A quantum cognitive simulation system and method based on classical computing

Abstract

The application discloses a quantum cognitive simulation system and method based on classical calculation, and belongs to the technical field of quantum calculation. A data processing module receives original data of an external data input device, and converts the original data into quantum state data after processing, and transmits the quantum state data to a quantum cognitive simulation calculation 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 the result in a storage module or directly transmits the result to an output module; and the output module outputs the calculation result to an external display / control device after format conversion. The application aims to solve the deficiencies of traditional cognitive models in processing decision ambiguity, context dependence and high-dimensional calculation efficiency, and proposes a quantum cognitive simulation system and method based on classical calculation, which can efficiently simulate quantum superposition, interference and entanglement effects, realize quantitative calculation of human consciousness related functions, and optimize the performance in actual application scenarios.

Description

Technical Field

[0001] This invention relates to the field of quantum computing technology, specifically to a quantum cognitive simulation system and method based on classical computing. Background Technology

[0002] Traditional cognitive models have the following shortcomings:

[0003] 1. Insufficient handling of decision ambiguity: Traditional cognitive models (such as Bayesian networks) are based on classical probability theory and can only output a single probability result, failing to effectively simulate the ambiguity and uncertainty that exist in human decision-making processes. For example, when faced with complex situations, humans may simultaneously favor multiple options, while classical models can only output a single "optimal" decision, unable to express superposition cognitive states such as "simultaneously favoring A and B," leading to a distortion in the simulation of real decision-making behavior.

[0004] 2. Inefficient handling of context-dependent issues: Traditional cognitive models typically require manual pre-setting of numerous parameters or manual adjustment of the model structure to adapt to different contexts when dealing with context-dependent problems. For example, in natural language processing, semantic understanding changes with the context. Classic models rely on manual rules or experience to adjust parameters to achieve a certain level of context adaptation, which not only consumes a lot of manpower and time but also makes it difficult to cope with dynamically changing and complex contexts, resulting in insufficient model generalization ability.

[0005] 3. Low computational efficiency in 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 the Bayesian network expands rapidly, making inference computation too time-consuming or even impossible to complete. This severely limits the application of the model in complex high-dimensional scenarios and cannot meet the needs of decision-making tasks with high real-time requirements.

[0006] Based on this, the present invention designs a quantum cognition simulation system and method based on classical computing to solve the above problems. Summary of the Invention

[0007] This invention aims to address the shortcomings of traditional cognitive models in handling decision ambiguity, context dependence, and high-dimensional computational efficiency. It proposes a quantum cognitive simulation system and method based on classical computing, which can efficiently simulate effects such as quantum superposition, interference, and entanglement, realize the quantitative calculation of functions related to human consciousness, and optimize its performance in practical application scenarios.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] 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. The modules work together to realize the quantum cognitive simulation function.

[0010] The data processing module receives raw data from external data input devices, processes it, converts it into quantum state data, and transmits it to the quantum cognitive simulation computing module.

[0011] The quantum cognitive simulation computing module utilizes CPU clusters, GPU arrays, and coprocessors to perform interference term calculations, Monte Carlo sampling to simulate entanglement, tensor network optimization, attention mechanism calculations, decision fuzzy processing, dynamic memory retrieval, and quantum state measurement.

[0012] The quantum cognitive simulation computing module stores the results in the storage module or transmits them directly to the output module.

[0013] The output module converts the calculation results into a new format and outputs them to an external display / control device.

[0014] Furthermore, the data processing module consists of a data acquisition submodule and a data encoding submodule. The data acquisition submodule is connected to external data input devices and the data encoding submodule. The data acquisition submodule is responsible for acquiring data from different application scenarios in real time. The data encoding submodule converts the acquired data into a quantum state format represented by complex probability amplitudes.

[0015] Furthermore, the quantum cognition simulation computing module consists of a CPU cluster, a GPU array, and a coprocessor, which are connected via a high-speed interconnect bus. The GPU array performs tensor network optimization and Monte Carlo sampling simulation of entanglement tasks in parallel, while the coprocessor is used to accelerate the calculation of interference terms, quantum state measurement algorithms, and to perform calculations for the consciousness function module.

