 2) Quantum cognitive model calculation, including the following steps: 2.1) Quantum state initialization: Initialize the quantum state according to the characteristics of the input data and the requirements of the application scenario. For example, in a multiple-choice decision-making scenario, assign an initial complex probability amplitude to each decision option to construct an initial quantum superposition state.

[0034] Replace the traditional Bayesian probability P with a complex probability amplitude Ψ = a + bi, where a, b ∈ R, and define the probability as |Ψ| = a2+ b2. 2 =a 2 +b 2 . Where a represents the real part of the probability amplitude, reflecting the classical probability component of the current path or cognitive state; b represents the imaginary part of the probability amplitude, used to describe non-classical features such as interference and context sensitivity; i is the imaginary unit, satisfying i2=-1. By introducing the imaginary part bi, the quantum superposition state is naturally represented.

[0035] 2.2) Apply quantum effect simulation algorithms to calculate interference terms, simulate entanglement through Monte Carlo sampling, and optimize tensor networks: Interference term calculation: Based on the complex probability amplitude, calculate the interference term between different decision paths or cognitive states through the interference term formula.

[0036] The interference term formula is: I=2·∣Ψ1∣·∣Ψ2∣·cos(θ) Where θ is the phase difference between the two path probability amplitudes, dynamically adjusting the weights of each state to simulate quantum interference effects and automatically correct context dependence. Used to describe the degree of interference between two cognitive states or decision paths; Ψ1, Ψ2 represent the complex probability amplitudes of the two paths, and their moduli correspond to the weights or tendencies of each path in the quantum superposition state. By controlling θ and the amplitudes of Ψ1 and Ψ2, the influence of different contexts or cognitive preferences on the final decision result can be simulated.

[0037] Monte Carlo sampling simulates entanglement: For scenarios that require simulation of quantum entanglement, Monte Carlo sampling algorithm is used to introduce non-local correlation. By setting the number of samples and related parameters, the number of samples is usually set to 10²~10 5 within a certain range, which is dynamically adjusted according to the simulation accuracy requirement; related parameters include: particles involved in entanglement simulation, correlation strength parameter (correlation_strength) for controlling non-locality, optional initial probability distribution form (initial_state_dist) and disturbance noise level (noise_level) etc.; after sampling, the entanglement degree is quantitatively evaluated by mutual information or entanglement entropy, etc. Sample data with entanglement characteristics is generated for subsequent calculation and decision-making. Mutual information is used as the quantitative entanglement metric, and the larger the value, the stronger the entanglement degree.

[0038] An example of the basic form of Monte Carlo sampling entanglement simulation in the prior art is as follows: def quantum_entanglement_sampling(particles, iterations=1000): """Use Monte Carlo method to simulate entanglement effect between particles""" samples = [] for _ in range(iterations): # Generate samples with non-local correlation sample = correlated_sampling(particles) samples.append(sample) # Calculate entanglement metric (such as mutual information) entanglement_metric = calculate_mutual_information(samples) return entanglement_metric Tensor network optimization: For high-dimensional data processing, construct a tensor network structure. Use matrix product state (MPS) or tree tensor network (TTN) technology to decompose and compress high-dimensional quantum states, reduce the computational complexity from exponential to polynomial, and improve the calculation efficiency. In the calculation process, through tensor contraction operation, the expected value and related statistics of quantum state are calculated efficiently.

[0039] An example of the tensor network structure in the prior art is as follows: def tensor_network_compression(quantum_state, max_bond_dim=16): """Use matrix product state (MPS) to compress high-dimensional quantum states""" # Decompose the N-dimensional quantum state into a tensor chain mps = matrix_product_state(quantum_state, max_bond_dim=max_bond_dim) # Efficiently compute expectation values by tensor contraction expectation_value = contract(mps) return expectation_value 2.3) Perform consciousness function module calculation, including attention mechanism calculation, decision ambiguity processing, dynamic memory retrieval, and quantum state measurement; Attention mechanism calculation: According to the input data and the current cognitive state, apply quantum measurement theory to perform attention mechanism calculation. Define attention operator , by measuring the quantum state, the cognitive state collapses to a specific attention dimension, so as to realize the selective processing of key information.

