A topological optical quantum hopfield network and adaptive regulation method

Abstract

The application discloses a kind of topological optical quantum Hopfield networks and adaptive control method, belong to optical quantum computing and topological photonics cross technical field.System is deployed in integrated optical quantum chip, core includes programmable multi-photon interference network, reconfigurable topological protection waveguide array and collaborative controller.The three-way collaborative control architecture of the application is constructed memory coding phase matrix-pump control parameter matrix-topological waveguide configuration matrix, the critical state identification function is used to actively determine preheating critical state, assisted by weak coherent pulse trigger photonic phase transition, realize programmable associative memory computing under topological protection.The energy consumption of single associative computing of the application is as low as 0.85fJ / bit, the critical state trigger success rate can reach 100% under experimental conditions, the environmental robustness is improved by two orders of magnitude compared with traditional open-loop scheme, and can be widely used in optical quantum AI accelerator, ultra-low power edge computing, memory and calculation integrated chip and other scenes.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary fields of optical quantum computing, topological photonics and neuromorphic computing, and specifically relates to a topological optical quantum Hopfield network and an adaptive control method. Background Technology

[0002] With the rapid development of artificial intelligence technology, traditional CMOS electronic computing architecture faces the dual constraints of the von Neumann bottleneck and the energy consumption wall. The energy consumption of a single associative computation has exceeded 10 pJ / bit, which is difficult to meet the needs of future large-scale AI inference. Optical quantum computing, with its inherent parallelism, low energy consumption and high-speed transmission characteristics, is considered to be one of the most promising technical routes to break through the limits of electronic computing.

[0003] In 2026, Sciarrino et al. from the Italian National Research Council (CNR) published a paper entitled "Photonic Hopfield Networks via Multiphoton Interference" in Volume 136, Issue 12 of *Physical Review Letters*, which proved for the first time the precise mathematical correspondence between multiphoton quantum interference and the generalized Hopfield memory model: The interference process of a photon is equivalent to: The evolution of the bulk Hopfield Hamiltonian. This discovery unifies quantum optics and neural networks at the underlying physical mechanism level, laying the theoretical foundation for building ultra-low-power optical quantum AI accelerators. However, this work only passively observes the phase transition phenomenon between the disordered and ordered phases of the photonic Hopfield network, and does not propose an active method for identifying and triggering the phase transition window.

[0004] Current optical quantum Hopfield network technology is still in the laboratory proof-of-concept stage, with three fundamental technological gaps between it and engineering applications: First, the preheating critical state is uncontrollable. Current technology can only passively observe the existence of the critical state, unable to actively identify its threshold range. The success rate of triggering the critical state is less than 30%, and it cannot utilize the critical state to accelerate computation, resulting in a computation speed 2-3 orders of magnitude slower than the theoretical limit. Second, there is a contradiction between topology protection and computational flexibility. Traditional fixed-topology photonic structures cannot be dynamically reconfigured, only able to perform fixed computational tasks, resulting in poor programmability. Existing reconfigurable topology photonic schemes mostly target the tuning of single structural parameters, failing to form a synergistic control mechanism with the encoding and phase transition processes of optical quantum computing, thus failing to achieve full-task programmability while maintaining topology protection. Third, there is a lack of a unified underlying control framework. Current optical quantum computing systems use an open-loop control method, with a synergistic delay exceeding 1 μs between the light source, interference network, and readout system. The system has poor robustness, and a fluctuation of 0.1℃ in ambient temperature can cause the computational error rate to rise to over 50%. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention aims to solve the following three core technical problems: first, to resolve the issue of the inability to actively identify and accurately trigger the preheating critical state in optical quantum Hopfield networks, thus breaking through the theoretical limit of computing speed; second, to resolve the long-standing contradiction between topology protection and programmability, achieving full-task dynamic reconfiguration while maintaining topology bandgap and disturbance resistance; and third, to solve the problems of high open-loop control coordination latency and poor robustness in optical quantum computing systems, constructing a unified closed-loop control framework to significantly improve environmental adaptability.

[0006] This invention proposes a topological quantum computing architecture based on ternary synergistic regulation and active critical state identification to address the aforementioned problems. The core concept is to abstract the quantum computing system as a ternary synergistic entity composed of "information configuration, energy parameters, and physical structure." By establishing closed-loop regulation and topological classification isomorphic constraints among these three elements, the system maintains topological protection while possessing full-task programmability. Furthermore, based on the physical principle of non-equilibrium phase transition critical fluctuation amplification, an active identification function is constructed to achieve precise triggering of the preheated critical state. Based on this, the core regulation architecture of this scheme consists of three types of measurable and controllable physical matrices, which synergistically couple to form a closed-loop regulation system.

[0007] Memory-encoded phase matrix: Encodes multiphoton interference modes, Hopfield memory matrix and topological charge distribution, determining the target order and information configuration of the computation task;

[0008] Pump control parameter matrix: characterizes the power, frequency, phase and pulse timing of the pump light, providing phase transition drive and evolution dynamics;

[0009] Topological waveguide configuration matrix: Characterizes waveguide configuration, coupling strength and band structure, and provides the physical carrier for topological protection.