[0016] Furthermore, the computation of the consciousness function module includes attention mechanism computation, decision fuzziness processing, dynamic memory retrieval, and quantum state measurement.

[0017] Furthermore, the storage module adopts a storage architecture that combines SSD arrays and RAM.

[0018] Furthermore, 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 via an internal data bus; the output interface submodule is connected to the data format conversion submodule and external devices; 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 external devices.

[0019] To better achieve the objectives of this invention, this invention also provides a quantum cognitive simulation method, utilizing the aforementioned classical computation-based quantum cognitive simulation system, comprising the following steps:

[0020] 1) Data input and preprocessing: Receive data from different application scenarios and convert the data into a format suitable for quantum cognitive simulation, specifically by encoding the data into a quantum state form represented by complex probability amplitudes;

[0021] 2) Quantum cognitive model calculation, including quantum state initialization, calculation of interference terms using quantum effect simulation algorithms, Monte Carlo sampling to simulate entanglement, and tensor network optimization; calculation of consciousness function modules, including attention mechanism calculation, decision fuzziness processing, dynamic memory retrieval, and quantum state measurement;

[0022] 3) Output and post-processing: Perform measurement operations on the quantum state results calculated by the quantum cognitive model to collapse the quantum state into a classical probability distribution or specific decision result; perform post-processing on the output results.

[0023] Furthermore, interference term calculation: Based on complex probability amplitudes, the interference term between different decision paths or cognitive states is calculated using the following formula:

[0024] I=2·∣Ψ1∣·∣Ψ2∣·cos(θ)

[0025] Where θ is the phase difference between the probability amplitudes of the two paths, and Ψ1 and Ψ2 represent the complex probability amplitudes of the two paths, respectively.

[0026] Furthermore, Monte Carlo sampling simulates entanglement: the Monte Carlo sampling algorithm is used to introduce nonlocal correlations to generate sample data with entanglement characteristics.

[0027] Furthermore, tensor network optimization: For high-dimensional data processing, matrix product states or tree-structured tensor networks are used to decompose and compress high-dimensional quantum states.

[0028] Compared to existing technologies, the advantages of this invention are as follows: 1. Overcoming the limitations of decision-making fuzziness and improving simulation realism. This invention represents the decision state as a superposition state through the calculation of complex probability amplitudes and interference terms, allowing the system to simultaneously consider multiple option tendencies (e.g., "simultaneously favoring A and B"), and dynamically adjusting the weights of each option through interference terms. For example, in complex investment decision-making scenarios, traditional models can only provide a single investment option, while this invention can simulate the decision-maker's simultaneous consideration of multiple risk-return combinations, simulating the mutual influence between different decision paths through interference terms, making the simulation results closer to real human decision-making behavior.

[0029] 2. Automatically adapts to changing context, reducing manual costs. This invention utilizes quantum interference effects to automatically correct context-dependent behavior: by encoding contextual information as quantum state phase differences, the interference term automatically adjusts the decision path weights based on these phase differences, eliminating the need for manual intervention. For example, in natural language processing, when faced with sentences whose semantics dynamically change with the context, the system can automatically adjust the semantic understanding path according to the context, avoiding the need for manually pre-setting numerous rules.

[0030] 3. Significantly improved high-dimensional computation efficiency, expanding application boundaries. This invention employs tensor network compression technology to decompose high-dimensional quantum states into low-rank tensor chains, reducing computational complexity from exponential to polynomial level.

[0031] 4. Achieving Quantitative Computation of Consciousness Functions. This invention innovatively decomposes consciousness into computable modules such as attention, decision fuzziness, and memory retrieval: it simulates attention focus switching through quantum measurement, represents decision fuzziness using quantum superposition states, and achieves memory association retrieval based on quantum entanglement. For example, in psychological experimental simulations, the system can quantitatively analyze the differences in attention allocation among individuals under different emotional states, providing a reproducible computational model for cognitive science research and filling the gap in traditional methods for quantitative research on consciousness functions.

