Resumen de: CN123014250A
本发明公开了一种量子态测量方法、装置、设备、存储介质和程序产品,获取一组完备的厄米算子基序列;构建机器学习模型和量子神经网络模型;其中,所述机器学习模型用于输出所述厄米算子基的概率分布;所述量子神经网络模型用于输出量子态;采用经典数据集对所述机器学习模型和所述量子神经网络模型进行训练;根据所述机器学习模型输出的概率分布对所述厄米算子基序列进行采样,得到目标数量的目标厄米算子基;将测试数据输入所述量子神经网络模型生成量子态,并采用所述目标厄米算子基进行测量,得到目标测量物理量的期望值。采用本发明,能够实现对特定学习任务的优化,并确保提取到有效的信息,从而得到最优的测量物理量。
Resumen de: CN123014044A
本申请实施例提供了一种基于量子忆阻系统的混沌信号预测方法和装置,所述方法包括:所述量子忆阻系统包含输入端、多个光量子忆阻器和探测端,所述方法包括:通过所述输入端生成单光子源信号,并将所述单光子源信号编码至第一量子态;通过所述光量子忆阻器对所述第一量子态进行转化得到第二量子态;通过所述探测端根据所述第二量子态进行探测得到密度矩阵信息;通过预设的信号预测模型对所述密度矩阵信息进行分析,得到混浊信号预测结果。从而通过融合量子机器学习与光量子忆阻器技术,利用量子叠加态和忆阻器的非线性动态特性实现信息处理,提高混沌信号预测任务的计算精度与效率,突破高维数据处理中的性能限制,为人工智能硬件实现提供新路径。
Resumen de: CN123017139A
本发明公开了一种分子对接方式的确定方法及相关装置,方法包括:分别提取受体蛋白的三维结构和药物小分子的三维结构特征,作为受体蛋白的第一特征和药物小分子的第一特征;对受体蛋白的第一特征和药物小分子的第一特征,分别进行量子傅里叶变换,获得受体蛋白的第二特征和药物小分子的第二特征;对受体蛋白的第二特征和药物小分子的第二特征,进行特征交互操作,得到交互后的特征;将交互后的特征进行量子傅里叶逆变换操作,获得量子傅里叶逆变换操作的结果,作为受体蛋白和药物小分子的匹配位置的打分;通过匹配位置的打分的最小值,确定最佳结合位置;本申请通过引入量子傅里叶变换,旨在加速分子对接中的特征提取,加快分子对接方式的确定。
Resumen de: CN123013683A
本发明公开了一种基于涡旋配对‑拓扑荷归零的二维自旋场仿真建模方法及系统,属于量子仿真与计算物理技术领域。本发明将二维自旋系统的自旋构型映射为素数谱流形上的涡旋分布,以总拓扑荷归零条件作为仿真过程的全局物理约束,引导系统在约束优化中快速收敛到满足有限能量边界条件的基态构型。本发明可避免局部极小值陷阱,加速收敛 2‑5 倍,适用于海森堡模型、Skyrmion 模拟、量子磁性材料仿真等场景,可与拓扑量子计算硬件协同工作。
Resumen de: CN123019890A
本发明公开了一种拓扑保护的量子隐形传态全链路工程方法及其系统。所述方法包括:以复投影权重对数为物理正则化约束项筛选拓扑材料;根据拓扑相图公式确定器件最优结构参数并制造拓扑保护器件;根据稳定性判据实时监测并维持系统稳定性;在拓扑保护条件下执行量子隐形传态;以拓扑保护带宽为统一标定基准进行闭环反馈控制。本发明首次将拓扑材料筛选、拓扑保护器件制造、拓扑稳定性控制和量子隐形传态协议执行整合为统一的技术链条,实现了从理论设计到工程实施的全链路闭环控制。
Resumen de: CN123020212A
本发明涉及信息安全技术领域,尤其涉及一种基于量子遗传算法特征选择的流量异常检测方法及系统,方法包括:基于特征重要性度量方法计算各特征的初始重要性分数;构建量子种群并通过观测操作将量子染色体坍塌为二进制特征掩码向量;对量子种群执行分层适应度评估,基于互信息对全部个体计算预评估价值,选取预评估价值排名靠前的个体作为精英候选并训练分类器计算精确适应度值;当种群多样性低于预设阈值时从历史精英片段记忆库中采样精英片段并对非精英个体实施定向赋值,否则执行量子旋转门更新,迭代至收敛后输出最优特征子集;基于最优特征子集训练异常检测分类器,对实时网络流量提取对应特征并输入异常检测分类器进行异常判定。
Resumen de: CN123014048A
本发明公开了一种拓扑光量子Hopfield网络及自适应调控方法,属于光量子计算与拓扑光子学交叉技术领域。系统部署于集成光量子芯片,核心包括可编程多光子干涉网络、可重构拓扑保护波导阵列与协同控制器。本发明构建记忆编码相位矩阵‑泵浦调控参数矩阵‑拓扑波导配置矩阵的三元协同调控架构,通过临界态识别函数主动判定预热化临界态,辅以微弱相干脉冲触发光子相变,实现拓扑保护下的可编程联想记忆计算。本发明单次联想计算能耗低至0.85fJ/bit,实验条件下临界态触发成功率可达100%,环境鲁棒性较传统开环方案提升两个数量级,可广泛应用于光量子AI加速器、超低功耗边缘计算、存算一体芯片等场景。
Resumen de: CN123010492A