[0010] The three types of matrices belong to different physical levels, and the numerical values ​​and dimensions of the topological invariants are different, but they belong to the same topological class in topological taxonomy, satisfying a one-to-one mapping relationship: the topological charge order of the memory-encoded phase matrix = the number of energy spectral branches of the pump control parameter matrix = the energy band Chern number of the topological waveguide configuration matrix. This constraint is the core criterion for maintaining the topological steady state of the system, ensuring that the topological protection properties are not lost during the control process. The engineering judgment standard is: the integer part of the topological order of the three types of topological invariants is consistent, and the relative deviation after normalization is ≤5%, that is, it is judged to satisfy the topological classification isomorphism constraint. The control sequence follows the evolutionary logic of "target-power-carrier": information configuration first determines the target topological class, energy parameter adaptation provides the phase transition driving force, material structure tuning solidifies the topological protection carrier, and the three achieve the self-organized steady state of the system through closed-loop iteration.

[0011] In a first aspect, this invention provides an adaptive control method for a topological photonic Hopfield network, applied to a photonic quantum computing chip integrating a programmable multiphoton interference network and a reconfigurable topological photonic structure, comprising the following steps: S1 System initialization and topological steady-state anchoring: Constructing a ternary collaborative control architecture, initializing the memory-encoded phase matrix, pump control parameter matrix, and topological waveguide configuration matrix; anchoring global topological steady-state constraints: maintaining the stable distribution of eigenvalues ​​of the Hopfield memory matrix throughout the computation process, ensuring that the fluctuation amplitudes of the topological invariants corresponding to the three types of matrices are all controlled within a preset threshold. S2 Memory mode quantum interference encoding: Encoding the target memory mode set into the memory-encoded phase matrix through multiphoton quantum interference, so that the system steady-state output corresponds to the pre-stored memory attractor; the encoding process satisfies the mathematical correspondence between multiphoton quantum interference and the p-volume Hopfield Hamiltonian. S3. Preheating Critical State Active Identification and Phase Transition Triggering: A critical state identification function is constructed, and the statistical characteristics and quantum overlap of photon output are monitored in real time, and the function value is calculated. When the function value enters the critical interval, the system is determined to have entered the preheating critical state. A weak coherent pulse with specific parameters is used to trigger the system to complete the associative memory phase transition, outputting the steady-state photonic quantum state of the corresponding memory attractor, thus realizing associative memory recall. The power of the weak coherent pulse is 0.5%~2% of the calculated pump light power, and the pulse width is ≤100ps. S4. Ternary Closed-Loop Cooperative Control and Topological State Maintenance: Closed-loop control is executed according to a preset order, and the coupling error of the three types of matrices is calculated in real time. When the error exceeds a preset threshold, the control is repeated until the topological classification isomorphism constraint is met, and the topological protection attribute is maintained throughout the process. S5. Associative Calculation and Result Readout: The external input mode is encoded as a photonic quantum state and injected into the system. Under the drive of the ternary closed loop, the system automatically converges to the nearest memory attractor. The output state distribution is read through a single-photon detector array to complete the associative memory calculation.

[0012] In one implementation, the information configuration update specifically involves updating the memory encoding phase matrix based on the target memory pattern or external input, thereby determining the coherence rules and information order of the computation task.

[0013] In one implementation, the energy parameter control specifically involves: based on the updated memory-encoded phase matrix, using a PID algorithm to control the frequency, power, phase, and pulse timing of the pump light to match the energy requirements of the topological photonic quantum state, and updating the pump control parameter matrix.

[0014] In one implementation, physical structure tuning specifically involves: based on the control results of information configuration and energy parameters, dynamically tuning the waveguide configuration and coupling strength of the reconfigurable topological photonic network, updating the topological waveguide configuration matrix, and providing a material carrier for topological protection in the computation process.

[0015] In a preferred embodiment, the ternary closed-loop control consists of three independent steps: information configuration update, energy parameter control, and physical structure tuning. These steps are executed sequentially in the order of pilot-adaptation-tuning, and the topological isomorphism constraint is finally confirmed through coupling error verification. Specifically, in this preferred embodiment, the coupling error verification and topological classification isomorphism constraint are as follows: the coupling error η of the normalized three types of matrices is calculated in real time. When the coupling error η exceeds a preset threshold, the control steps are repeated until the topological classification isomorphism constraint is met. The formula for calculating the coupling error η is: in , , The normalized memory-encoded phase matrix, pump control parameter matrix, and topological waveguide configuration matrix are as follows: For unit tensor components.

[0016] As a preferred implementation, the critical state identification function integrates photon statistical fluctuations and quantum overlap, and the threshold and system size satisfy a quantitative correspondence. It can be adapted to systems of different sizes through a calibration process, and its expression is: in for The photon count output by the system at any given time. The standard deviation of the photon number statistics. The statistical mean of the number of photons. The quantum overlap between the system output state and the target memory state; when The system is determined to have entered the preheating critical state at a certain time; the threshold value and system parameters satisfy a quantitative relationship: , ,in For photon mode dimension, The number of photons involved in the interference.

[0017] As a preferred implementation, the system memory capacity and photon mode dimension Positive correlation, memory capacity under 2-body interaction is not less than .

[0018] As an optional implementation, it also includes step S6, topological optical energy storage and parameter self-optimization: during the interval of computing tasks, the highly coherent photonic quantum state carrying memory information is locked through the topological resonant cavity, and the information order and energy quantum are stored in an integrated manner; based on historical computing results and state parameters, the initial values ​​and control thresholds of the three types of matrices are iteratively updated to optimize the phase transition triggering accuracy and realize the continuous improvement of the system's computing power.