[0032] 5. Compatible with classical computing platforms, lowering the deployment threshold. This invention simulates quantum effects using classical algorithms (such as Monte Carlo sampling to simulate entanglement and complex probability amplitudes to replace quantum states), enabling efficient computation 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 this invention is only 1 / 10 of that of a quantum computing solution, and the deployment cycle is shortened from months to weeks, significantly lowering the threshold for technology implementation and accelerating the commercial application of quantum cognitive technology in multiple fields. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0034] Figure 1 This is a structural diagram of a quantum cognitive simulation system based on classical computation according to the present invention;

[0035] Figure 2 This is a flowchart of a quantum cognition simulation method based on classical computation according to the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present 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 the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0037] Example 1: Please refer to the accompanying drawings in the instruction manual. Figure 1 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. The modules work together to realize the quantum cognitive simulation function.

[0038] The data processing module adopts a modular design, consisting of a data acquisition submodule and a data encoding submodule. The data acquisition submodule is equipped with multiple data interfaces, such as USB, Ethernet, and wireless communication interfaces, for connecting to different types of data input devices. The data encoding submodule is built on an FPGA (Field-Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit) chip to implement efficient data encoding algorithms. The data acquisition submodule connects to external data input devices through corresponding interfaces; the data acquisition submodule and the data encoding submodule are connected via an internal high-speed data bus and integrated on the same printed circuit board (PCB) to reduce data transmission latency.

[0039] The data acquisition submodule is responsible for collecting data from different application scenarios in real time. For example, in a psychological state prediction scenario, it receives individual behavioral data and questionnaire scores; in an AI dialogue system, it receives text data input by users; and in an autonomous driving scenario, it receives data from sensors such as images, radar, and lidar. The data encoding submodule converts the collected data into a quantum state format represented by complex probability amplitudes, providing the foundational data for subsequent quantum cognitive simulation calculations. For example, for text data, each word is mapped to a high-dimensional vector using a word vector model, and then the vector elements are converted into complex probability amplitudes; for sensor data, the measurement values ​​from different types of sensors are encoded into the real and imaginary parts of the quantum state parameters.

[0040] The quantum cognition simulation computing module consists of a central processing unit (CPU) cluster, a graphics processing unit (GPU) array, and coprocessors. Based on the quantum state data output by the data processing module, the CPU cluster coordinates the workflow of each computing resource to execute logic control and general computing tasks. The GPU array performs tensor network optimization and Monte Carlo sampling simulation of entanglement tasks in parallel, rapidly processing high-dimensional data. Coprocessors (such as FPGA accelerators) accelerate algorithms for interferometric term calculation and quantum state measurement, enabling efficient simulation of effects such as quantum superposition, interference, and entanglement, as well as calculations for the consciousness function module. The CPU cluster, GPU array, and coprocessors are connected via a high-speed interconnect bus (such as a PCI-Express bus) and deployed within the same computing node chassis, with a cooling system ensuring stable operation of each component.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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:

[0046] 1) Data Input and Preprocessing: This involves receiving data from various application scenarios, such as individual behavioral data and questionnaire scores in psychological state prediction; text data input by users in AI dialogue systems; and images, radar, and lidar data collected by sensors in autonomous driving scenarios. The data is then converted into a format suitable for quantum cognitive simulation, specifically encoding it into quantum state forms represented by complex probability amplitudes. For example, for text data, each word is mapped to a high-dimensional vector using a word vector model, and then the vector elements are converted into complex probability amplitudes; for sensor data, the measurements from different types of sensors are encoded into the real and imaginary parts of the quantum state parameters.

[0047] 2) Quantum cognitive model calculation, including the following steps:

[0048] 2.1) Quantum state initialization:

[0049] The quantum state is initialized based on the characteristics of the input data and the requirements of the application scenario. For example, in a multi-option decision-making scenario, an initial complex probability amplitude is assigned to each decision option to construct an initial quantum superposition state.

[0050] The traditional Bayesian probability P is replaced by a complex probability amplitude Ψ = a + bi, where a, b ∈ R, and the probability is defined as |Ψ|. 2 =a 2 +b 2 Here, '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 characterize non-classical features such as interference and contextual sensitivity; and 'i' is the imaginary unit, satisfying i² = -1. By introducing the imaginary part 'bi', a quantum superposition state is naturally represented.

