自旋網絡量子儲備池計算中與頻率和耗散相關的糾纏優勢研究
在具有耗散的自旋網絡量子儲備池計算中，量子糾纏的優勢取決於輸入信號的頻率；當輸入信號的頻率高於系統耗散時間尺度時，量子糾纏對系統性能有積極影響。
스핀 네트워크 양자 저장 풀 컴퓨팅에서 주파수 및 소산 의존적 얽힘 이점
소산이 있는 스핀 네트워크 양자 저장 풀 컴퓨팅 시스템에서 얽힘 이점은 입력 신호 주파수에 따라 달라지며, 양자 메모리가 유지되는 시간 내에 입력 신호의 시간적 특징이 나타날 때 얽힘 이점이 발생합니다.
The Impact of Frequency and Dissipation on Entanglement Advantage in Spin-Network Quantum Reservoir Computing
In spin-network quantum reservoir computing, the presence of an entanglement advantage for memory tasks is highly dependent on the input signal frequency and the system's dissipation level, suggesting that quantum memory persistence relative to input signal complexity is crucial for leveraging quantum advantages.
Harnessing Random Matrices for Efficient Quantum Reservoir Computing
A novel approach to reservoir computing using random matrices to generate diverse state descriptions for simple quantum systems, enabling effective time-series prediction and data interpolation.
Reservoir Computing Using Measurement-Controlled Quantum Dynamics: A Quantum Approach to Machine Learning
Quantum Reservoir Computing ermöglicht präzise Vorhersagen mit geringem Ressourcenbedarf.
Reservoir Computing Using Measurement-Controlled Quantum Dynamics: A Comprehensive Analysis
The authors propose a novel quantum reservoir computing system based on the dynamics of a probed atom in a cavity, showcasing its superior performance with minimal artificial neurons compared to traditional systems.
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