[0040] In the present invention, the attention mechanism is re-modeled as a quantum measurement process, by constructing a measurement operator related to the cognitive focus, projecting the input quantum state, and realizing the selective enhancement of key information. This mechanism simulates the attention focus switching process in human cognition, which is different from the "weight weighting" method in traditional deep learning, and has higher information selectivity and interpretability. The quantum measurement-based attention mechanism method includes the following steps: 1、Convert the input information into a quantum state represented by a complex probability amplitude; 2、Determine the attention dimension according to the task focus, and construct the corresponding measurement operator; 3、Apply the measurement operator to the quantum state to generate a new measured state; 4、Use the measured state for downstream cognitive computation or information processing tasks.

[0041] Attention mechanism example as follows: def quantum_attention(state, focus_dimension): """Implement quantum attention mechanism to selectively enhance information processing in specific dimensions""" # Construct attention measurement operator attention_operator = construct_measurement_operator(focus_dimension) # Perform quantum measurement post_measurement_state = measure(state, attention_operator) return post_measurement_state Decision ambiguity handling: Utilize quantum superposition states to represent ambiguity in decision-making processes. Through interference term calculations and quantum state evolution, simulate the dynamic competition of decision paths.

[0042] At the decision output stage, through quantum state measurement, obtain the final decision result, which can reflect the uncertainty and multi-option inclination in the decision-making process, integrating dynamic weight initialization, parameterized Hamiltonian evolution, POVM measurement, and a simple feedback learning framework. Including the following steps: 1. Quantum state initialization: Represent decision options as quantum superposition states, with each option corresponding to a probability amplitude. 2. Hamiltonian construction: Construct a Hamiltonian based on context parameters to describe the system evolution law. 3. Quantum state evolution: Evolve the initial state using the constructed Hamiltonian. 4. POVM measurement: Use positive operator value measurement (POVM) to simulate the uncertainty of the decision result. 5. Feedback learning: Adjust model parameters based on feedback after decision-making to achieve learning and optimization.

