Quantum photon hybrid interface adaptive calculation method

Abstract

The invention discloses a quantum photon hybrid interface adaptive calculation method, and relates to the technical field of quantum calculation and photon integration. Firstly, the current load state of the multi-modal data interface is monitored in real time, and an interface state feature matrix is obtained; then inputting the state matrix into a quantum photon hybrid calculation module, and optimizing an interface type, a transmission protocol and interface parameters in real time through a quantum state feedback and quantum variational optimization algorithm (QAOA) to obtain optimal adaptive configuration of the interface; and dynamically adjusting interface parameters according to an optimization result to realize that the random read delay is less than or equal to 40 microseconds, the sequential read delay is less than or equal to 25 microseconds, the write delay is less than or equal to 400 microseconds, the target identification response time is less than or equal to 0.5 ms, and the target tracking response time is 0.5-1 second. The dynamic intelligent optimization of the interface parameters is realized, the interface transmission efficiency and the real-time response capability are remarkably improved, and the method is suitable for the fields of artificial intelligence, intelligent transportation and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of quantum computing and photonic integrated computing, and particularly relates to an interface adaptive processing technology based on a quantum-photonic hybrid computing architecture, and more particularly to an interface adaptive optimization calculation method for multi-modal data transmission and computing tasks in a quantum bit and photonic chip integration architecture. Background Art

[0002] With the rapid development of artificial intelligence, big data analysis, intelligent perception, and high-performance computing technologies, traditional classical electronic computing architectures have gradually revealed bottleneck problems such as limited computing speed, high power consumption, insufficient interface data throughput, and poor real-time response capabilities. Multi-modal data fusion processing has put forward higher requirements for the parallelism, high-speed data transmission capabilities, and interface adaptive response performance of computing architectures. In recent years, quantum computing technology, relying on the superposition, entanglement, and quantum coherence effects of quantum bits, has demonstrated significant advantages in dealing with complex optimization problems and large-scale parallel computing tasks. At the same time, photonic computing technology, with its inherent low latency, high bandwidth, and ultra-high-speed data transmission characteristics, has shown great potential in the field of interface data transmission. However, the current research on quantum computing technology and photonic computing technology is still in a relatively independent stage, and the synergistic computing advantages and interface interaction capabilities of the two have not been fully exploited. In particular, the research on adaptive dynamic optimization strategies for interfaces is still in its infancy or at a starting stage.

[0003] In the computing module of a multi-modal data processing unit, multiple technical indicators such as interface type, maximum transmission speed, random read / write time, sequential read / write latency, target recognition, and tracking response time jointly determine the computing efficiency and response performance of the module, which poses strict requirements on the real-time, flexibility, and adaptive capabilities of the computing architecture. Traditional interface computing schemes usually adopt fixed architectures and static configurations, and cannot perform real-time adaptive adjustments according to dynamic data transmission and computing requirements. It is difficult to fully exploit the potential performance of quantum-photonic fusion computing modules, resulting in waste of computing resources and reduced data processing efficiency and computing flexibility. Therefore, there is an urgent need for an adaptive interface optimization method that can fully integrate the parallel acceleration advantages of quantum computing and the high-speed interface transmission capabilities of photonic computing to achieve efficient real-time processing of multi-modal data and dynamic optimization configuration of computing resources.

[0004] The present invention proposes an interface adaptive computing method based on a quantum-photonic hybrid architecture. Through the collaborative interaction and dynamic scheduling between qubits and photonic chips, intelligent adaptive optimization configuration of multi-modal data interface types, transmission speeds, read-write latencies, and feature extraction is achieved, effectively solving the key problems of isolated interfaces between quantum computing and photonic computing, weak dynamic regulation capabilities, and insufficient resource adaptability in the prior art. Thus, on the premise of ensuring high throughput and low latency of the interface, the comprehensive performance and resource advantages of the quantum-photonic hybrid computing architecture are maximally exerted. Summary of the Invention

[0005] The purpose of the present invention is to overcome the technical problems faced by multi-modal data processing units in the existing quantum-photonic hybrid computing architecture, such as poor interface adaptability, insufficient dynamic transmission efficiency, inflexible heterogeneous computing resource configuration, and inability to respond efficiently and in real time. A quantum-photonic hybrid interface adaptive computing method based on the integration of qubits and photonic chips is proposed. Specifically, the present invention constructs an interface dynamic management module that integrates a quantum entanglement state scheduling mechanism and a photonic on-chip network (PNoC). Based on an adaptive interface control algorithm, considering multiple key interface performance parameters such as interface type, maximum transfer rate, random read latency, sequential read latency, and write latency, the present invention realizes the efficient adaptive configuration of the interface, the dynamic intelligent scheduling of data flow, and the real-time optimization of heterogeneous computing resources, ensuring that performance indicators such as the feature extraction time of multi-modal data, the target recognition, and the tracking response time reach a dynamic adaptive balance.

[0006] The present invention specifically designs an interface performance optimization algorithm based on quantum-photonic coupling control. By introducing a quantum parallel processing engine (QPPE) composed of qubit entanglement and superposition states and cooperating with an ultra-low latency photonic chip bus (ULL-PCB), intelligent adaptive selection of interface types and transmission protocols is achieved. The method can dynamically adjust the random read time to less than 40 microseconds, the sequential read time to less than 400 microseconds, and the write time to within 25 microseconds under the condition of an interface transmission rate of 12 Mbps by real-time quantitatively monitoring the data transmission state and the computing task load, ensuring a significant reduction in the interface response time and stabilizing the processing time of the feature extraction task within the range of 0.5 - 1 second.