[0051] 2.2) Applying quantum effect simulation algorithms to calculate interference terms, Monte Carlo sampling to simulate entanglement, and tensor network optimization:

[0052] Interference term calculation: Based on the complex probability amplitude, the interference term between different decision paths or cognitive states is calculated using the interference term formula.

[0053] The formula for the interference term is:

[0054] I=2·∣Ψ1∣·∣Ψ2∣·cos(θ)

[0055] Here, θ represents the phase difference between the probability amplitudes of the two paths. The weights of each state are dynamically adjusted to simulate quantum interference effects, enabling automatic correction of context-dependent behavior. Ψ1 and Ψ2 describe the degree of interference between two cognitive states or decision paths. Ψ1 and Ψ2 represent the complex probability amplitudes of the two paths, with their magnitudes corresponding to the weights or biases of each path in the quantum superposition state. By controlling the amplitudes of θ and Ψ1 and Ψ2, the influence of different contexts or cognitive preferences on the final decision outcome can be simulated.

[0056] Monte Carlo sampling to simulate entanglement: For scenarios requiring the simulation of quantum entanglement, the Monte Carlo sampling algorithm is used to introduce nonlocal correlation. By setting the number of samples and correlation parameters, the number of samples is typically set between 10² and 10⁻⁶. 5 Within the specified range, the parameters are dynamically adjusted according to the simulation accuracy requirements. Relevant parameters include: specifying the particle pairs participating in the entanglement simulation, the correlation strength parameter controlling the nonlocality, an optional initial probability distribution form, and the noise level. After sampling, the degree of entanglement is quantitatively evaluated using mutual information or entanglement entropy. Sample data with entanglement characteristics is generated for subsequent calculations and decisions. Mutual information is used as the quantification metric for entanglement; a higher value indicates a stronger degree of entanglement.

[0057] The following is an example of a basic form of Monte Carlo sampling entanglement simulation in the prior art:

[0058] def quantum_entanglement_sampling(particles, iterations=1000):

[0059] "Using the Monte Carlo method to simulate the entanglement effect between particles"

[0060] samples = []

[0061] for _ in range(iterations):

[0062] # Generate samples with non-local correlation

[0063] sample = correlated_sampling(particles)

[0064] samples.append(sample)

[0065] # Calculate entanglement metrics (such as mutual information)

[0066] entanglement_metric = calculate_mutual_information(samples)

[0067] return entanglement_metric

[0068] Tensor Network Optimization: For high-dimensional data processing, tensor network structures are constructed. Techniques such as Matrix Product States (MPS) or Tree Tensor Networks (TTN) are used to decompose and compress high-dimensional quantum states, reducing computational complexity from exponential to polynomial levels and improving computational efficiency. During computation, tensor contraction operations are used to efficiently calculate the expected value and related statistics of the quantum state.

[0069] The following are examples of tensor network structures in existing technologies:

[0070] def tensor_network_compression(quantum_state, max_bond_dim=16):

[0071] "Using Matrix Product States (MPS) to Compress High-Dimensional Quantum States"

[0072] # Decompose an N-dimensional quantum state into a tensor chain

[0073] mps = matrix_product_state(quantum_state, max_bond_dim=max_bond_dim)

[0074] # Efficiently Calculate Expected Values ​​Using Tensor Shrinkage

[0075] expectation_value = contract(mps)

[0076] return expectation_value

[0077] 2.3) Perform calculations on consciousness function modules, including calculations of attention mechanisms, handling of decision fuzziness, dynamic memory retrieval, and quantum state measurement;

[0078] Attention mechanism computation: Based on input data and the current cognitive state, attention mechanism computation is performed using quantum measurement theory. The attention operator is defined. By measuring quantum states, cognitive states can be collapsed into specific attentional dimensions, thereby enabling selective processing of key information.

[0079] In this invention, the attention mechanism is remodeled as a quantum measurement process. By constructing a measurement operator related to cognitive focus, the input quantum state is projected to achieve selective enhancement of key information. This mechanism simulates the attention focus switching process in human cognition, differing from the "weighted" approach in traditional deep learning, and possesses higher information selectivity and interpretability. The quantum measurement-based attention mechanism method includes the following steps:

[0080] 1. Convert the input information into a quantum state represented by a complex probability amplitude;

[0081] 2. Determine the attention dimension based on the task focus and construct the corresponding measurement operator;

[0082] 3. Apply a measurement operator to the quantum state to generate a new post-measurement state;

[0083] 4. Use the measured state for downstream cognitive computing or information processing tasks.