[0019] As a preferred implementation, weak coherent pulses possess a defined power and pulse width range, enabling precise triggering of non-equilibrium quantum phase transitions and significantly improving computational speed and success rate. As another preferred implementation, during computational intervals, integrated information-energy optical energy storage can be achieved through a topological resonant cavity, and control parameters can be iteratively optimized based on historical data to achieve self-improvement of system performance.

[0020] In a second aspect, this invention provides a topological optical quantum Hopfield network system deployed on an integrated optical quantum chip. The core includes a programmable multiphoton interference network, an entangled multiphoton source array, a reconfigurable topological guard waveguide array, an on-chip temperature control array, and a cooperative controller. Wherein:

[0021] A programmable multiphoton interference network, composed of a cascaded Mach-Zehnder interferometer array, is used to realize photonic quantum simulation of arbitrary unitary transformations and p-body Hopfield Hamiltonians. An entangled multiphoton source array, optically connected to the programmable multiphoton interference network, is used to generate high-purity, highly indistinguishable entangled photons. A reconfigurable topology-protected waveguide array, integrated with the programmable multiphoton interference network on the same chip, provides a topology-protected transmission channel for photonic quantum states. An on-chip temperature control array, thermally coupled to the reconfigurable topology-protected waveguide array, is used to suppress the influence of thermal crosstalk on quantum coherence. A cooperative controller, electrically connected to all the above modules, is used to perform ternary closed-loop control, critical state identification triggering, and calculation result readout.

[0022] In a preferred embodiment, the reconfigurable topological waveguide supports four tuning methods: phase change materials, microelectromechanical systems (MEMS), electro-optic effect, and thermo-optic effect, covering the needs of different speeds, power consumption, and tuning ranges. The phase change material tuning involves switching between crystalline and amorphous states of Ge2Sb2Te5 or Sb2Se3 phase change materials to change the effective refractive index and topological band structure of the waveguide. The MEMS tuning involves adjusting the coupling spacing and coupling strength between waveguides through MEMS structure displacement. The electro-optic effect tuning involves tuning the resonant frequency of the microring resonator through the electro-optic effect to reconstruct the coupled topology of the optical network. The thermo-optic effect tuning involves tuning the waveguide phase through the thermo-optic effect to achieve programmable configuration of the topological photonic network. In a preferred embodiment, the system is configured with a topological optical energy storage module to realize integrated information-energy storage and computing architecture. The topological optical energy storage module switches between energy storage and release states through a tunable coupler to lock highly coherent photonic quantum states during computation intervals, achieving integrated information-energy storage.

[0023] As a preferred implementation, the system supports two working scenarios: low temperature and high precision, and room temperature and low power consumption, to adapt to different application requirements.

[0024] In a third aspect, this invention provides a performance testing method for topological optical quantum Hopfield networks. This method uses four dimensions—statistical scaling law verification, topological invariant consistency verification, perturbation self-recovery verification, and phase transition triggering characteristic verification—combined with specific measurement methods to determine the technical characteristics and performance level of the tested system.

[0025] As a preferred implementation method, clear quantitative judgment criteria are set for each dimension to ensure that the test results are reproducible and comparable. The preset judgment criteria include: statistical scaling law, fitting correlation coefficient. The three types of topological invariants satisfy isomorphic mapping and have a deviation of ≤5%, the computational performance degradation after 10% element failure is <10%, and the phase transition trigger time is ≤500ps.

[0026] In a fourth aspect, the present invention provides an optical quantum computing chip, comprising a memory, a processor, and an integrated photonic structure, wherein the memory stores a computer program, the processor executes the computer program and drives the integrated photonic structure to implement any one of the adaptive control methods of the topological optical quantum Hopfield network described in the first aspect.

[0027] In summary, this invention constructs two synergistic positive feedback paths to drive the system's spontaneous evolution towards a steady state: one is near-field evanescent wave coupling feedback, using a reconfigurable topology-protected waveguide array where adjacent waveguides couple with each other via evanescent waves, spontaneously adjusting the local optical field phase and coupling strength to form a coherent self-consistent basis and maintain the overall system steady state; the other is far-field multiphoton interference feedback, where multiphoton quantum interference is encoded into a memory-encoded phase matrix, and a programmable multiphoton interference network is used to simulate the p-volume Hopfield Hamiltonian. The multiphoton interference result modulates the equivalent impedance of the interference network through the echo effect, strengthening the coherence intensity in the target direction and guiding the system towards a memory attractor. The synergistic effect of these two feedback mechanisms drives the system to spontaneously evolve towards a minimum dissipation configuration. Combined with the statistical smoothing effect of large-scale modes, this endows the system with intrinsic self-stabilizing and self-aligning characteristics.

[0028] The preheating critical state of this invention is a non-equilibrium phase transition window between the disordered phase and the ordered memory retrieval phase. In this state, the system is at a critical point of "disorder but close to order", with significant fluctuation effects. Even a small perturbation can trigger a global phase transition, resulting in extremely high response sensitivity. The intensity of fluctuations characterizing photon number statistics corresponds to the degree of disorder in the system; fluctuations gradually increase before the phase transition; quantum overlap. The degree of closeness between the characterization system and the target memory state gradually increases before the phase transition; the ratio of the two reflects the critical characteristics of "fluctuation amplification + orderly convergence", which conforms to the fluctuation divergence law of the phase transition critical point.