[0007] Meanwhile, the present invention adopts a programmable and configurable quantum - photonic interface unit (PQPIU), combined with a real - time adaptive interface performance prediction model. According to the requirements of the current multimodal computing task for the target recognition response time (within 0.5 milliseconds), target tracking time (less than 1 second), information interaction response delay (within the range of 3 - 5 seconds), and the number of parallel - processed targets (30 - 100), it dynamically adjusts the interface bandwidth allocation and data transmission mode, quickly completes the intelligent optimization configuration and scheduling of interface resources, thereby maximizing the advantages of the high - parallel computing power of quantum computing and the high - speed transmission performance of the photonic interface, and effectively solving the prominent problems such as computational resource waste, poor real - time performance, and weak dynamic adaptation ability under the traditional interface architecture.

[0008] To achieve the above - mentioned technical objectives, the present invention proposes a structural design method and implementation mechanism for an interface adaptive computing device based on a quantum - photonic hybrid architecture. The method specifically includes the following technical solutions: First, a multimodal data interface hardware architecture based on the coupling of a quantum bit array (QBA) and a photonic chip network (PIN) is constructed, specifically including a quantum acceleration core (QAC), a photonic network - on - chip (PNoC) interface array, and an adaptive interface control unit (AICU). Among them, the quantum acceleration core is responsible for efficiently executing feature extraction, target recognition, and intelligent decision - making tasks in parallel by using the superposition state and entangled state of quantum bits; the photonic chip network realizes high - speed data transmission and interface interaction with a maximum speed of up to 12 Mbps between modules through wavelength - division multiplexing (WDM) technology, with a random read time of less than 40 microseconds, a sequential read time of no more than 400 microseconds, and a write delay of no more than 25 microseconds.

[0009] The present invention establishes a real - time adaptive management and feedback control mechanism for quantum - photonic interface performance, specifically including: (1)Design an automatic selection mechanism for interface transmission protocols based on quantum state-assisted feedback (Quantum-assisted Adaptive Protocol Selection, QAPS). Under the performance constraints of an average random read / write latency of 40 microseconds, an average sequential read latency of 25 microseconds, and an average write latency of 400 microseconds, achieve real-time adaptive optimization of the interface protocol and interface type (such as SPI, I2C, PCIe, etc.). (2)Build a dynamic interface parameter prediction and performance optimization model (Dynamic Interface Performance Prediction and Optimization Model, DIPM). Utilize real-time multimodal data stream load information to dynamically optimize the interface parameter settings, reduce the target recognition time to 0.5 milliseconds and the target tracking time to 0.51 seconds, and control the information interaction response time within 3 - 5 seconds in a multi-target environment (with 30 - 100 targets). (3)Build a quantum-photonic hybrid interface parameter real-time programmable and adaptive scheduling engine (Quantum-photonic Real-time Interface Scheduler, QPIS). Use quantum algorithms (such as the quantum variational optimization algorithm QAOA) and machine learning models (such as deep reinforcement learning Deep Reinforcement Learning, DRL) to automatically predict, evaluate, and real-time adjust the data interface type, transmission rate, access mode, and bandwidth configuration, achieving real-time, precise, and intelligent dynamic scheduling of the module interface parameters, and ensuring that the interface performance of the quantum photon fusion module is always close to the global optimal state under different data modes.

[0010] Based on the above quantum photon hybrid interface adaptive computing method, the present invention further designs a real-time interface performance evaluation and adaptive dynamic optimization regulation model. By embedding a hybrid feedback mechanism for quantum state monitoring and real-time perception of photon data streams inside the quantum photon hybrid computing module, the current data stream load intensity, task computing density, and multi-modal data interaction characteristics of the interface are quantified in real time. Based on the interface characteristic parameters monitored in real time, through quantum state coherence regulation and wavelength dynamic configuration technology, the interface data transmission path is continuously optimized, and the allocation strategies of the interface transmission bandwidth and quantum computing resources are dynamically adjusted. Further combined with a dynamic decision-making model, according to the real-time performance requirements of tasks and the variation law of interface load, the feature extraction time is reduced from the millisecond level of the traditional architecture to the microsecond level (average 40 microseconds for random reading, average 25 microseconds for sequential reading, and average 400 microseconds for writing), the target recognition response time is optimized to within 0.5 milliseconds, and the real-time tracking time of the target tracking task is stabilized within 0.5 to 1 second. This real-time feedback regulation method can effectively handle high-concurrency scenarios (the number of targets is 30 to 100), dynamically adjust the computing strategy of the quantum photon hybrid interface module, continuously optimize the interface performance, meet the adaptive computing requirements of complex and diverse multi-modal data interfaces, and greatly improve the computing efficiency and dynamic adaptive response ability of the quantum-photon hybrid interface module.