[0084] An example of an attention mechanism is as follows:

[0085] def quantum_attention(state, focus_dimension):

[0086] "To achieve quantum attention mechanisms and selectively enhance information processing in specific dimensions."

[0087] # Constructing Attention Measurement Operators

[0088] attention_operator = construct_measurement_operator(focus_dimension)

[0089] # Performing quantum measurements

[0090] post_measurement_state = measure(state, attention_operator)

[0091] return post_measurement_state

[0092] Decision fuzziness handling: The fuzziness in the decision-making process is represented by quantum superposition states. Through interference term calculation and quantum state evolution, the dynamic competition of decision paths is simulated.

[0093] In the decision output stage, the final decision result is obtained through quantum state measurement. This result reflects the uncertainty and multi-option tendency in the decision-making process, integrating dynamic weight initialization, parameterized Hamiltonian evolution, POVM measurement, and a simple feedback learning framework. The process includes the following steps:

[0094] 1. Quantum state initialization: Represent the decision options as quantum superposition states, with each option corresponding to a probability amplitude;

[0095] 2. Hamiltonian Construction: A Hamiltonian is constructed based on context parameters to describe the evolution of the system;

[0096] 3. Quantum state evolution: The initial state is evolved using a constructed Hamiltonian;

[0097] 4. POVM Measurement: Using Positive Operator Value Measurement (POVM) to simulate the uncertainty of decision outcomes;

[0098] 5. Feedback Learning: Adjust model parameters based on feedback after decision-making to achieve learning and optimization.

[0099] For example, in multi-option decision-making: Python runs

[0100] def quantum_decision_making(options, context):

[0101] "Simulating fuzzy decision-making processes"

[0102] # Initialization options for quantum superposition states

[0103] superposition_state = initialize_superposition(options)

[0104] # Evolution of Hamiltonian dependent on application context

[0105] evolved_state = evolve(superposition_state, context)

[0106] # Measurement leads to the final decision

[0107] decision = measure(evolved_state)

[0108] return decision

[0109] def initialize_superposition(options, prior_weights=None):

[0110] """

[0111] Initialize the superposition state, where options is the list of options and prior_weights is the prior weights (probability amplitude).

[0112] """

[0113] n = len(options)

[0114] if prior_weights is None:

[0115] prior_weights = np.ones(n) / np.sqrt(n) # Uniform superposition state, normalized probability amplitude

[0116] else:

[0117] # Normalized probability magnitude vector (the sum of the squares of the magnitudes of the probability magnitudes is 1)

[0118] prior_weights = np.array(prior_weights, dtype=np.complex128)

[0119] norm = np.linalg.norm(prior_weights)

[0120] prior_weights / = norm

[0121] return prior_weights # Quantum state is represented as an array of complex probability magnitudes

[0122] def construct_hamiltonian(context_params, n):

[0123] """

[0124] Constructing the Hamiltonian, parameterized design, with context_params as the context parameters.

[0125] Here, a simple diagonal matrix with perturbation is used, which can be finely tuned using a real-world learning algorithm.

[0126] """

[0127] base_energy = np.diag(np.linspace(0, 1, n)) # Basic energy level

[0128] perturbation = context_params.get('perturbation', 0.1) * (np.ones((n,n)) - np.eye(n))

[0129] hamiltonian = base_energy + perturbation

[0130] return hamiltonian

[0131] def evolve_state(state, hamiltonian, time=1.0):

[0132] """

[0133] State evolution: |psi(t)> = exp(-iHt) |psi(0)>

[0134] """

[0135] U = expm(-1j * hamiltonian * time) # Evolution operator

[0136] evolved_state = U @ state

[0137] return evolved_state

[0138] def perform_povm_measurement(state, povm_elements):

[0139] """

[0140] Using POVM to measure simulated decision output

[0141] povm_elements are n positive operators whose sum is an identity matrix.