本发明公开了一种冷原子量子门多束窄线宽激光同步相位锁定电控系统,涉及激光器技术领域。通过全局同步主控模块产生同源时钟并建立统一时序基准,结合多路同步调制信号输出模块、跨激光器相位差实时提取模块、全局统一反馈校准模块以及多路激光驱动锁频模块,构建基于统一时钟源的全数字多通道闭环相位同步控制架构。该系统通过同步采集各路待锁激光与参考激光形成的拍频检测信号,实时解算相对相位偏移量,并根据相位误差生成补偿控制量,对各路窄线宽激光进行动态相位校准。本发明实现了多路激光时序同步、相位检测及闭环补偿一体化控制,降低了动态相位漂移,提高了多束窄线宽激光的相对相位稳定性,满足冷原子量子门高精度操控需求。
Resumen de: CN123010145A
本发明公开一种基于双量子线路近远程输送交互的大气臭氧浓度预报方法。本发明首先利用起报前污染物观测与数值天气预报构建动态有向输送图;继而将源城市‑输送起始时刻‑特征张量划分为近场和远程两类,经降维和振幅编码分别输入两条参数化量子线路;根据量子测量结果恢复近场、远程输送消息,并与目标城市本地化学‑气象状态融合,以输送经典臭氧预报模型的近、远场输送消息和臭氧观测数据联合蒸馏训练两条量子线路,预报未来臭氧浓度。本发明能够在保持与输送经典臭氧预报模型精度相当的同时,以紧凑量子表示和处理不同空间尺度的高维输送交互关系,并为适用量子硬件上的计算加速提供实现路径。
Resumen de: CN123014246A
本申请公开了超导量子比特、耦合调控方法及超导量子芯片,该超导量子比特的第一电极区和第二电极区分别绕同一中心沿螺旋轨迹同向延伸以形成耦合圈层,一方面,可有效约束非预期方向的电磁场杂散分量,降低了邻近电路元件间的非期望串扰;另一方面,电极区的螺旋环绕可以增加两个电极区之间的有效耦合路径,提高耦合电容在等效总电容中的占比,减小电极区所占面积,实现超导量子比特的小型化。同时,耦合圈层的边界为圆滑的弧形设计,替代了相关技术中的插指或尖锐边缘结构,使得电场能量分布更加均匀,减少了电荷在局部尖端或拐角处积聚的可能性,使得超导量子比特在保持目标非谐性的同时,降低电极区对封装和相邻器件的寄生耦合。
Resumen de: CN123010971A
本发明公开了一种基于投影角并行控制的拓扑光计算芯片及其控制方法。所述芯片包括拓扑光子波导阵列、相位并行调控模块、光输入耦合阵列、光输出干涉阵列、光探测阵列和权重控制模块。所述方法通过权重控制模块获取目标权重矩阵,计算各拓扑光子单元所需的投影角序列θ_i,在同一时钟边沿并行输出至所有权重单元,一次性加载完整权重矩阵;输入光信号同时进入所有计算路径,经相位调制后干涉叠加,完成矩阵‑向量乘法。本发明实现了光计算权重的纳秒级并行配置,权重加载时间小于1纳秒,相位分辨率优于0.001弧度,可广泛应用于矩阵乘法加速、神经网络推理、光学信号处理和量子‑经典混合计算领域。
Resumen de: CN123012688A
本发明公开了一种基于变分测量基的量子卷积神经网络分类模型构建方法,通过引入测量基参数生成网络,将测量基优化过程从传统量子卷积神经网络的固定读出方式中分离出来,并与量子卷积特征提取过程进行协同设计,使测量基能够随分类任务的训练过程自适应优化。与此同时,本发明采用监督学习与强化学习交替进行的混合学习机制,在监督学习阶段优化量子卷积神经网络参数,在强化学习阶段优化测量基参数生成网络参数,从而实现量子特征提取过程与量子测量过程的协同优化。本发明能够提高分类任务中的特征表达能力、分类准确性以及对噪声环境的适应能力,从而为量子机器学习在多类分类场景中的应用提供一种有效的技术方案。
Resumen de: CN123010105A
本发明涉及量子精密测量领域,提供一种基于神经网络的原子重力仪干涉条纹优化方法与装置,包括:获取多个振动时序信号,采用多个振动时序信号对神经网络进行训练,得到预优化神经网络;将振动时序信号输入至预优化神经网络,得到预测寄生相位;获取与各个振动时序信号时序对齐的原始干涉信号,根据预测寄生相位对相应的原始干涉信号进行补偿,得到补偿干涉信号;对补偿干涉信号进行拟合得到补偿干涉条纹,获取补偿干涉条纹所对应的拟合波形曲线与理想干涉条纹所对应的标准余弦曲线之间的波形差异,以最小化波形差异为目标对预优化神经网络进行优化,得到优化神经网络,优化神经网络能够对更多的噪声源进行补偿,以达到优化干涉条纹的目的。
Resumen de: CN123014242A
本发明公开了一种基于全域能量工程化模型的RGB三角动力学全光谱量子芯片系统,属于光量子芯片与拓扑量子信息领域。本发明采用薄膜铌酸锂基底制备正三角拓扑晶格,顶点异质集成近红外、可见光、紫外三波段三能级量子单元,分别映射信息域、能量域、结构域,支持平衡三进制编码;通过侧边叉指换能器激发声表面波,实现大失谐下声介导三体远程有效耦合;内置芯片级全域能量工程化模型与韬时间多尺度补偿算法,结合非厄米架构主动抵消退相干。本发明GHZ纠缠制备速率较一维二体级联方案提升8倍以上,常温补偿后相干时间可达40μs,可应用于量子保密通信、精密惯性传感等场景。
Resumen de: CN123019955A