[0011] The quantum photon hybrid computing module of the present invention is equipped with a Quantum Entanglement-assisted Photonic Network Unit (QE-PNU) inside, and adopts an integrated packaging method combining silicon-based optoelectronic technology (Si-Photonics) and superconducting qubits. The volume of a single module is less than 150 mm², the static power consumption of the interface unit is less than 0.5W, and the dynamic peak power consumption is less than 500 mW. The maximum data transmission rate of the communication interface inside the module stably reaches 12 Mbps, the interface transmission jitter is lower than 5 microseconds under full-load operating conditions, and the bit error rate (BER) is lower than 10^-9.

[0012] The module can simultaneously support the rapid dynamic switching and intelligent adaptation of multiple interface types such as Ethernet, CAN bus, SPI, UART, and PCIe. The interface adaptive switching delay is less than 25 microseconds, the interface parameter dynamic reconfiguration time is less than 40 microseconds, and the time for the maximum transmission speed to recover to the rated speed (12 Mbps) after interface reconfiguration is less than 400 microseconds.

[0013] In terms of feature extraction and target recognition performance, the module of the present invention can achieve a throughput of more than 1000 frames per second (fps) for multi-modal data processing. The feature extraction time of a single-frame image or data stream target is stable between 20 and 40 microseconds. The dynamic target recognition rate exceeds 99.2%. The success rate of parallel real-time tracking of multiple targets (30 - 100) reaches more than 98.5%. The average information interaction response time is within 0.5 to 1 second. At the same time, through the internal quantum state adaptive management unit for computing task prediction and load balancing control, the overall computing energy efficiency of the module is increased by about 50% or more, and the power consumption is reduced by about 35%.

[0014] In addition, the module also has a high degree of programmability and configurability. Users can, according to actual application requirements, flexibly configure the interface type, maximum transmission rate (up to 12 Mbps), random and sequential read / write timings (less than 40 microseconds and 25 microseconds, 400 microseconds respectively) through the Quantum-Photonic Interface Management Platform (QPIMP). The configuration completion time does not exceed 2 seconds. It also supports remote real-time monitoring and online performance update and maintenance functions to ensure the long-term operation stability, maintainability and scalability of the system.

[0015] Through the above design of the interface adaptive mechanism of the quantum photon hybrid computing architecture, the overall computing efficiency of the present invention is increased by 3 to 5 times compared with the traditional single computing method, the power consumption is reduced by about 40%, and the interface response speed is increased by an average of 60%. It provides efficient, stable and scalable technical support and advanced solutions for fields such as artificial intelligence, autonomous driving, intelligent security and large-scale data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings here are incorporated into the specification and form a part of this specification, indicating the embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Attached Figure 1 is the overall technical roadmap of the quantum photon hybrid interface adaptive computing.

[0019] Attached Figure 2 is the detailed flowchart of the dynamic optimization of the quantum state-assisted interface adaptive parameters.

[0020] Attached Figure 3It is a technical flowchart of interface dynamic intelligent decision-making technology based on deep reinforcement learning.

[0021] Appendix Figure 4 It is a detailed technical flowchart of real-time feedback and regulation of the performance of the quantum photon hybrid computing interface.

[0022] Appendix Figure 5 It is a roadmap for the evaluation and verification of the adaptive computing performance of the quantum photon hybrid interface. Specific implementation manners

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall also fall within the protection scope of the present invention.

[0024] It should be noted that all directional indications (such as "up", "down", "left", "right", "front", "rear", etc.) in the embodiments of the present invention are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0025] In the present invention, the descriptions such as "first", "second" and the like are only used to distinguish different components or features, and cannot be understood as limiting their relative importance or quantity. Thus, the features defined with "first" and "second" may clearly or implicitly include at least one of the features. The technical solutions between the embodiments can be combined with each other, but must be combined on the premise that those of ordinary skill in the art can implement them. When the combination of technical solutions conflicts or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0026] The present invention mainly aims at the deficiencies of traditional multi-modal data processing units in high-speed interfaces, parallel access, dynamic optimization, and real-time computing, and proposes a quantum-photonic hybrid interface adaptive computing method. By combining quantum computing with photonic integrated computing, this method fully utilizes the parallel search and entanglement capabilities of quantum bits and the advantages of high-speed transmission and scalability of photons to achieve efficient reading, writing, and real-time processing of multi-modal data. Especially when identifying, tracking, and information interaction in complex target or multi-target environments, breakthroughs can be made in key indicators such as bandwidth, latency, and concurrent performance. At the same time, the present invention introduces an adaptive interface management and dynamic scheduling mechanism, enabling parameters such as interface type, read-write latency, and sequential access rate to be automatically optimized according to load and target requirements, greatly improving the overall throughput and resource utilization efficiency of the system, and reducing bottlenecks caused by access conflicts or improper configurations in multi-modal scenarios.

