Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybri...Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-展开更多
In recent years, rapid developments of quantum computer are witnessed in both the hardware and the algorithm domains, making it necessary to have an updated review of some major techniques and applications in quantum ...In recent years, rapid developments of quantum computer are witnessed in both the hardware and the algorithm domains, making it necessary to have an updated review of some major techniques and applications in quantum algorithm design.In this survey as well as tutorial article, the authors ?rst present an overview of the development of quantum algorithms, then investigate ?ve important techniques: Quantum phase estimation, linear combination of unitaries, quantum linear solver, Grover search, and quantum walk, together with their applications in quantum state preparation, quantum machine learning, and quantum search. In the end, the authors collect some open problems in?uencing the development of future quantum algorithms.展开更多
To search for a target n-product Boolean vector of fixed weight d, we propose an important method involving the notion of a fixed-weight "vector label" accompanied with a vector label restoration algorithm. ...To search for a target n-product Boolean vector of fixed weight d, we propose an important method involving the notion of a fixed-weight "vector label" accompanied with a vector label restoration algorithm. Based on these, we present a new quantum algorithm designed to search for a fixed-weight target whose computation complexity, specifically O ((Cdn+1)^(1/2)) , is better than that for a classical algorithm. Finally, we use the procedure to search for the NTRU private key as an example to verify the efficiency of the new algorithm in searching for fixed-weight target solutions.展开更多
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
Classical machine learning algorithms seem to be totally incapable of processing tremendous data,while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over...Classical machine learning algorithms seem to be totally incapable of processing tremendous data,while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over classical counterparts.In this paper,we propose two quantum support vector machine algorithms for multi classification.One is the quantum version of the directed acyclic graph support vector machine.The other one is to use the Grover search algorithm before measurement,which amplifies the amplitude of the phase storing of the classification result.For k classification,the former provides quadratic reduction in computational complexity when classifying.The latter accelerates the training speed significantly and more importantly,the classification result can be read out with a probability of at least 50%using only one measurement.We conduct numerical simulations on two algorithms,and their classification success rates are 96%and 88.7%,respectively.展开更多
In this work, we demonstrated a fixed-point quantum search algorithm in the nuclear magnetic resonance (NMR) system. We constructed the pulse sequences for the pivotal operations in the quantum search protocol. The ex...In this work, we demonstrated a fixed-point quantum search algorithm in the nuclear magnetic resonance (NMR) system. We constructed the pulse sequences for the pivotal operations in the quantum search protocol. The experimental results agree well with the theoretical predictions. The generalization of the scheme to the arbitrary number of qubits has also been given.展开更多
城市交通拥堵日益严重,高效的路径导航方法一直是当前研究的热点和缓解拥堵的主要途径.现有的研究成果主要集中在对单个车辆行驶时间的路径寻优和小规模路网的多车辆均衡化的路径导航,没有实现大规模多车辆多路径的实时动态路径导航.当...城市交通拥堵日益严重,高效的路径导航方法一直是当前研究的热点和缓解拥堵的主要途径.现有的研究成果主要集中在对单个车辆行驶时间的路径寻优和小规模路网的多车辆均衡化的路径导航,没有实现大规模多车辆多路径的实时动态路径导航.当前研究主要存在以下局限:(1)导航方案评价指标单一,不能充分表示导航方案的优劣;(2)无法实现大规模路网的实时导航.针对这些问题,本文提出一种城市交通路网实时动态多路口路径导航量子搜索方法(A Route Guidance Method based on Quantum Searching for Real-time Dynamic Multi-intersections in Urban Traffic Networks,RGQS),该方法充分考虑各种因素,实时提供大规模路网的路径导航.本文的实验分别在人工路网和真实路网中验证了RGQS方法相比于对比算法可以使行驶时间减少达到20%.展开更多
The goal of qubit mapping is to map a logical circuit to a physical device by introducing additional gates as few as possible in an acceptable amount of time.We present an effective approach called Tabu Search Based A...The goal of qubit mapping is to map a logical circuit to a physical device by introducing additional gates as few as possible in an acceptable amount of time.We present an effective approach called Tabu Search Based Adjustment(TSA)algorithm to construct the mappings.It consists of two key steps:one is making use of a combined subgraph isomorphism and completion to initialize some candidate mappings,and the other is dynamically modifying the mappings by TSA.Our experiments show that,compared with state-of-the-art methods,TSA can generate mappings with a smaller number of additional gates and have better scalability for large-scale circuits.展开更多