[0029] This invention uses numerical simulation of different modal dimensions With photon number The phase transition process under these conditions is used as the critical interval, with the interval corresponding to a phase transition power ≥ 95%. A quantitative relationship between the threshold and system parameters is obtained through fitting: mode dimension. The larger the value, the stronger the statistical smoothing effect, and the critical lower limit. The lower the value, the better. ; Photon number The more numerous the groups, the stronger the many-group interactions, reaching a critical upper limit. The higher, the more satisfied. .

[0030] The weak coherent pulses of this invention provide symmetry-breaking initial seeds, reduce the phase transition barrier, guide the system to complete non-equilibrium quantum phase transitions along the target attractor direction, significantly shorten the convergence time, and improve the phase transition power.

[0031] According to the law of large numbers, when the photon mode dimension... When large enough, the phase error of a single mode will be statistically averaged and compressed, and the memory retrieval error will satisfy... The scaling law states that the more patterns, the higher the retrieval accuracy. Regarding memory capacity, the capacity of the Hopfield network under two-body interactions is no less than... This solution uses a p-body Hopfield architecture. The storage capacity increases with the number of photons, which conforms to the capacity scaling law of p-body Hopfield networks and is significantly better than the traditional 2-body scheme.

[0032] This invention also constructs a topological optical energy storage physical mechanism. Topological optical energy storage utilizes the backscattering suppression characteristics of topological boundary states to confine multiphoton coherent states encoding memory information within a topological resonant cavity: energy is stored in the form of an optical field, and information is stored in the form of a phase topological configuration; the two are naturally integrated, requiring no additional storage medium. Specifically, topological protection reduces photon scattering loss by one order of magnitude and increases coherence retention time by three times; a tunable coupler enables the coupling switch between the cavity and the bus waveguide, switching between energy storage and release states with a switching time ≤100ps. During energy storage, information order and energy quantum synchronization are maintained synchronously; after release, it can be directly used for the next calculation without re-initialization, truly achieving integrated storage and computation.

[0033] In this invention, the generalized Hopfield Hamiltonian realized by multiphoton interference is in non-Hermitian form, corresponding to a complex-valued memory-encoded phase matrix, whose complex eigenvalue spectrum can be defined by a topological Chern number: for the generalized Hopfield Hamiltonian Its complex eigenvalue spectrum is The Chern number is defined as: The integration path Select as the plane that encloses all complex eigenvalues. A closed curve; in practical calculations, a rectangular or circular enclosure can be used, with the enclosure radius satisfying... This ensures that all eigenvalues ​​reside within the enclosure. The system satisfies topological steady-state constraints when the Chern number of the Hopfield Hamiltonian remains constant during computation.

[0034] Compared with existing technologies, this invention has the following core technological advantages:

[0035] (1) Active controllability of critical state and order of magnitude improvement in computation speed: Active identification and precise triggering of preheated critical state can be achieved. Under experimental conditions, the success rate of critical state triggering can reach 100%, the phase transition time is shortened to less than 500ps, and the computation speed is more than 200 times faster than the existing passive observation quantum optical scheme.

[0036] (2) Topology protection and programmability are achieved simultaneously: Through ternary closed-loop coordinated tuning, the entire system can be programmable while maintaining the 120GHz topological bandgap, solving the long-standing technical problem of "fixed topology means fixed function" in the field of topological photonics.

[0037] (3) The environmental robustness is greatly enhanced: the unified control of the three-element closed loop reduces the system collaborative delay from 1μs to less than 10ns, and the calculation error rate is still less than 1% when the ambient temperature fluctuates by 0.5℃. The robustness is improved by two orders of magnitude compared with the open loop scheme.

[0038] (4) Energy consumption breaks through the limit: the energy consumption of a single association calculation is as low as 0.85fJ / bit, which is 14,000 times lower than the electronic Hopfield solution of the same scale 28nm CMOS process and 3,700 times lower than the existing optical quantum solution, making it suitable for ultra-low power consumption scenarios at the edge.

[0039] (5) Pioneering information-energy integrated optical energy storage: using topological resonant cavity to realize synchronous storage of information and energy of optical quantum state, with energy storage efficiency ≥95%, truly realizing storage and computing integrated architecture, and standby power consumption can be reduced to zero. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the multiphoton encoding process of the memory-encoded phase matrix according to an embodiment of the present invention; Figure 3 This is a timing diagram of active identification of preheated critical state and triggering of quantum phase transition according to an embodiment of the present invention; Figure 4 This is a flowchart of a ternary closed-loop coordinated control process according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the associative memory recall and calculation process according to an embodiment of the present invention; Figure 6 The diagram shows the structure and energy storage-release timing of a topological optical energy storage module according to an embodiment of the present invention.

[0041] Figure reference numerals: 101 Cooperative controller, 102 Entangled multiphoton source array, 103 Programmable multiphoton interference network, 104 Reconfigurable topology-protected waveguide array, 105 On-chip temperature control array, 106 Single-photon detector array, 107 Topology optical energy storage module. Detailed Implementation

[0042] The technical solution of the present invention will be described in detail and completely below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can reproduce and implement the present invention based on the content of this specification. All numerical values ​​in this application are merely examples for the purpose of understanding the present application. Actual parameters depend on the system hardware platform, application scenario, and control cycle, and do not constitute a limitation on the scope of protection of this application. First, the engineering definition and measurement method of the core parameters are clarified as follows.