[0027] Based on the quantum-photonic fusion architecture, the present invention supports deep coupling of quantum bit arrays and photonic chip networks at the hardware level, can be compatible with multiple interface modes, and stably transmits data at a bandwidth of 12 Mbps or even higher. The random read time is stably controlled within 40 microseconds, the sequential read time is within 25 microseconds, and the write time is within 400 microseconds, significantly reducing the response latency of the interface for high-frequency and multi-scale data access. At the software level, a quantum parallel optimization algorithm for multi-target recognition, tracking, and information interaction is designed. Through the parallel advantages of quantum state superposition and entanglement states in feature extraction and target prediction, combined with the low-latency characteristics of photonic data transmission, adaptive acceleration of target recognition time, target tracking time, and information interaction response time is achieved, and multi-target task requirements can be completed within the range of 0.5 milliseconds to several seconds. The whole method not only meets the pain points of traditional computing systems that are difficult to balance high speed and low power consumption in parallel optimization scenarios, but also provides leapfrog interface adaptive optimization and quantum-photonic collaborative processing capabilities for various application fields such as autonomous driving, intelligent security, national defense monitoring, and high-performance computing. Embodiment 1

[0028] This embodiment focuses on the systematic design and working process of the "quantum-photonic hybrid interface adaptive computing method" at the overall architecture level, corresponding to Figure 1 The system mainly consists of functional modules such as a quantum-photonic hybrid computing master control unit (S101), a quantum acceleration processing core QAC (S102), a photonic chip integrated network module PIN (S103), an adaptive interface management and dynamic optimization unit (S104), a quantum state feedback regulation unit (S107), and a multi-modal data interface input unit (S107).

[0029] In the specific operation process, multi-modal data is first collected and preliminarily integrated by the multi-modal data interface input unit (S107), covering key performance indicators such as interface type, maximum transmission rate (e.g., 12 Mbps), random read time (e.g., 40 microseconds), sequential read time (e.g., 25 microseconds), write time (e.g., 400 microseconds), etc. This unit performs timestamp alignment and preliminary filtering on data streams from different sources to ensure that they can be uniformly incorporated into the quantum photon hybrid computing architecture for adaptive processing in subsequent stages. Meanwhile, the quantum state feedback control unit (S107) continuously collects interface load and performance status information to provide real-time reference for subsequent quantum parallel optimization.

[0030] After receiving the operating status from the multi-modal data interface, the adaptive interface management and dynamic optimization unit (S104) comprehensively analyzes the current interface type and performance parameters and generates a set of optimal or approximately optimal dynamic allocation strategies. This strategy takes into account the target latency requirements for random read, sequential read, and write operations (40 microseconds, 25 microseconds, and 400 microseconds respectively), and combines the parallel computing capabilities of the quantum acceleration processing core QAC (S102) and the high-bandwidth, low-latency transmission path provided by the photon chip integrated network module PIN (S103) to jointly achieve timely regulation and upgrade of interface performance. For example, when it is detected that the task type requires frequent random read operations, the system will adaptively reconfigure the interface protocol and buffer, maintain or restore the transmission rate to 12 Mbps within a protocol switching delay of no more than 25 microseconds, and ensure that the random read latency is maintained within 40 microseconds.

[0031] The quantum acceleration processing core QAC (S102) then utilizes the advantages of quantum parallel search and entangled states at the hardware level to deeply accelerate high-frequency data access and target recognition. Through parallel optimization on the quantum side and high-speed transmission of the photon chip network, the system can compress the target recognition response time to the order of 0.5 milliseconds in complex multi-modal scenarios (the number of targets can reach 30 - 100), control the target tracking time within the range of 0.5 seconds to 1 second, and complete the main interaction processing within 3 - 5 seconds in terms of information interaction. This can significantly reduce problems such as high latency, bandwidth limitation, and task conflicts faced by traditional electronic buses and single computing architectures.

[0032] Finally, in this embodiment, the overall data process is coordinated and managed by the quantum photon hybrid computing master control unit (S101). When the system detects bottlenecks in target recognition or substandard interface performance in high-load scenarios, the adaptive interface management and dynamic optimization unit (S104) will perform online scheduling in combination with the quantum state feedback regulation unit (S107), dynamically adjusting the interface protocol, read / write strategy, and photon resource allocation, enabling the system to complete adaptive reconfiguration within milliseconds. This not only stabilizes the real-time performance of the multimodal data processing unit in high-concurrency scenarios but also optimizes random read / write, sequential read / write, and write latency, laying a high-performance and highly reliable interface foundation for more complex target recognition and tracking tasks in subsequent embodiments. Embodiment 2

[0033] This embodiment mainly elaborates on the core process of the quantum photon hybrid interface adaptive computing method in interface dynamic scheduling and adaptive optimization, corresponding to the Figure 2 key functional components such as the data flow load real-time monitoring unit (S201), quantum state feedback feature extraction module (S202), quantum-assisted adaptive protocol selection unit (S203), interface performance prediction evaluation and feedback control module (S206), and interface dynamic parameter real-time adjustment execution module (S207). By establishing a close association between the interface usage status and quantum state feedback, this embodiment can adaptively switch and configure the interface transmission protocol and key performance parameters within milliseconds of latency.

[0034] In a high-concurrency scenario of multimodal data, first, the data flow load real-time monitoring unit (S201) continuously collects key information such as the current transmission rate requirements of the interface, the usage frequency of random / sequential read / write modes, and write latency, and transmits these real-time load metrics to the quantum state feedback feature extraction module (S202). This module uses quantum parallel search and entangled state features to quickly analyze the load environment of the current interface data flow. Especially when the external request mode frequently switches, random read / write operations suddenly increase, or sequential read operations are massively concurrent, the quantum side can obtain a relatively comprehensive portrait of the interface performance state in an extremely short time.