Our aim is to determine the conditions for quantum computing technology to give rise to the security risks associated with quantum Bitcoin mining.Specifically,we determine the speed and energy efficiency a quantum com...Our aim is to determine the conditions for quantum computing technology to give rise to the security risks associated with quantum Bitcoin mining.Specifically,we determine the speed and energy efficiency a quantum computer needs to offer an advantage over classical mining.We analyze the setting in which the Bitcoin network is entirely classical except for a single quantum miner with a small hash rate compared to the network.We develop a closed-form approximation for the probability that the quantum miner successfully mines a block,with this probability dependent on the number of Grover iterations the quantum miner applies before making a measurement.Next,we show that for a quantum miner that is“peaceful”,this success probability is maximized if the quantum miner applies Grover iterations for 16 min before measuring,which is surprising,as the network mines blocks every 10 min on average.Using this optimal mining procedure,we show that the quantum miner outperforms a classical computer in efficiency(cost per block)if the condition Q<Crb is satisfied,where Q is the cost of a Grover iteration,C is the cost of a classical hash,r is the quantum miner's speed in Grover iterations per second,and b is a factor that attains its maximum if the quantum miner uses our optimal mining procedure.This condition lays the foundation for determining when quantum mining and the known security risks associated with it will arise.展开更多
The Grover quantum search algorithm is a landmark quantum computing application, which has a speed advantage over classical algorithms for searching an unsorted database. For an√ unsorted database of N items, the cla...The Grover quantum search algorithm is a landmark quantum computing application, which has a speed advantage over classical algorithms for searching an unsorted database. For an√ unsorted database of N items, the classical algorithm needs to search O(N) times, while the Grover algorithm only needs O(√N) times. However, except for the special case of N = 4, the traditional Grover algorithm always has some probability of failure. To solve this problem, several schemes for deterministically performing quantum search have been proposed, but they all impose additional requirements on the query Oracle and cannot be implemented in many practical scenarios. Recently, Roy et al. [Phys. Rev. Res. 4, L022013(2022)] proposed a new deterministic quantum search scheme with no additional requirements on the query Oracle, which has the potential to perfectly replace the traditional Grover algorithm. In this study, we experimentally implement on a programmable silicon quantum photonic chip four deterministic quantum search algorithms, including the Roy algorithm, all of which obtained an average search success rate of over0.93, exceeding the theoretical maximum of 0.9074 that the traditional Grover algorithm can achieve. Our results demonstrate the feasibility and superiority of the deterministic quantum search algorithms and are expected to facilitate the wider application of these algorithms in future quantum information processing.展开更多
基金supported by the Natural Science Foundation of Hubei Province(Grant No.2015CFB586)
文摘Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-
基金supported partially by the National Natural Science Foundation of China under Grant No.11671388CAS Project QYZDJ-SSW-SYS022GF S&T Innovation Special Zone Project
文摘In recent years, rapid developments of quantum computer are witnessed in both the hardware and the algorithm domains, making it necessary to have an updated review of some major techniques and applications in quantum algorithm design.In this survey as well as tutorial article, the authors ?rst present an overview of the development of quantum algorithms, then investigate ?ve important techniques: Quantum phase estimation, linear combination of unitaries, quantum linear solver, Grover search, and quantum walk, together with their applications in quantum state preparation, quantum machine learning, and quantum search. In the end, the authors collect some open problems in?uencing the development of future quantum algorithms.
文摘To search for a target n-product Boolean vector of fixed weight d, we propose an important method involving the notion of a fixed-weight "vector label" accompanied with a vector label restoration algorithm. Based on these, we present a new quantum algorithm designed to search for a fixed-weight target whose computation complexity, specifically O ((Cdn+1)^(1/2)) , is better than that for a classical algorithm. Finally, we use the procedure to search for the NTRU private key as an example to verify the efficiency of the new algorithm in searching for fixed-weight target solutions.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
基金supported by the Shandong Provincial Natural Science Foundation for Quantum Science(No.ZR2021LLZ002)the Fundamental Research Funds for the Central Universities(No.22CX03005A).
文摘Classical machine learning algorithms seem to be totally incapable of processing tremendous data,while quantum machine learning algorithms could deal with big data unhurriedly and provide exponential acceleration over classical counterparts.In this paper,we propose two quantum support vector machine algorithms for multi classification.One is the quantum version of the directed acyclic graph support vector machine.The other one is to use the Grover search algorithm before measurement,which amplifies the amplitude of the phase storing of the classification result.For k classification,the former provides quadratic reduction in computational complexity when classifying.The latter accelerates the training speed significantly and more importantly,the classification result can be read out with a probability of at least 50%using only one measurement.We conduct numerical simulations on two algorithms,and their classification success rates are 96%and 88.7%,respectively.