[0043] Coupling error definition: Characterizing the degree of coupling fit among three types of matrices—the memory-encoded phase matrix, the pump control parameter matrix, and the topological waveguide configuration matrix—it is a core monitoring indicator for maintaining topological steady state; a smaller value indicates a higher degree of fit. Calculation method: in , , The normalized matrices are of three types, and the normalization methods are as follows: the memory-encoded phase matrix is ​​normalized by the maximum value of the components, the pump control parameter matrix is ​​normalized by the rated power, and the topology waveguide configuration matrix is ​​normalized by the maximum grid spacing. This is a unit tensor component. The typical operating threshold is 0.05. When η ≥ 0.05, the matching degree is considered insufficient, and repeated adjustments are required.

[0044] Quantum overlap is defined as the degree of coincidence between the actual photonic quantum state and the target memory state. Theoretical expression: Engineering measurement method: The second-order coherence of the optical field is measured using a balanced null detector. The HOM (Homo simulcasting) interference contrast approximation method is used. When the photon indistinguishability is ≥99%, the error of this approximation method is ≤5%, which meets the closed-loop control accuracy requirements.

[0045] The topological classification isomorphism determination method obtains the corresponding topological invariants through three types of measurement methods:

[0046] Memory-encoded phase matrix: The phase distribution is extracted through phase demodulation, and the topological charge order of the optical field is calculated;

[0047] Pump control parameter matrix: The energy spectrum structure is obtained through spectral analysis, and the number of energy spectrum branches is counted;

[0048] Topological waveguide configuration matrix: The photonic band structure is obtained through band characterization tests, and the band Chern number is calculated;

[0049] The normalization methods for the three types of topological invariants are as follows: the topological charge order of the memory-encoded phase matrix is ​​normalized to the system's maximum resolvable topological charge order; the number of spectral branches of the pump control parameter matrix is ​​normalized to the system's maximum realizable spectral branches; and the band Chern number of the topological waveguide configuration matrix is ​​normalized to the maximum supportable Chern number of the topological structure. The normalized values ​​all fall within the range of [0,1]. If the topological classification orders of the three correspond one-to-one, and the relative deviation after normalization is ≤5%, the topological classification isomorphism constraint is satisfied.

[0050] Implementation method of core hardware modules

[0051] The programmable multiphoton interference network employs a hierarchical binary tree structure of cascaded Mach-Zehnder interferometer (MZI) arrays. Each MZI contains two 3dB couplers and one thermo-optical phase modulator, enabling arbitrary unitary transformations through phase adjustment. Array size... , Insertion loss ≤0.5dB / cell.

[0052] The entangled multiphoton source array employs a spontaneous parametric downconversion (SPDC) photon source, based on a silicon nitride microring resonator, with photon indistinguishability ≥99.5% and luminance ≥1MHz / mW. The array size can be expanded as needed.

[0053] The reconfigurable topology-protected waveguide array adopts a valley-Hall phase topology design with a topology bandgap ≥100GHz, integrates a dynamic tuning unit, and supports the following four tuning modes:

[0054] Phase change material tuning: Integrated Ge2Sb2Te5 thin film, switching speed of about 10ns, and large tuning range;

[0055] MEMS tuning: The cantilever beam structure allows for adjustment of waveguide spacing, providing a wide range of coupling strength adjustment.

[0056] Electro-optic tuning: Lithium niobate material platform, tuning speed up to GHz level;

[0057] Thermo-optical tuning: Silicon-based platform, mature technology, low cost.

[0058] The collaborative controller, implemented using an FPGA, has a control latency of ≤10ns and integrates five functional units: information encoding, critical state identification, energy regulation, structural tuning, and closed-loop verification. Updates to the three types of matrices are synchronously initiated using a unified trigger signal. Preset delay compensation offsets the response time differences between different modules, ensuring precise synchronization of control timing. Closed-loop control employs an incremental PID algorithm, with parameters determined through offline calibration.

[0059] Example 1: Prototype System of 256-Dimensional Topological Optical Quantum Hopfield Network

[0060] like Figure 1 As shown, this embodiment constructs a prototype system with 256 photon mode dimensions to verify the effectiveness of the core technology.

[0061] Hardware configuration:

[0062] Chip platform: Silicon nitride (SiN) photonic chip, size 8mm×8mm;

[0063] Programmable multiphoton interference network 103: 256×256 cascaded MZI array, hierarchical binary tree structure;

[0064] Entangled multiphoton source array 102: 128 SPDC micro-ring photon sources, photon indistinguishability ≥99.5%;

[0065] Reconfigurable topology-protected waveguide array 104: Valley Hall phase design, topology bandgap 120GHz, integrated Ge2Sb2Te5 phase-change tuning unit;

[0066] On-chip temperature control array 105: 16×16 pixel resistance heating array, temperature control accuracy ±0.05℃;

[0067] 106: A 256-channel superconducting nanowire single-photon detector (SNSPD) array with a detection efficiency ≥90%;

[0068] Cooperative Controller 101: Xilinx Kintex UltraScale+ FPGA, control latency ≤10ns.

[0069] Core parameters:

[0070] Critical state threshold: , (N=256, Nph=4);

[0071] Weak coherent triggering: power is 1% of the pump light, pulse width is 80 ps;

[0072] Coupling error threshold: ;

[0073] Topology Photovoltaic Energy Storage Module 107: Energy storage duration 1.2ms, energy storage efficiency 96.2%.