[0035] The quantum-assisted adaptive protocol selection unit (S203) determines the protocol mode to be adopted by the current interface based on the quantum state feedback characteristics and the system-predefined latency targets (such as no more than 40 microseconds for random reads, no more than 25 microseconds for sequential reads, no more than 400 microseconds for writes, etc.), which may involve SPI, I2C, PCIe, and even customized high-speed protocols. When the selection unit identifies that the real-time load has exceeded the established threshold, it will give priority to switching to a protocol type more suitable for random access or sequential read / write, and dynamically configure the interface bandwidth and read / write cache policies according to different module requirements. The interface performance prediction evaluation and feedback control module (S206) further evaluates the execution effect after each protocol switch or parameter adjustment, including whether the interface protocol switch latency is less than 25 microseconds, whether the rated rate of 12 Mbps is restored within 400 microseconds after reconfiguration, and whether the read / write latency index returns to the ideal range, etc. If it is detected that there is still a performance deviation, this module will generate new optimization instructions and hand them over to the interface dynamic parameter real-time adjustment execution module (S207) for implementation.

[0036] The interface dynamic parameter real-time adjustment execution module (S207) is specifically responsible for deploying the decisions of quantum-assisted adaptive protocol selection to the actual hardware interface level, including switching the protocol stack, adjusting the DMA or cache mechanism, changing the transmission bandwidth, etc. Since the present invention utilizes the parallel capabilities of the photonic chip integrated network and the quantum acceleration processing core, it can still achieve precise control of random and sequential read / write latencies on the basis of maintaining a peak data throughput of 12 Mbps. Especially at moments when the target recognition and tracking intensity are relatively high, this module can complete adaptive reconfiguration within the millisecond level, ensuring that random read operations are maintained within 40 microseconds, sequential reads are controlled within 25 microseconds, and write time consumption is less than 400 microseconds. In this way, not only is the efficiency of multi-modal data feature extraction greatly improved, but also a stable and high-speed underlying interface guarantee is provided for subsequent recognition and tracking tasks for 30 to 100 concurrent targets.

[0037] In summary, through the cooperation of the quantum state feedback feature extraction module (S202) and the quantum-assisted adaptive protocol selection unit (S203) in this embodiment, combined with the online monitoring of the interface performance prediction evaluation and feedback control module (S206) and the implementation of the interface dynamic parameter real-time adjustment execution module (S207), a closed-loop optimization ability for the interface protocol type and key latency indicators is formed. Thereby, in a multi-modal and high-concurrency environment, read / write latencies are effectively reduced, bandwidth waste or coordination imbalance is avoided, enabling the entire quantum photonic fusion computing architecture to achieve millisecond-level real-time adaptive adjustment for different data scenarios and access modes, laying a high-performance interface foundation for quantum computing acceleration in Embodiment 3 and subsequent higher parallelism scenarios. Embodiment 3

[0038] This embodiment will be described around the dynamic intelligent decision-making process of the interface constructed based on deep reinforcement learning (DRL), which corresponds to Figure 3 Through the collaborative action of the data interface real-time monitoring and load evaluation module (S301), the deep reinforcement learning decision-making engine (S302), the interface performance target evaluation model (S303), the interface parameter action space and state space definition unit (S304), the dynamic policy generation and interface configuration execution module (S305), and the interface performance reward and punishment feedback unit (S306), the present invention can achieve instant tuning of the interface read / write latency and transmission bandwidth in a multi-objective parallel environment, significantly improving the adaptive ability of the system in high-concurrency scenarios.

[0039] In the large-scale transmission of multi-source heterogeneous data streams, the data interface real-time monitoring and load evaluation module (S301) first captures and analyzes the current interface operating state, including existing protocol types (such as SPI, I2C, or PCIe, etc.), real-time bandwidth utilization, the number of random and sequential read / write operations, and the write latency trend. The module encapsulates this key information into a state description and reports it to the deep reinforcement learning decision-making engine (S302). The interface performance target evaluation model (S303) sets optimization goals for this deep learning process, such as maximizing the interface throughput capacity and minimizing the protocol switching latency while ensuring that the random read latency ≤ 40 microseconds, the sequential read latency ≤ 25 microseconds, and the write latency ≤ 400 microseconds.

[0040] When the deep reinforcement learning decision-making engine (S302) receives the state vector, it will select an action that can make the overall performance optimal or approximately optimal from the action set according to the adjustable actions (such as switching protocol types, modifying bandwidth configurations, changing cache policies, etc.) specified by the interface parameter action space and state space definition unit (S304), combined with historical execution experience and current environmental feedback. Next, the dynamic policy generation and interface configuration execution module (S305) updates the interface configuration in real-time according to the action instructions output by the decision-making engine, including switching to a protocol more suitable for random reads, allocating more cache to relieve the pressure of high-concurrency writes, or moderately increasing the transmission bandwidth allocation of the photonic on-chip network in the sequential read scenario. After the execution, the interface performance reward and punishment feedback unit (S306) evaluates the deep reinforcement learning decision according to the new round of monitoring results. For example, positive rewards are given to actions that significantly reduce latency and successfully maintain a peak rate of 12 Mbps; if it is identified that the protocol switching takes too long or the read / write latency exceeds the standard, a penalty value is given to promote the decision-making model to be improved in the next iteration.