本发明涉及量子计算异构通信技术领域,特别涉及一种面向异构通信的量子测控波形数据压缩方法及系统,通过对原始波形数据进行量化处理,计算量化后波形的哈希键,利用哈希键在波形字典中进行去重存储,所述波形字典为预先构建的量子门脉冲波形样本字典;利用播放指令序列存储各原始波形的波形标识及全局时间轴上波形绝对起始时间和对应目标通道标识;将波形字典、播放指令序列及原始波形元数据序列化为字节流,并通过压缩封装将数据包传输至量子测控设备,使量子测控设备通过对接收的数据包进行反序列化来恢复并获取波形字典和播放指令序列。本发明能够实现波形数据在异构融合架构中的低延迟传输,为大规模量子计算系统可扩展性提供关键技术支撑。
Resumen de: CN123012692A
本申请实施例提供一种基于两级架构的吊艇架液压系统故障诊断方法及系统,涉及吊艇架故障诊断技术领域,其方法包括:获取吊艇架液压系统的当前运行状态数据,并通过对所述当前运行状态数据进行预处理,得到预处理后的当前运行状态数据;通过将所述预处理后的当前运行状态数据输入至训练好的改进LightGBM模型中,得到吊艇架液压系统的特征数据;通过将所述吊艇架液压系统的特征数据输入至训练好的改进Transformer模型中,得到吊艇架液压系统的故障诊断结果。
Resumen de: WO2025179382A1
The present disclosure relates to a method, quantum circuit, and computer-readable memory for hyperpolarization. Illustratively, the method for the system, which system includes a target system, an auxiliary system, and a reset system each with their own energy levels, includes applying one or more operations to increase the probability of the target system energy levels in lower energy state, and to reduce the probability of the target system energy levels that have the target element in the higher energy state.
Resumen de: WO2025151958A1
There is described herein a superconducting quantum processor package comprising: a package substrate having an external connector port; an interposer having first and second opposing sur¬ faces, the first surface of the interposer facing the package substrate, the interposer carrying a first quantum readout device coupled to the external connector port; a first qubit substrate having top and bottom opposing surfaces, the bottom surface of the first qubit substrate facing the second surface of the interposer, the first qubit substrate carrying a first qubit circuit with interleaved qubits and couplers, the first qubit circuit coupled to the first quantum readout device, wherein the first qubit circuit, the first quantum readout device, and the external connector port form a first readout communication path; and a first amplifying device in the first readout communication path, coupled between the first quantum readout device and the external connector port, to amplify readout sig¬ nals from the first quantum readout device.