[0074] The test results are as follows, and the performance comparison under the same conditions is detailed in Table 1:

[0075] Memory programming: The 35 memory patterns are randomly generated binary vectors, each with a dimension of 256 and element values ​​of ±1. The average Hamming distance between patterns is approximately 128, satisfying the quasi-orthogonality requirement of the Hopfield network memory patterns. After encoding, the Hopfield Hamiltonian convergence error is ≤10⁻⁻⁶. 6It satisfies the topological steady-state constraints.

[0076] Critical state triggering: When γ(t) rises to 1.5, it is determined that it has entered the critical state. After injecting an 80ps trigger pulse, the system completes the phase transition in 480ps. The triggering success rate of a single batch experiment is 100%. In 1000 consecutive repeated experiments, the triggering success rate fluctuates between 98.7% and 100%, which shows stable repeatability.

[0077] Lenovo Computing: With an input containing 30% noise and an incomplete pattern, the memory retrieval success rate is 99.2%, and the output topological invariant fluctuation is 2.3%, which is far below the 5% design requirement.

[0078] Topology protection verification: By artificially introducing three waveguide defects, the retrieval success rate only decreased by 0.3%, demonstrating a significant effect of topology protection.

[0079] Photovoltaic energy storage verification: After the calculation is completed, the photonic quantum state is locked and released after 1ms. The system can be directly calculated without re-initialization, and the performance is not significantly reduced.

[0080] Coherence retention time: 1.2ms ± 0.1ms at 4K low temperature, 15μs ± 2μs at room temperature. Topology protection improves coherence time by about 3 times.

[0081] Storage capacity: The measured storage capacity reached 0.134N, approximately 34.3 memory patterns, which is significantly better than the 0.10N level of traditional Hopfield networks.

[0082] Table 1: Performance Comparison under the Same Conditions

[0083] Critical state triggering time not applicable Uncontrollable (passive observation) 480ps Trigger success rate not applicable 30% 98.7%~100% Search success rate 85% 82% 99.2% 4K coherent retention time not applicable 85μs 1.2ms Energy consumption per calculation 12pJ / bit 3.2pJ / bit 0.85fJ / bit Temperature robustness none No topology protection Error rate of 0.5℃ fluctuation <1% Memory capacity 0.10N 0.11N 0.134N System Coordination Delay 100ns 1μs 10ns Energy storage efficiency not applicable 85% 96.2%

[0084] Notes: 28nm CMOS electronic Hopfield data are based on performance indicators of similar chips reported in publicly available literature; existing optical quantum Hopfield scheme data are based on experimental results reported in the PRL paper by Sciarrino et al.; data in this invention are test values ​​of the prototype system.

[0085] Example 2: Embedded Edge AI Inference Accelerator

[0086] This embodiment constructs an embedded accelerator for room-temperature edge computing scenarios to verify its feasibility for implementation.

[0087] System Configuration:

[0088] Photonic chip: Silicon-based photonic chip, integrating a 64-dimensional topological photonic Hopfield network;

[0089] Control circuitry: 65nm CMOS process, integrating a co-controller, ADC / DAC, and power management;

[0090] Readout system: CMOS-compatible single-photon detector array with a detection efficiency of ≥30%;

[0091] Operating modes: AI inference mode + standby photovoltaic energy storage mode.

[0092] Performance testing:

[0093] CIFAR-10 image classification accuracy: 92.5% (end-to-end); here, end-to-end refers to the complete computation process from the original image input through quantum encoding, Hopfield network evolution, to the output classification result, excluding the time and cost of image preprocessing and postprocessing.

[0094] Energy consumption per inference cycle: 5.7 fJ / bit;

[0095] Standby power consumption: 0mW (all-optical energy storage mode);

[0096] Continuous working time: ≥72 hours (photovoltaic energy storage + battery hybrid power supply).

[0097] Example 3: Comparative Experiment of Ablation of Core Mechanism

[0098] This embodiment verifies the synergistic gain of the ternary closed loop by disabling single-dimensional control separately.

[0099] Test configuration: Same as the 256-dimensional system in Example 1, with four control groups set up:

[0100] Group A: Complete three-way closed-loop regulation;

[0101] Group B: Turn off topological waveguide tuning, only retain coding + pump control;

[0102] Group C: Turn off pump parameter adaptation, only retain encoding and waveguide tuning;

[0103] Group D: Open-loop control, no closed-loop regulation.

[0104] The test results are detailed in Table 2.

[0105] Table 2: Ablation Comparison Test Results

[0106] Search success rate 99.2% 92.5% 87.3% 78.1% Phase transition time 480ps 1.2ns 2.5ns 8.3ns Temperature robustness (0.5℃ fluctuation) Error rate <1% Error rate 8% Error rate 12% Error rate 52% Topological invariant fluctuations 2.3% 5.8% 7.2% 18.5%

[0107] Experiments have shown that the ternary collaborative closed-loop scheme significantly outperforms single-dimensional control and open-loop schemes in terms of computational accuracy, speed, and robustness, verifying the innovative value of the core architecture.

[0108] Example 4: Fault Robustness Verification

[0109] This embodiment verifies the system's fault self-recovery capability.

[0110] Test method: Randomly disable different proportions of photon source and waveguide units, and test the change in retrieval success rate.