[0142] Return to measurement results index

[0143] """

[0144] probabilities = np.array([np.real(state.conj().T @ E @ state) forE in povm_elements])

[0145] probabilities = np.clip(probabilities, 0, 1)

[0146] probabilities / = probabilities.sum()

[0147] decision = np.random.choice(len(povm_elements), p=probabilities)

[0148] return decision, probabilities

[0149] def update_model_feedback(prior_weights, hamiltonian, decision,reward, learning_rate=0.1):

[0150] """

[0151] Simple feedback adjustment mechanism

[0152] Rewards are positive or negative incentives provided as feedback after a decision is made.

[0153] Used to adjust prior weights and Hamiltonian parameters (only weight adjustment is demonstrated here).

[0154] """

[0155] # Adjust the probability amplitude based on the reward (simplified version)

[0156] adjustment = learning_rate * reward

[0157] prior_weights[decision] += adjustment

[0158] # Renormalization

[0159] prior_weights / = np.linalg.norm(prior_weights)

[0160] # Hamiltonian learning can be achieved using gradient methods, omitted here.

[0161] return prior_weights, hamiltonian

[0162] def quantum_decision_making(options, context_params, prior_weights=None, povm_elements=None, feedback=None):

[0163] """

[0164] Improved quantum decision function

[0165] """

[0166] n = len(options)

[0167] # Initialize superposition state

[0168] state = initialize_superposition(options, prior_weights)

[0169] # Constructing the Hamiltonian

[0170] H = construct_hamiltonian(context_params, n)

[0171] # State Evolution

[0172] evolved_state = evolve_state(state, H, time=context_params.get('evolution_time', 1.0))

[0173] # If there are no POVM measurement elements, use simple orthogonal projection.

[0174] if povm_elements is None:

[0175] povm_elements = [np.zeros((n,n)) for _ in range(n)]

[0176] for i in range(n):

[0177] povm_elements[i][i,i] = 1 # Simple projective measurement operator

[0178] # Measurement Decision

[0179] decision, probabilities = perform_povm_measurement(evolved_state,povm_elements)

[0180] # Feedback Learning (Optional)

[0181] If feedback is not None:

[0182] prior_weights, H = update_model_feedback(state, H, decision,feedback)

[0183] return options[decision], probabilities

[0184] Dynamic memory retrieval: storing memory content in quantum state form. By constructing quantum entanglement, efficient memory retrieval based on correlation is achieved. When a memory needs to be retrieved, the query information is entangled with the memory quantum state, and the relevant memory content is extracted through measurement. This includes the following steps:

[0185] 1. Quantum state preparation: Encode the query information into a quantum state;

[0186] 2. Quantum entanglement operation: Entangling the query quantum state with each memory quantum state;

[0187] 3. Measurement and scoring: Measure the entangled states and obtain the relevance score for each memory;

[0188] 4. Result selection: Select the memory with the highest score as the retrieval result;

[0189] 5. Feedback Adjustment: Update entanglement parameters and memory state based on feedback information.

[0190] The search process is illustrated below:

[0191] def quantum_memory_retrieval(query, memory_states, entangle_params,feedback=None):

[0192] """

[0193] Quantum dynamic memory retrieval

[0194] - query: The quantum state encoded in the query information

[0195] - memory_states: The set of quantum states corresponding to the memory content

[0196] - entangle_params: Entanglement operation parameters

[0197] - Feedback: Optional, feedback will be provided after the search for adjustments.

[0198] """

[0199] # 1. Encoding the query state (assuming it has been encoded as a query)

[0200] q_state = query

[0201] # 2. Entangling the query state and each memory state in sequence.

[0202] entangled_states = []

[0203] for m_state in memory_states:

[0204] entangled = entangle_operator(q_state, m_state, entangle_params)

[0205] entangled_states.append(entangled)

[0206] # 3. Measure all entangled states and obtain correlation scores.

[0207] scores = [measure(entangled) for entangled in entangled_states]

[0208] # 4. Select the highest score and memorize it.