[0043] For example, in a multi-option decision: python run def quantum_decision_making(options, context): """Simulate a decision-making process with ambiguity""" # Initialize the quantum superposition state of options superposition_state = initialize_superposition(options) # Apply context-dependent Hamiltonian evolution evolved_state = evolve(superposition_state, context) # Measure the final decision decision = measure(evolved_state) return decision def initialize_superposition(options, prior_weights=None): """ Initialize a superposition state, options is a list of options, prior_weights is the prior weight (probability amplitude) """ n = len(options) if prior_weights is None: prior_weights = np.ones(n) / np.sqrt(n) # Uniform superposition, normalized probability amplitude else: # Normalize the probability amplitude vector (the sum of the squares of the probability amplitudes is 1) prior_weights = np.array(prior_weights, dtype=np.complex128) norm = np.linalg.norm(prior_weights) prior_weights / = norm return prior_weights # Quantum state represented as a complex probability amplitude array def construct_hamiltonian(context_params, n): """ Construct Hamiltonian, parameterized design, context_params is the environmental context parameter Here we use a simple diagonal matrix + perturbation, and the real learning algorithm can be optimized """ base_energy = np.diag(np.linspace(0, 1, n)) # Base energy level perturbation = context_params.get('perturbation', 0.1) * (np.ones((n,n)) - np.eye(n)) hamiltonian = base_energy + perturbation return hamiltonian def evolve_state(state, hamiltonian, time=1.0): """ State evolution: |psi(t)> = exp(-iHt) |psi(0)> """ U = expm(-1j * hamiltonian * time) # Evolution operator evolved_state = U @ state return evolved_state def perform_povm_measurement(state, povm_elements): """ Simulate decision output using POVM measurement povm_elements are n positive operators summing to identity Return measurement outcome index """ probabilities = np.array([np.real(state.conj().T @ E @ state) forE in povm_elements]) probabilities = np.clip(probabilities, 0, 1) probabilities / = probabilities.sum() decision = np.random.choice(len(povm_elements), p=probabilities) return decision, probabilities def update_model_feedback(prior_weights, hamiltonian, decision,reward, learning_rate=0.1): """ Simple feedback adjustment mechanism reward is positive or negative incentive after decision For adjusting prior weights and Hamiltonian parameters (here only weight adjustment is exemplified) """ # Simplified version of adjusting probability amplitude size according to reward adjustment = learning_rate * reward prior_weights[decision] += adjustment # Renormalize prior_weights / = np.linalg.norm(prior_weights) # Hamiltonian learning can be done using gradient methods, omitted here return prior_weights, hamiltonian def quantum_decision_making(options, context_params, prior_weights=None, povm_elements=None, feedback=None): """ Improved version of quantum decision-making function """ n = len(options) # Initialize superposition state state = initialize_superposition(options, prior_weights) # Construct Hamiltonian H = construct_hamiltonian(context_params, n) # State evolution evolved_state = evolve_state(state, H, time=context_params.get('evolution_time', 1.0)) # If no POVM measurement elements are provided, use simple orthogonal projection if povm_elements is None: povm_elements = [np.zeros((n,n)) for _ in range(n)] for i in range(n): povm_elements[i][i,i] = 1 # simple projection measurement operator # Measurement decision decision, probabilities = perform_povm_measurement(evolved_state, povm_elements) # Optional feedback learning if feedback is not None: prior_weights, H = update_model_feedback(state, H, decision, feedback) return options[decision], probabilities Dynamic memory retrieval: Store memory content as quantum states , by constructing quantum entanglement relationships, achieve efficient memory retrieval based on correlation. When you need to retrieve memory, entangle the query information with the memory quantum state, and extract the relevant memory content by measurement. Including the following steps: 1、Quantum state preparation: Encode query information into quantum state; 2、Quantum entanglement operation: Entangle the query quantum state with each memory quantum state; 3、Measurement and scoring: Measure the entangled state to get the relevance score of each memory; 4、Result selection: Select the memory with the highest score as the retrieval result; 5、Feedback adjustment: Update the entanglement parameters and memory state according to the feedback information.

[0044] Retrieval process example as follows: def quantum_memory_retrieval(query, memory_states, entangle_params, feedback=None): """ Quantum dynamic memory retrieval - query: Query information encoded as a quantum state - memory_states: Set of quantum states corresponding to memory content - entangle_params: Entanglement operation parameters - feedback: Optional, feedback adjustment after retrieval """ 1. Encode the query state (assuming it has already been encoded as query) q_state = query 2. Entangle the query state with each memory state in sequence entangled_states = [] for m_state in memory_states: entangled = entangle_operator(q_state, m_state, entangle_params) entangled_states.append(entangled) 3. Measure all entangled states to obtain relevance scores scores = [measure(entangled) for entangled in entangled_states] 4. Select the memory corresponding to the highest score best_idx = np.argmax(scores) retrieved_memory = memory_states[best_idx] 5. Adjust parameters based on feedback (if any) if feedback: entangle_params = update_entangle_params(entangle_params,feedback) memory_states = update_memory_states(memory_states, feedback) return retrieved_memory,scores 3) Result output and post-processing: Perform measurement operations on the quantum state results obtained by the quantum cognitive model, collapsing the quantum state into a classical probability distribution or specific decision results. For example, in psychological state prediction, output the probabilities of different psychological states. According to the requirements of the application scenario, post-process the output results, such as format conversion, result interpretation, etc., to facilitate practical application and user understanding.

[0045] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements will 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 application.