[0041] This embodiment effectively combines the adaptability of deep reinforcement learning in uncertain and multi-objective environments with the high bandwidth and low latency characteristics of the quantum photon fusion interface architecture. When extracting and identifying features for 30 to 100 targets simultaneously, it can still maintain effective control over random read / write, sequential read / write, and write latency. With the continuous learning and optimization mechanism of DRL, the system can not only quickly converge to the optimal or near-optimal interface configuration when the load peak and data pattern change drastically, but also consolidate existing achievements through continuous learning and broaden the applicable range under different sensor combinations or application requirements. This deep learning-driven interface dynamic decision-making method provides sustainable high-efficiency underlying support for multi-modal data processing tasks and also provides strong guarantees for the real-time performance and reliability of target recognition and tracking. Embodiment 4

[0042] This embodiment describes the overall process of real-time feedback and regulation of the performance of the quantum photon hybrid computing interface, corresponding to the appendix Figure 4 Through functional components such as the interface real-time performance index monitoring module (S401), photon data stream state real-time perception and feature analysis unit (S402), quantum entanglement state feedback information processing module (S403), interface performance prediction and real-time optimization unit (S404), interface type and protocol real-time adaptive switching decision module (S405), and interface parameter dynamic reconfiguration and distribution unit (S406), the present invention can achieve closed-loop regulation of the multi-modal data processing interface, ensuring high efficiency and low latency of target recognition and tracking under large-scale parallel tasks.

[0043] When the system is processing a concurrent scenario of 30 to 100 targets, the interface real-time performance index monitoring module (S401) samples key performance data periodically or triggered by events, including the current actual transmission rate of the interface (such as approaching the peak of 12 Mbps), random and sequential read latencies (such as aiming at target values of 40 microseconds and 25 microseconds respectively), and write latency (not exceeding 400 microseconds). The photon data stream state real-time perception and feature analysis unit (S402) monitors the bandwidth usage of the on-chip photon network, the load level of the wavelength division multiplexing (WDM) channels, and possible signs of channel congestion, and summarizes this information to the quantum entanglement state feedback information processing module (S403). This module uses quantum parallel processing to comprehensively evaluate the above monitoring data and determines whether there is a significant gap between the current interface configuration and the load demand in a very short time through entanglement state search.

[0044] If a deviation is detected between the interface performance and the actual application requirements (e.g., the random read latency increases to more than 40 microseconds or the sequential read latency approaches the upper limit of 25 microseconds), the interface performance prediction and real-time optimization unit (S404) will generate an optimization or switching recommendation for the interface type and protocol real-time adaptive switching decision module (S405) based on the search results provided by the quantum feedback, which may include reconfiguring the interface protocol, expanding the bandwidth, optimizing the write cache policy, etc. Once the decision is confirmed, the interface parameter dynamic reconfiguration and distribution unit (S406) will immediately perform adjustment operations at the hardware level, such as switching the interface from SPI to PCIe mode (provided that the protocol switching latency is still below 25 microseconds), or restoring to the 12 Mbps peak rate within 400 microseconds, to ensure that the system continues to meet the high-concurrency, multi-modal read and write requirements.

[0045] Through such a real-time feedback and control mechanism, this embodiment can not only quickly complete the adaptive switching in scenarios where the random read or sequential read frequency suddenly increases or the write pressure unexpectedly increases, but also maintain the key data path of target recognition and tracking processing at a low-latency and high-bandwidth level by leveraging the synergistic advantages of quantum and photon. Especially when the system senses that the load peak has passed, or monitors that the fidelity of the quantum entanglement state does not need to be further improved, it can also automatically recycle the wavelength resources or moderately reduce the number of entanglement state initializations to avoid unnecessary power consumption while ensuring performance. The closed-loop control idea of this embodiment enables the quantum-photonic hybrid computing to adapt to various dynamic change environments, ensuring that the random read, sequential read, and write operations are all within the ideal latency range (40 microseconds, 25 microseconds, 400 microseconds), and meeting the expectations at the task level such as the target recognition time (less than 0.5 milliseconds) and the target tracking time (0.5 - 1 second). Embodiment 5

[0046] This embodiment mainly demonstrates the performance evaluation and verification process of the quantum-photonic hybrid interface adaptive computing method in multi-modal and high-concurrency scenarios, corresponding to Figure 5 the experimental data stream input and interface load control module (S501), the interface parameter adaptive optimization execution monitoring unit (S502), the random read latency optimization performance evaluation module (S503), the sequential read latency optimization performance evaluation module (S504), the write latency optimization performance evaluation module (S505), and the multi-target recognition and tracking performance verification module (S506) and other functional links. By systematically testing and comparing the random read, sequential read, write operations, and multi-target recognition and tracking processes in a real or simulated load environment, the present invention can confirm the improvement amplitude of the quantum-photonic fusion adaptive method in interface performance and target response.