[0111] The test results are detailed in Table 3.

[0112] Table 3: Fault Robustness Test Results

[0113] 0% 99.2% 0% 5% 98.7% 0.5% 10% 97.4% 1.8% 20% 94.1% 5.1%

[0114] The performance degradation is less than 2% when 10% of the units fail, which is far better than the traditional centralized architecture, verifying the inherent robustness of topology protection and self-organizing architecture.

[0115] Parameter self-optimization implementation method

[0116] The system employs a gradient descent algorithm for parameter self-optimization, with the memory retrieval success rate as the objective function. Initial values ​​of the three types of matrices and adjustment thresholds are used as optimization variables. Parameters are iteratively updated based on feedback data from each batch of calculations, with the iteration step size adjusted via an adaptive learning rate to ensure convergence stability. A parameter iteration is completed every 1000 calculations. In typical scenarios, the phase transition triggering accuracy can be improved by 3% to 5% after 100,000 calculations.

[0117] The above embodiments are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the core concept and principles of this application should be included within the protection scope of this application.

[0118] The minimum applicable scale dimension of this invention is 32; below this value, the statistical smoothing effect is insufficient. Theoretically, there is no upper limit to the scale, and performance continuously optimizes with the increase in the number of modes. The applicable frequency bands include the entire optical spectrum from visible light to near-infrared and mid-infrared. Different bands only require adjustment of the waveguide size, and the core mechanism remains completely consistent. It can superimpose multi-wavelength multiplexing, entanglement enhancement, and on-chip detector integration schemes, all without deviating from the core architecture of ternary synergistic control, seamlessly extending to quantum optimization solutions, sensing and imaging, and other fields, forming a "one-base, multi-effect" technology matrix.

[0119] The technical solution of this invention can be mass-produced using mature silicon-based and silicon nitride photonics processing technologies, is compatible with existing CMOS production lines, and has low mass production modification costs. The system can be widely used in multiple fields such as AI acceleration, edge computing, in-memory computing, and security sensing, and has extremely high economic and strategic value.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive control method for a topological optical quantum Hopfield network, characterized in that, This invention relates to a photonic quantum computing chip integrating a programmable multiphoton interference network and a reconfigurable topological photonic structure, comprising the following steps: S1 System initialization and topological steady-state anchoring: Constructing a ternary collaborative control architecture, initializing the memory-encoded phase matrix, pump control parameter matrix, and topological waveguide configuration matrix; anchoring global topological steady-state constraints: maintaining the stable eigenvalue distribution of the Hopfield memory matrix throughout the computation process, ensuring that the fluctuation amplitudes of the topological charge order of the memory-encoded phase matrix, the number of spectral branches of the pump control parameter matrix, and the energy band Chern number of the topological waveguide configuration matrix are all controlled within preset thresholds; S2 Memory mode quantum interference encoding: Encoding the target memory mode set into the memory-encoded phase matrix through multiphoton quantum interference, so that the system steady-state output corresponds to the pre-stored memory attractor; the encoding process satisfies the mathematical correspondence between multiphoton quantum interference and the p-body Hopfield Hamiltonian; S3 Active identification and phase transition triggering of preheated critical state: Construct a critical state identification function, monitor the statistical characteristics and quantum overlap of photon output in real time and calculate the function value; when the function value enters the critical interval, the system is determined to have entered the preheated critical state, triggered by a weak coherent pulse of pump light, inducing the system to complete the associative memory phase transition, outputting the steady-state photonic quantum state of the corresponding memory attractor, and realizing associative memory recall; the power of the weak coherent pulse is 0.5%~2% of the calculated pump light power, and the pulse width is ≤100ps; S4 Ternary closed-loop coordinated control and topological state maintenance: Execute closed-loop control according to a preset order, calculate the coupling error η of the three types of matrices in real time, and repeat the control when the coupling error η is higher than the preset threshold until the topological classification isomorphic constraint is met; the topological classification isomorphic constraint is that the topological charge order of the information configuration, the number of energy spectrum branches of the energy parameter, and the energy band Chern number of the physical structure correspond one-to-one in the topological classification, and the relative deviation does not exceed the preset threshold; S5 Associative computation and result readout: The external input mode is encoded as a photonic quantum state and injected into the system. The system automatically converges to the nearest memory attractor under the drive of a ternary closed loop. The output state distribution is read through a single-photon detector array to complete the associative memory computation. Topology protection during the computation process makes the system immune to local disturbances.

2. The method according to claim 1, characterized in that, In step S4, the information configuration update specifically involves updating the memory encoding phase matrix based on the target memory pattern or external input, and determining the coherence rules and information order of the computation task.

3. The method according to claim 1, characterized in that, In step S4, the energy parameter regulation specifically involves: based on the updated memory-encoded phase matrix, using a PID algorithm to regulate the frequency, power, phase, and pulse timing of the pump light to match the energy requirements of the topological photonic quantum state, and updating the pump regulation parameter matrix.

4. The method according to claim 1, characterized in that, In step S4, physical structure tuning specifically involves: based on the control results of information configuration and energy parameters, dynamically tuning the waveguide configuration and coupling strength of the reconfigurable topological photonic network, updating the topological waveguide configuration matrix, and providing a material carrier for topological protection in the computation process.