Resumen de: CN122348818A
A coprocessor, a chip, an electronic device and a key generation method for implementing a lattice-based anti-quantum cryptographic algorithm, the coprocessor comprising: a processor core comprising at least two operation cores capable of implementing all operations of the lattice-based anti-quantum cryptographic algorithm; the register is used for storing configuration information required for calling functions in the operation core; and the memory controller is used for calling data in the memory according to the configuration information under the control of the at least two operation cores so as to realize parallel operation between the at least two operation cores. According to the coprocessor, full-hardware design of operation functions in the lattice-based anti-quantum cryptographic algorithm is achieved, the operation speed of the overall process of the algorithm can be greatly increased, in addition, part of cryptographic functions can be processed in parallel, the utilization rate of system and hardware resources is greatly increased while the operation performance is further improved, and the cost is reduced. And the method can be applied to middle and high-end products with higher algorithm operation performance.
Resumen de: WO2025210763A1
This arithmetic device is configured so as to comprise a contraction unit (1) that expresses a tensor of rank L (where L is an integer equal to or greater than 2) as the product of a plurality of minor tensors, each of which has a smaller number of elements than the number of elements in the tensor of rank L, and performs a contraction process on the plurality of minor tensors to acquire factor information present among the plurality of minor tensors.
Resumen de: US20260340464A1
A processing unit in an information processing apparatus acquires a Hamiltonian operator. The processing unit determines, for each of a plurality of time evolution operators, the number of sampling runs to be performed in a quantum computing device, based on weights of the plurality of time evolution operators, the Hamiltonian operator being approximated as a linear combination of the plurality of time evolution operators using a finite difference formula.
Resumen de: AU2025256006A1
Method of automated spatial patterning of defect centers (6) in a substrate, particularly a diamond crystal lattice (50), comprising the following steps: - providing a defect center (6) distribution to a machine-learning model, the machine-learning model being particularly trained to determine an output for displacement of at least one defect center (6) based on the provided defect center (6) distribution, the machine learning model providing an output for displacement of individual defect centers (6), - particularly providing a substrate, particularly diamond, comprising defect centers (6) in a bulk structure of the substrate, particularly a bulk diamond crystal lattice (50), - detecting the position of at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), and - displacing (61) the at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), particularly site-specific, in a defined direction.
Resumen de: WO2026224573A1
Methods for scheduling attempts to entangle quantum systems assign entanglement attempts for pairs of the quantum systems to corresponding time slots of a frame of a series of frames. Each frame includes plural time slots each long enough to execute an entanglement protocol on a pair of the quantum systems. The methods yield data indicating the assignments of pairs of the quantum systems to corresponding time slots. A frame duration is long enough that a period between the end of a time slot in one frame and the beginning of the same time slot in the next frame is sufficient to determine that an entanglement attempt for a pair of the quantum systems in the time slot and to disengage the successfully-entangled pair of quantum systems from further entanglement attempts. Control systems and apparatus for entangling quantum systems are also described.
Resumen de: US20260340474A1
An error mitigation method for a quantum computing device can include receiving a quantum circuit configuration and obtaining a set of hardware-implementable quantum gates. The method can determine an inverse noise representation based on a noise representation of a physical implementation of a target gate. A generalized quasi-probabilistic decomposition of the inverse noise representation can be generated, including a positive component and a negative component. An updated quantum circuit configuration can be generated by inserting at least one implementable quantum gate subsequent to the target gate. The inserted gate is selected to correspond to the positive component of the decomposition, while the negative component can be explicitly excluded. The updated quantum circuit configuration is transmitted to the quantum computing device for physical execution, and an error-mitigated expectation value is received based on the execution.
Nº publicación: US20260340462A1 29/10/2026
Solicitante:
BANK OF AMERICA CORP [US]
Bank of America Corporation
Resumen de: US20260340462A1
Quantum entangled domain map analysis and quantum gradient descent network landscape analysis are designed to enhance inter-domain connectivity and decision-making efficiency across distributed computer networks. Quantum entangled domain map analysis uses quantum entanglement principles to create a comprehensive mapping of network domain features to identify connectivity gaps between domains and to adaptively provide cross-domain optimizations to the network. Quantum gradient descent network landscape analysis utilizes quantum gradient descent methods to intelligently navigate the optimization landscape of inter-domain relationships in the distributed network. By minimizing cost functions associated with network connectivity, quantum gradient descent network landscape analysis reduces computation time significantly while enhancing the accuracy of determining the most effective pathways for data flow.