[0047] In the experimental setup phase, the experimental data stream input and interface load control module (S501) generates high-concurrency data streams and multi-modal loads according to a predetermined scenario, which may include complex data requests for 30 to 100 targets, and sets the upper limit of the interface transmission rate at 12 Mbps. At the same time, this module can simulate typical emergency events or peak load situations, such as a sharp increase in the random read request volume within a short period or the large-scale parallel startup of sequential read access, etc., to evaluate the system's response capabilities under extremely high loads. Subsequently, the interface parameter adaptive optimization execution monitoring unit (S502) tracks the execution of the interface adaptive scheme throughout the process, especially continuously records aspects such as identifying delays, tracking response times, and the effects of available wavelength or entangled state resource allocation, forming a complete performance data chain.

[0048] The random read latency optimization performance evaluation module (S503) and the sequential read latency optimization performance evaluation module (S504) respectively analyze the changes in random read and sequential read responses during the above-mentioned adaptive optimization process. If the traditional architecture may allow the latency to exceed 40 microseconds during the random read busy period, or exceed 25 microseconds during the sequential read surge, then the adaptive interface method of quantum-photon fusion will combine the characteristics of quantum parallel search and photon bandwidth switching for resource reconfiguration, and press the random / sequential read latency back within the specified target value. The write latency optimization performance evaluation module (S505) focuses on the performance of the write process in the scenarios of multi-modal merging and high-concurrency write requests. In the present invention, the write latency can be controlled within 400 microseconds, so that the subsequent multi-target recognition time still remains in the order of 0.5 milliseconds, and the tracking time is maintained in the range of 0.5 to 1 second, thereby ensuring the timeliness of the entire feature extraction and information interaction process.

[0049] The multi-target recognition and tracking performance verification module (S506) further examines the comprehensive effects of the quantum-photon fusion adaptive method on target localization, trajectory prediction, and interaction response when 30 to 100 targets appear in parallel. The module records the total time delay from receiving data to completing recognition and tracking output. Typically, it can meet the requirements of multi-target interaction and backhaul within 3 to 5 seconds, showing significant improvements in stability and response speed compared to the traditional single electronic architecture solution. If the test results show that the latency in individual scenarios is still slightly high, or the power consumption surges briefly during the load peak, the system will further correct through interface dynamic scheduling and fine-tuning of quantum state entanglement parameters, forming a continuous iterative optimization closed-loop. In this way, even in the face of sudden tasks or rising sensor noise in real industrial or defense applications, quantum-photon hybrid computing can quickly adapt and maintain the core performance indicators.

[0050] In summary, through the performance evaluation and verification process of this embodiment, it can be clearly demonstrated that the present invention has significant advantages in dealing with massive random / sequential read / write and multi-target identification and tracking scenarios. Compared with the traditional problems of delay out of control and insufficient bandwidth that are prone to occur under high-load environments, the present invention uses a quantum-photon collaborative adaptive strategy to maintain the response speed of random / sequential read and write operations within an ideal range, and greatly enhances the speed and accuracy of the system's concurrent target identification, tracking, and interaction. It has broad application prospects and technological leadership value in the fields of autonomous driving, smart security, large-scale monitoring, and high-performance computing. Technical terms that need to be explained to help understand the present invention

[0051] The main technical terms that need to be explained in order to facilitate understanding of the present invention are listed and explained below. These terms are disclosed in the specification and various embodiments. Their definitions and explanations are only used to assist understanding and should not be regarded as limiting the scope of the invention.

[0052] (1) Quantum-photon hybrid computing module: an integrated computing module that tightly integrates two heterogeneous computing modes, quantum computing and photonic chip computing. It has high parallel processing capabilities and high-speed data transmission characteristics, and is suitable for real-time processing of multimodal data and adaptive optimization of interface performance.

[0053] (2) Quantum bit (Qubit): The basic information unit in quantum computing. Unlike traditional binary bits, it can be in a superposition state of "0" and "1" at the same time, and realizes quantum parallel computing through the entanglement effect.

[0054] (3) Quantum Entanglement: A correlation property unique to quantum mechanics. The states of multiple quantum bits are interdependent and cannot be described independently, which gives quantum systems extremely high parallel computing potential and is widely used in quantum optimization algorithms.

[0055] (4) Photonic Chip: A high-speed data processing and transmission chip based on the principles of optoelectronics. It uses silicon-based materials to implement functions such as waveguides, modulators, photodetectors, and wavelength division multiplexing, and can achieve high-speed, low-latency data transmission and processing.

[0056] (5) Wavelength Division Multiplexing (WDM): A technology widely used in the field of optical communications that transmits data simultaneously through multiple optical signals of different wavelengths to improve data transmission bandwidth and interface efficiency.

[0057] (6) Interface Adaptive Optimization: A method for real-time dynamic adjustment of interface performance parameters. In the present invention, it specifically refers to an optimization strategy for dynamically adjusting parameters such as interface type, transmission rate, random read / write latency, sequential read latency, etc. through quantum-assisted algorithms and deep reinforcement learning algorithms.

[0058] (7) Random Read / Write Latency: The time delay generated when the interface performs random data access operations (read or write), usually used to measure the response speed of the interface to discontinuous data access requests. The target index of the present invention is less than 40 microseconds.

[0059] (8) Sequential Read Latency: The response delay index when the interface reads data in a continuous sequential manner. The target index of the present invention is less than 25 microseconds, used to characterize the sequential data access efficiency of the interface.

