Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v...Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.展开更多
Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prereq...Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.展开更多
Reservoir engineering has been widely used in various quantum technologies.Based on a cavity-QED(quantum electrodynamics)model,we propose a potentially practical scheme using squeezed-vacuum reservoir engineering to o...Reservoir engineering has been widely used in various quantum technologies.Based on a cavity-QED(quantum electrodynamics)model,we propose a potentially practical scheme using squeezed-vacuum reservoir engineering to optimize the performance of a quantum battery(QB)located inside a cavity driven by a broadband squeezed laser,which acts as a squeezed-vacuum reservoir.Using the reduced master equation of the QB obtained via the adiabatic elimination method,we focus on the QB's charging dynamics under tunable squeezed reservoirs governed by parametrically controlled squeezing parameters,which dictate the efficiency of energy transfer and the extractable work(ergotropy)of the QB.We show that increasing the squeezing strength improves the charging rate and enables rapid energy transfer,whereas the steady-state energy of the QB saturates at specific values of the squeezing parameter.Notably,the ergotropy of the QB reaches its maximum at a critical squeezing strength and does not scale monotonically with the squeezing strength.This nonmonotonic behavior underscores the existence of optimal parameter regimes,through which the performance of the QB can be significantly enhanced.展开更多
In the 9 December 2024 issue of Nature[1],a team of Google engineers reported breakthrough results using“Willow”,their lat-est quantum computing chip(Fig.1).By meeting a milestone“below threshold”reduction in the ...In the 9 December 2024 issue of Nature[1],a team of Google engineers reported breakthrough results using“Willow”,their lat-est quantum computing chip(Fig.1).By meeting a milestone“below threshold”reduction in the rate of errors that plague super-conducting circuit-based quantum computing systems(Fig.2),the work moves the field another step towards its promised super-charged applications,albeit likely still many years away.Areas expected to benefit from quantum computing include,among others,drug discovery,materials science,finance,cybersecurity,and machine learning.展开更多
As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large lang...As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large language models(LLMs)for Salmonella resistance prediction,data presentation,and data sharing.To overcome this issue,we firstly propose a two-step feature-selection process based on the chi-square test and conditional mutual information maximization to find the key Salmonella resistance genes in a pan-genomics analysis and develop an LLM-based Salmonella antimicrobial-resistance predictive(SARPLLM)algorithm to achieve accurate antimicrobial-resistance prediction,based on Qwen2 LLM and low-rank adaptation.Secondly,we optimize the time complexity to compute the sample distance from the linear to logarithmic level by constructing a quantum data augmentation algorithm denoted as QSMOTEN.Thirdly,we build up a user-friendly Salmonella antimicrobial-resistance predictive online platform based on knowledge graphs,which not only facilitates online resistance prediction for users but also visualizes the pan-genomics analysis results of the Salmonella datasets.展开更多
Distributed Quantum Computing(DQC)provides a means for scaling available quantum computation by interconnecting multiple quantum processor units(QPUs).A key challenge in this domain is efficiently allocating logical q...Distributed Quantum Computing(DQC)provides a means for scaling available quantum computation by interconnecting multiple quantum processor units(QPUs).A key challenge in this domain is efficiently allocating logical qubits from quantum circuits to the physical qubits within QPUs,a task known to be NP-hard.Traditional approaches,primarily focused on graph partitioning strategies,have sought to reduce the number of required Bell pairs for executing non-local CNOT operations,a form of gate teleportation.However,these methods have limitations in terms of efficiency and scalability.Addressing this,our work jointly considers gate and qubit teleportations introducing a novel meta-heuristic algorithm to minimise the network cost of executing a quantum circuit.By allowing dynamic reallocation of qubits along with gate teleportations during circuit execution,our method significantly enhances the overall efficacy and potential scalability of DQC frameworks.In our numerical analysis,we demonstrate that integrating qubit teleportations into our genetic algorithm for optimizing circuit blocking reduces the required resources,specifically the number of EPR pairs,compared to traditional graph partitioning methods.Our results,derived fromboth benchmark and randomly generated circuits,show that as circuit complexity increases—demanding more qubit teleportations—our approach effectively optimises these teleportations throughout the execution,thereby enhancing performance through strategic circuit partitioning.This is a step forward in the pursuit of a global quantum compiler which will ultimately enable the efficient use of a‘quantum data center’in the future.展开更多
A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuch...A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.展开更多
Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely...Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely on the complexity of cryptographic operations,which are expected to be efficiently solved by quantum computers soon.This review explores how PPC can be built on top of quantum computing itself to alleviate these future threats.We analyze quantum proposals for Secure Multi-party Computation,Oblivious Transfer and Homomorphic Encryption from the last decade focusing on their maturity and the challenges they currently face.Our findings show a strong focus on purely theoretical works,but a rise on the experimental consideration of these techniques in the last 5 years.The applicability of these techniques to actual use cases is an underexplored aspect which could lead to the practical assessment of these techniques.展开更多
Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology,including communication.In this review,we discuss the current state of quantum computing in comm...Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology,including communication.In this review,we discuss the current state of quantum computing in communication and its potential applications in various areas such as network optimization,signal processing,and machine learning for communication.First,the basic principle of quantum computing,quantum physics systems,and quantum algorithms are analyzed.Then,based on the classification of quantum algorithms,several important basic quantum algorithms,quantum optimization algorithms,and quantum machine learning algorithms are discussed in detail.Finally,the basic ideas and feasibility of introducing quantum algorithms into communications are emphatically analyzed,which provides a reference to address computational bottlenecks in communication networks.展开更多
