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Science China(Physics,Mechanics & Astronomy) 2023年第11期66卷 17-43页

作者： Wenlong Cai Yan Huang Xueying Zhang Shihong Wang Yuanhao Pan Jialiang Yin Kewen Shi Weisheng Zhao Fert Beijing Institute MIIT Key Laboratory of SpintronicsSchool of Integrated Circuit Science and EngineeringBeihang UniversityBeijing100191China Beihang-Goertek Joint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdao266000China

Intelligent computing paradigms have become increasingly important for the efficient processing of massive amounts of data.However,using traditional electronic devices to implement these intelligent paradigms is currently mismatched and limited by their energy,area,and speed.Spintronics,which exploits the magnetic and electrical properties of electrons,could break through these limitations and bring new possibilities to electrical devices.In particular,the tunneling magnetoresistance effect,merging quantum and spintronics,enables spintronic devices to be compatible with standard integrated circuits with a magnetic tunnel junction(MTJ)design,showing great potential for implementing hardware-based intelligent frameworks.In this review,we introduce the specific capabilities of MTJs,including nonvolatility,stochasticity,plasticity,and nonlinearity,which are highly favorable in artificial intelligence algorithms.We then present how these devices could impact the development of intelligent computing,including in-memory computing,probabilistic computing,and neuromorphic computing.Finally,we discuss their challenges and perspectives in intelligent hardware implementations.

关键词： tunneling magnetoresistance effect magnetic tunnel junction in-memory computing probabilistic computing neuromorphic computing

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Comparison among Classical,probabilistic and Quantum Algorithms for Hamiltonian Cycle Problem

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Journal of Quantum computing 2023年第1期5卷 55-70页

作者： Giuseppe Corrente Carlo Vincenzo Stanzione Vittoria Stanzione Computer Science Department Universitàdi TorinoTorinoItaly Computer Science Department ITIS Avogadro SeraleTorinoItaly Computer Engineering Department(DII) UniversitàDegli Studi di PisaPisa56122Italy Justonearth SRL Roma00168Italy Physics Deparment E.Fermi UniversitàDegli Studi di PisaPisa56127Italy

The Hamiltonian cycle problem(HCP),which is an NP-complete problem,consists of having a graph G with n nodes and m edges and finding the path that connects each node exactly once.In this paper we compare some algorithms to solve a Hamiltonian cycle problem,using different models of computations and especially the probabilistic and quantum ones.Starting from the classical probabilistic approach of random walks,we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine(QTM),which can be a useful conceptual project tool for quantum algorithms.Introducing several constraints to the graphs,our analysis leads to not-exponential speedup improvements to the best-known algorithms.In particular,the results are based on bounded degree graphs(graphs with nodes having a maximum number of edges)and graphs with the right limited number of nodes and edges to allow them to outperform the other algorithms.

关键词： Quantum computing probabilistic computing hamiltonian cycle problem random walk quantum turing machine

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