5. The method according to claim 1, characterized in that, In step S4, the coupling error verification and topological classification isomorphism constraint are specifically performed as follows: the coupling error η of the three types of matrices after normalization is calculated in real time. When the coupling error η is higher than a preset threshold, the adjustment step is repeated until the topological classification isomorphism constraint is satisfied. The formula for calculating the coupling error η is: in , , The normalized memory-encoded phase matrix, pump control parameter matrix, and topological waveguide configuration matrix are as follows: For unit tensor components.

6. The method according to claim 1, characterized in that, In step S4, closed-loop control is executed sequentially in the order of information configuration guidance, energy parameter adaptation, and physical structure tuning.

7. The method according to claim 1, characterized in that, In step S3, the expression for the critical state identification function is: in for The photon count output by the system at any given time. The standard deviation of the photon number statistics. The statistical mean of the photon number. The quantum overlap between the system output state and the target memory state; when The system is determined to have entered the preheating critical state at a certain time; the threshold value and system parameters satisfy a quantitative relationship: , ,in For photon mode dimension, The number of photons involved in the interference.

8. The method according to claim 1, characterized in that, System memory capacity and photon mode dimension Positive correlation, memory capacity under 2-body interaction is not less than .

9. The method according to claim 1, characterized in that, It also includes step S6: S6 Topological optical energy storage and parameter self-optimization: During the interval between computing tasks, the highly coherent optical quantum state carrying memory information is locked through the topological resonant cavity, and the information order and energy quantum are stored in an integrated manner. Based on historical calculation results and state parameters, the initial values ​​and control thresholds of the three types of matrices are iteratively updated to optimize the phase transition triggering accuracy and achieve continuous improvement in the system's computing power.

10. A topological optical quantum Hopfield network system, characterized in that, Deployed on an integrated photonic quantum chip for implementing the method described in any one of claims 1 to 9, the method comprises: a programmable multiphoton interference network, composed of a cascaded Mach-Zehnder interferometer array, for realizing photonic quantum simulation of arbitrary unitary transformations and p-body Hopfield Hamiltonians; an entangled multiphoton source array, optically connected to the programmable multiphoton interference network, for generating high-purity, highly indistinguishable entangled photons; a reconfigurable topology-protected waveguide array, integrated with the programmable multiphoton interference network on the same chip, for providing a topology-protected transmission channel for photonic quantum states; an on-chip temperature control array, thermally coupled to the reconfigurable topology-protected waveguide array, for suppressing the influence of thermal crosstalk on quantum coherence; and a cooperative controller, electrically connected to all the above modules, for performing ternary closed-loop control, critical state identification triggering, and calculation result readout.

11. The system according to claim 10, characterized in that, The dynamic tuning method of the reconfigurable topology-protected waveguide array is selected from any one or a combination of at least two of phase change material tuning, microelectromechanical system (MEMS) tuning, electro-optic effect tuning, and thermo-optic effect tuning. The phase change material tuning is achieved by switching the crystalline to amorphous state of Ge2Sb2Te5 or Sb2Se3 phase change materials to change the effective refractive index and topological band structure of the waveguide. The MEMS tuning is achieved by adjusting the coupling spacing and coupling strength between waveguides through the displacement of the MEMS structure. The electro-optic effect tuning is achieved by tuning the resonant frequency of the microring resonator through the electro-optic effect to reconstruct the coupling topology of the optical network. The thermo-optic effect tuning is achieved by tuning the waveguide phase through the thermo-optic effect to realize the programmable configuration of the topological photonic network.

12. The system according to claim 10, characterized in that, It also includes a topological optical energy storage module, which is composed of multiple topological resonant cavity arrays and connected to the optical path of the programmable multiphoton interference network; the topological optical energy storage module switches between energy storage and release states through a tunable coupler, which is used to lock highly coherent photonic quantum states during computation intervals to achieve integrated information-energy storage.

13. A performance testing method for a topological optical quantum Hopfield network, used to determine the control method described in claim 1, characterized in that, Includes the following steps: S1 Statistical Scaling Law Verification: Photon output distribution under different photon mode dimensions is measured using a single-photon detector array. Memory retrieval accuracy is calculated, and the retrieval error is verified to meet the requirements. The statistical scaling law; S2. Verification of topological invariant consistency: The topological charge order of the memory-encoded phase matrix, the number of energy spectral branches of the pump control parameter matrix, and the energy band Chern number of the topological waveguide configuration matrix are measured by phase demodulation, spectral analysis, and band characterization, respectively, to verify whether the three satisfy the topological classification isomorphism constraint; S3. Verification of perturbation self-recovery: Waveguide defects or photon source failures are artificially introduced, and the recovery time and change amplitude of the system's computational performance are measured; S4. Verification of phase transition triggering characteristics: The critical state identification function curve and trigger pulse timing are measured to verify the window characteristics and time scale of phase transition triggering; When all four characteristics meet the preset judgment criteria, it is determined that the tested system adopts the ternary synergistic control technology described in claim 1.

14. The detection method according to claim 13, characterized in that, The preset judgment criteria include: statistical scaling law, fitting correlation coefficient. The three types of topological invariants satisfy isomorphic mapping and have a deviation of ≤5%, the computational performance degradation after 10% element failure is <10%, and the phase transition trigger time is ≤500ps.

15. A photonic quantum computing chip, characterized in that, The method includes a memory, a processor, and an integrated photonic structure, wherein the memory stores a computer program, the processor executes the computer program and drives the integrated photonic structure to implement the method according to any one of claims 1 to 9.

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