基金supported by the SRFPD Program of Education Ministry ofChina (Grant No. 20090002110064)the National Natural Science Foundation of China (Grant No. 10874098)the National Basic Research Program of China (Grant Nos. 2009CB929402 and 2011CB921602)
文摘In this work, we demonstrated a fixed-point quantum search algorithm in the nuclear magnetic resonance (NMR) system. We constructed the pulse sequences for the pivotal operations in the quantum search protocol. The experimental results agree well with the theoretical predictions. The generalization of the scheme to the arbitrary number of qubits has also been given.
文摘城市交通拥堵日益严重,高效的路径导航方法一直是当前研究的热点和缓解拥堵的主要途径.现有的研究成果主要集中在对单个车辆行驶时间的路径寻优和小规模路网的多车辆均衡化的路径导航,没有实现大规模多车辆多路径的实时动态路径导航.当前研究主要存在以下局限:(1)导航方案评价指标单一,不能充分表示导航方案的优劣;(2)无法实现大规模路网的实时导航.针对这些问题,本文提出一种城市交通路网实时动态多路口路径导航量子搜索方法(A Route Guidance Method based on Quantum Searching for Real-time Dynamic Multi-intersections in Urban Traffic Networks,RGQS),该方法充分考虑各种因素,实时提供大规模路网的路径导航.本文的实验分别在人工路网和真实路网中验证了RGQS方法相比于对比算法可以使行驶时间减少达到20%.
基金supported by the National Natural Science Foundation of China under Grant Nos.61832015,62072176,12271172 and 11871221the Research Funds of Happiness Flower of East China Normal University under Grant No.2020ECNU-XFZH005+1 种基金the Fundamental Research Funds for the Central Universities of China under Grant No.2021JQRH014Shanghai Trusted Industry Internet Software Collaborative Innovation Center,and the “Digital Silk Road”Shanghai International Joint Lab of Trustworthy Intelligent Software under Grant No.22510750100.
文摘The goal of qubit mapping is to map a logical circuit to a physical device by introducing additional gates as few as possible in an acceptable amount of time.We present an effective approach called Tabu Search Based Adjustment(TSA)algorithm to construct the mappings.It consists of two key steps:one is making use of a combined subgraph isomorphism and completion to initialize some candidate mappings,and the other is dynamically modifying the mappings by TSA.Our experiments show that,compared with state-of-the-art methods,TSA can generate mappings with a smaller number of additional gates and have better scalability for large-scale circuits.
文摘Our aim is to determine the conditions for quantum computing technology to give rise to the security risks associated with quantum Bitcoin mining.Specifically,we determine the speed and energy efficiency a quantum computer needs to offer an advantage over classical mining.We analyze the setting in which the Bitcoin network is entirely classical except for a single quantum miner with a small hash rate compared to the network.We develop a closed-form approximation for the probability that the quantum miner successfully mines a block,with this probability dependent on the number of Grover iterations the quantum miner applies before making a measurement.Next,we show that for a quantum miner that is“peaceful”,this success probability is maximized if the quantum miner applies Grover iterations for 16 min before measuring,which is surprising,as the network mines blocks every 10 min on average.Using this optimal mining procedure,we show that the quantum miner outperforms a classical computer in efficiency(cost per block)if the condition Q<Crb is satisfied,where Q is the cost of a Grover iteration,C is the cost of a classical hash,r is the quantum miner's speed in Grover iterations per second,and b is a factor that attains its maximum if the quantum miner uses our optimal mining procedure.This condition lays the foundation for determining when quantum mining and the known security risks associated with it will arise.
基金supported by the National Key Research and Development Program(Grant No.2017YFA0305200)the Key Research and Development Program of Guangdong Province of China(Grant Nos.2018B030329001,and 2018B030325001)+1 种基金the National Natural Science Foundation of China(Grant No.61974168)support from the National Young 1000 Talents Plan。
文摘The Grover quantum search algorithm is a landmark quantum computing application, which has a speed advantage over classical algorithms for searching an unsorted database. For an√ unsorted database of N items, the classical algorithm needs to search O(N) times, while the Grover algorithm only needs O(√N) times. However, except for the special case of N = 4, the traditional Grover algorithm always has some probability of failure. To solve this problem, several schemes for deterministically performing quantum search have been proposed, but they all impose additional requirements on the query Oracle and cannot be implemented in many practical scenarios. Recently, Roy et al. [Phys. Rev. Res. 4, L022013(2022)] proposed a new deterministic quantum search scheme with no additional requirements on the query Oracle, which has the potential to perfectly replace the traditional Grover algorithm. In this study, we experimentally implement on a programmable silicon quantum photonic chip four deterministic quantum search algorithms, including the Roy algorithm, all of which obtained an average search success rate of over0.93, exceeding the theoretical maximum of 0.9074 that the traditional Grover algorithm can achieve. Our results demonstrate the feasibility and superiority of the deterministic quantum search algorithms and are expected to facilitate the wider application of these algorithms in future quantum information processing.