[0209] best_idx = np.argmax(scores)

[0210] retrieved_memory = memory_states[best_idx]

[0211] # 5. Feedback and adjustments (if any)

[0212] if feedback:

[0213] entangle_params = update_entangle_params(entangle_params,feedback)

[0214] memory_states = update_memory_states(memory_states, feedback)

[0215] return retrieved_memory,scores

[0216] 3) Output and Post-processing: Measurement operations are performed on the quantum state results calculated by the quantum cognitive model, collapsing the quantum states into classical probability distributions or specific decision outcomes. For example, in psychological state prediction, the probabilities of different psychological states are output. Post-processing, such as format conversion and result interpretation, is performed according to the application scenario requirements to facilitate practical application and user understanding.

[0217] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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. Such modifications or substitutions 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 invention.

Claims

1. A quantum cognitive simulation system based on classical computing, characterized in that, It includes a data processing module, a quantum cognitive simulation computing module, a storage module, and an output module. These modules work together to realize the quantum cognitive simulation function. The data processing module receives raw data from external data input devices, processes it, converts it into quantum state data, and transmits it to the quantum cognitive simulation computing module. The quantum cognitive simulation computing module utilizes CPU clusters, GPU arrays, and coprocessors to perform interference term calculations, Monte Carlo sampling to simulate entanglement, tensor network optimization, attention mechanism calculations, decision fuzzy processing, dynamic memory retrieval, and quantum state measurement. The quantum cognitive simulation computing module stores the results in the storage module or transmits them directly to the output module. The output module converts the calculation results into a new format and outputs them to an external display / control device. The computation of the consciousness function module includes attention mechanism computation, decision fuzzy processing, dynamic memory retrieval, and quantum state measurement; Interference term calculation: Based on complex probability amplitudes, the interference term between different decision paths or cognitive states is calculated using the following formula: I=2·∣Ψ1∣·∣Ψ2∣·cos(θ) Where θ is the phase difference between the probability amplitudes of the two paths, and Ψ1 and Ψ2 represent the complex probability amplitudes of the two paths, respectively. Monte Carlo sampling to simulate entanglement: The Monte Carlo sampling algorithm is used to introduce nonlocal correlations to generate sample data with entanglement properties; Tensor network optimization: For high-dimensional data processing, matrix product states or tree-structured tensor networks are used to decompose and compress high-dimensional quantum states.

2. The quantum cognitive simulation system based on classical computing according to claim 1, characterized in that, The data processing module consists of a data acquisition submodule and a data encoding submodule. The data acquisition submodule is connected to external data input devices and the data encoding submodule. The data acquisition submodule is responsible for collecting data from different application scenarios in real time. The data encoding submodule converts the collected data into a quantum state format represented by complex probability amplitudes.

3. The quantum cognitive simulation system based on classical computing according to claim 1, characterized in that, The quantum cognition simulation computing module consists of a CPU cluster, a GPU array, and a coprocessor, which are connected via a high-speed interconnect bus. The GPU array performs tensor network optimization and Monte Carlo sampling simulation of entanglement tasks in parallel, while the coprocessor is used to accelerate the calculation of interference terms, quantum state measurement algorithms, and to perform calculations for the consciousness function module.

4. The quantum cognitive simulation system based on classical computing according to claim 1, characterized in that, The storage module adopts a storage architecture that combines SSD arrays and RAM.

5. The quantum cognitive simulation system based on classical computing according to claim 1, characterized in that, 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 via an internal data bus; the output interface submodule is connected to the data format conversion submodule and external devices. 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; the output interface submodule outputs the processed data to external devices.

6. A quantum cognitive simulation method, utilizing the quantum cognitive simulation system based on classical computation as described in any one of claims 1 to 5, characterized in that, Includes the following steps: 1) Data input and preprocessing: Receive data from different application scenarios and convert the data into a format suitable for quantum cognitive simulation, specifically by encoding the data into a quantum state form represented by complex probability amplitudes; 2) Quantum cognitive model calculation, including quantum state initialization, calculation of interference terms using quantum effect simulation algorithms, Monte Carlo sampling to simulate entanglement, and tensor network optimization; calculation of consciousness function modules, including attention mechanism calculation, decision fuzziness processing, dynamic memory retrieval, and quantum state measurement; 3) Output and post-processing: Perform measurement operations on the quantum state results calculated by the quantum cognitive model to collapse the quantum state into a classical probability distribution or specific decision result; perform post-processing on the output results.

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