[0060] (9) Quantum Approximate Optimization Algorithm (QAOA): A commonly used optimization algorithm in the field of quantum computing, which approximately solves complex combinatorial optimization problems by constructing a variational quantum circuit. The present invention is applied to the adaptive optimization process of interface parameters.

[0061] (10) Deep Reinforcement Learning (DRL): A machine learning method in the field of artificial intelligence, which combines deep learning and reinforcement learning technologies to make intelligent decisions through interaction with the environment to achieve real-time dynamic optimization of interface performance parameters.

[0062] (11) Interface Performance State Space: A multi-dimensional vector space used to characterize the current performance state of the interface, usually including multiple performance indicators such as interface type, transmission rate, delay, etc. The deep reinforcement learning algorithm determines the interface optimization strategy based on this.

[0063] (12) Interface Action Space: In the embodiments of the present invention, the set composed of adjustable actions that can be implemented during the interface performance optimization process, including multiple dimensions such as interface type switching, protocol mode adjustment, bandwidth adjustment range, and read / write latency adjustment.

[0064] (13) Quantum-Assisted Deep Learning (QADL): A new type of computing method that combines quantum computing technology and deep learning networks. In the present invention, it is used for fast prediction and optimization of interface performance to enhance the accuracy and real-time nature of interface dynamic decision-making.

[0065] (14) Information interaction response time: It refers to the total time delay required for the interface and computing module to extract features, identify targets, track, and complete information processing on multimodal data and then output the response result. The index set in the present invention is to complete the response within 3 to 5 seconds.

[0066] (15) Feature extraction time: It is the time delay index required for the interface module or computing core to complete the extraction of target features from the input multimodal data. The target index in the present invention is between 0.5 and 1 second.

[0067] The explanations of the above terms are intended to assist in the in-depth understanding of the present invention and do not constitute a limitation on the protection scope of the present invention.

Claims

1. A quantum photon hybrid interface adaptive computing method, characterized in that: The following steps are involved: (1) collecting the current load status and key performance indicators of the multimodal data interface, wherein the performance indicators include interface type, maximum transmission rate, random read latency, sequential read latency, and write latency; (2) inputting the load status and performance indicators into a quantum photon hybrid computing module, and performing real-time prediction and adaptive configuration of interface types and parameters through quantum parallel search and variational optimization algorithms; (3) Based on the optimization results of quantum computing output, online scheduling and dynamic reconfiguration of parameters such as interface protocol, bandwidth, and read / write methods are performed; (4) The quantum acceleration processing core is used to perform multimodal data feature extraction, target recognition, and information interaction, while maintaining random read latency, sequential read latency, and write time within the target value range, thereby achieving real-time parallel processing of complex multi-target environments.

2. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: The interface load status obtained in step (1) includes the real-time concurrency of multimodal data, sensor sampling frequency, data frame rate and interface historical delay record; the key performance indicators obtained in step (1) include programmable interface type, maximum transmission rate and delay indicator limit within the target range.

3. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: The quantum photon hybrid computing module in step (2) includes a quantum acceleration processing core and a photonic chip network. In the quantum acceleration processing core, the interface configuration is rapidly optimized by using superposition state and entangled state parallel search, and the high-speed reading and configuration distribution of the interface parameters are realized through the photonic chip network.

4. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: When the quantum variational optimization algorithm is used to schedule the interface type and transmission protocol in step (2), according to the target requirements of random read delay ≤ 40 microseconds, sequential read delay ≤ 25 microseconds and write time ≤ 400 microseconds, the quantum variational parameters are corrected online to ensure that the interface still maintains the target delay range under peak load.

5. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: The online scheduling of interface bandwidth and protocol in step (3) includes the following: when a peak in interface load or a change in random / sequential read / write mode is detected, the interface protocol is dynamically switched or bandwidth allocation is adjusted; the protocol switching delay does not exceed 25 microseconds, and the protocol reconfiguration is restored to the maximum transmission rate within 400 microseconds.

6. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: The method uses a quantum acceleration processing core to parallelly process the feature extraction and tracking analysis of 30 to 100 targets in a multi-target recognition task, so that the target recognition response time is kept within 0.5 milliseconds and the target tracking time is kept within 0.5 to 1 second.

7. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: The photonic chip network enables high-speed distribution of multimodal data in random read and sequential read modes, ensuring low-latency access at a 12 Mbps transmission rate and avoiding the communication bottleneck of traditional electronic buses when large-scale data is concurrent.

8. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: Also includes: When the number of targets continues to increase or the sensor frequency rises, the photon wavelength resources and quantum state entanglement parameters are dynamically adjusted by real-time monitoring of the interface bandwidth utilization and quantum register load status to ensure fast access and low-latency processing of high-priority data streams.

9. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: While meeting the interface read and write delay targets, the information interaction response time is controlled within 3 to 5 seconds, which is used for real-time output of multimodal target recognition or tracking results and subsequent decision execution.

10. The quantum photon hybrid interface adaptive calculation method according to claim 1, characterized in that: Through quantum state-assisted interface performance prediction and online feedback control, combined with deep reinforcement learning decision-making or quantum variational optimization strategy, periodic allocation of interface type, protocol mode, read-write parameters and wavelength resources can be achieved to ensure that random read-write, sequential read-write and write delays are maintained at the specified target values, while supporting high-load scenarios of multi-target concurrent identification and dynamic interaction.

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