Claims

1. A classical-computation-based quantum cognitive simulation system, characterized by, The quantum cognitive simulation system comprises a data processing module, a quantum cognitive simulation calculation module, a storage module and an output module, and the modules work cooperatively to realize the quantum cognitive simulation function. The data processing module receives original data from an external data input device, and converts the data into quantum state data after processing, and transmits the quantum state data to the quantum cognitive simulation calculation module. The quantum cognitive simulation calculation module utilizes a CPU cluster, a GPU array and a coprocessor to perform interference term calculation, Monte Carlo sampling simulation entanglement, tensor network optimization, attention mechanism calculation, decision ambiguity processing, dynamic memory retrieval and quantum state measurement. The quantum cognitive simulation calculation module stores the results in the storage module or directly transmits the results to the output module. The output module converts the calculation results into a format and then outputs the results to an external display / control device.

2. The classical computation based quantum cognitive simulation system according to claim 1, wherein, The data processing module comprises a data acquisition sub-module and a data encoding sub-module, and the data acquisition sub-module is connected with the external data input device and the data encoding sub-module. The data encoding sub-module converts the collected data into quantum state data in the form of complex probability amplitude.

3. The classical computation based quantum cognitive simulation system of claim 1, wherein, The quantum cognitive simulation calculation module comprises a CPU cluster, a GPU array and a coprocessor, and the CPU cluster, the GPU array and the coprocessor are connected through a high-speed interconnection bus.

4. The classical computation based quantum cognitive simulation system of claim 1, wherein, The GPU array performs tensor network optimization and Monte Carlo sampling simulation entanglement in parallel, and the coprocessor is used to accelerate interference term calculation, quantum state measurement algorithm and consciousness function module calculation.

5. The classical computation based quantum cognitive simulation system of claim 1, wherein, The consciousness function module calculation comprises attention mechanism calculation, decision ambiguity processing, dynamic memory retrieval and quantum state measurement.

6. The classical computation based quantum cognitive simulation system of claim 1, wherein, The storage module adopts a storage architecture combining an SSD array and RAM. The output module comprises a data format conversion sub-module and an output interface sub-module, the data format conversion sub-module is connected with the quantum cognitive simulation calculation module through an internal data bus, and the output interface sub-module is connected with the data format conversion sub-module and an external device.

7. A quantum cognitive simulation method using the quantum cognitive simulation system based on classical computation according to any one of claims 1 to 6. The data format conversion sub-module receives the result data output by the quantum cognitive simulation calculation module, and converts the result data into a format meeting the requirements of an application scenario. The steps comprise the following steps: 1) data input and preprocessing: receiving data from different application scenarios, and converting the data into a format suitable for quantum cognitive simulation, specifically, encoding the data into quantum state data in the form of complex probability amplitude; 2) quantum cognitive model calculation, comprising quantum state initialization, interference term calculation based on quantum effect simulation algorithm, Monte Carlo sampling simulation entanglement and tensor network optimization, consciousness function module calculation, including attention mechanism calculation, decision ambiguity processing, dynamic memory retrieval and quantum state measurement; 8. The quantum cognitive simulation method of claim 7, wherein, 3) result output and post-processing: performing measurement operation on the quantum state result obtained by the quantum cognitive model calculation, and collapsing the quantum state into a classical probability distribution or a specific decision result; and post-processing the output result. Interference term calculation: based on complex probability amplitude, the interference term between different decision paths or cognitive states is calculated through the following interference term formula: Where θ is the phase difference between the two path probability amplitudes, and Ψ1, Ψ2 represent the complex probability amplitudes of the two paths, respectively.

9. The quantum cognitive simulation method of claim 7, wherein, Monte Carlo sampling simulates entanglement: Monte Carlo sampling algorithm is used to introduce non-local correlation to generate sample data with entanglement characteristics.

10. The quantum cognitive simulation method of claim 7, wherein, Tensor network optimization: For high-dimensional data processing, matrix product states or tree tensor networks are used to decompose and compress high-dimensional quantum states.

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