With a paper published in the 19 February 2025 issue of Nature[1],Microsoft(Redmond,WA,USA)fanned the flames of its unique vision for quantum computing:a stable,error-resistant qubit based on the Majorana fermion,one ...With a paper published in the 19 February 2025 issue of Nature[1],Microsoft(Redmond,WA,USA)fanned the flames of its unique vision for quantum computing:a stable,error-resistant qubit based on the Majorana fermion,one of the strangest and most elusive particles in physics.The Microsoft Azure Quantum research team’s descriptions of a means to detect the as-yet theoretical particles[1]—called“an entirely new state of matter”by Microsoft’s chief executive officer[2]—and a design for a chip powered by them(Fig.1)[3]have refocused attention on the company’s ambition to build a topological quantum computer.The approach—if it works—could potentially leapfrog every other in the field.展开更多
Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resourc...Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.展开更多
This paper investigatesWindfarmLayoutOptimization(WFLO),where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization(QUBO)problem.Wind energy plays a critical role in ...This paper investigatesWindfarmLayoutOptimization(WFLO),where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization(QUBO)problem.Wind energy plays a critical role in the transition toward sustainable power systems,but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions.With recent advances in quantum computing,there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks.We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver(VQE)implemented onQiskit’s quantum computer simulator,employing a quantum noise-free,gate-based circuit model.Three classical optimizers are discussed,with a detailed analysis of the two most effective approaches:Constrained Optimization BY Linear Approximation(COBYLA)and Bayesian Optimization(BO).We compare these simulated quantum results with two established classical optimization methods:Simulated Annealing(SA)and the Gurobi solver.The study focuses on 4×4 grid configurations(requiring 16 qubits),providing insights into near-term quantum algorithm applicability for renewable energy optimization.展开更多
This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents ...This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex-inspired model with modern deep learning(a transformer-based reinforcement learning module)and quantum algorithms.In particular,our framework incorporates quantum computational routines(Deutsch-Jozsa,Bernstein-Vazirani,and Grover’s search)to enhance decision-making efficiency.As a novelty of this research,this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.Another main contribution is that the proposed architecture offers some features,such as meta-cognition and situation awareness.The meta-cognition aspect is responsible for hierarchically learning sub-tasks,enabling the agent to achieve the master goal.The situation-awareness property identifies how spatial-temporal reasoning activities related to the world model of the agent can be extracted in a dynamic simulation environment with unstructured uncertainties by quantum computation-based machine learning algorithms with the explainable artificial intelligence paradigm.In this research,the Minecraft game-based simulation environment is utilized for the experimental evaluation of performance and verification tests within complex,multi-objective tasks related to the autonomous behaviors of a smart agent.By implementing several interaction scenarios,the results of the system performance and comparative superiority over alternative solutions are presented,and it is discussed how these autonomous behaviors and cognitive skills of a smart agent can be improved in further studies.Results show that the quantum-enhanced agent achieves faster convergence to an 80%task 2×success rate in exploration tasks and approximately 15%higher cumulative rewards compared to a classical deep RL baseline.These findings demonstrate the potential of quantum algorithms to significantly improve learning and performance in cognitive agent architectures.However,advantages are task-specific and less pronounced under high-uncertainty,reactive scenarios.Limitations of the simulation environment are acknowledged,and a structured future research roadmap is proposed involving highfidelity simulation validation,hardware-in-the-loop robotic testing,and integration of advanced hybrid quantum-classical architectures.展开更多
The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a f...The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a fundamental problem in the field of power systems that aims to satisfy the power balance constraint with minimal cost.In this paper,we focus on the implementation of the UC solution using exact quantum algorithms based on the quantum neural network(QNN).This method is tested with a ten-unit system under the power balance constraint.In order to improve computing precision and reduce network complexity,we propose a knowledge-based partially connected quantum neural network(PCQNN).The results show that exact solutions can be obtained by the improved algorithm and that the depth of the quantum circuit can be reduced simultaneously.展开更多
The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the explo...The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing paradigm.The scope of the related research is notably diverse.This paper consolidates and presents quantum computing research related to the financial sector.The finance applications considered in this study include portfolio optimization,fraud detection,and Monte Carlo methods for derivative pricing and risk calculation.In addition,we provide a comprehensive analysis of quantum computing’s applications and effects on blockchain technologies,particularly in relation to cryptocurrencies,which are central to financial technology research.As discussed in this study,quantum computing applications in finance are based on fundamental quantum physics principles and key quantum algorithms.This review aims to bridge the research gap between quantum computing and finance.We adopt a two-fold methodology,involving an analysis of quantum algorithms,followed by a discussion of their applications in specific financial contexts.Our study is based on an extensive review of online academic databases,search tools,online journal repositories,and whitepapers from 1952 to 2023,including CiteSeerX,DBLP,Research-Gate,Semantic Scholar,and scientific conference publications.We present state-of-theart findings at the intersection of finance and quantum technology and highlight open research questions that will be valuable for industry practitioners and academicians as they shape future research agendas.展开更多
This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation syste...This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation systems.The learning-based parameter transfer scheme to realize adaptive quantum optimization leverages Bayesian optimization to predict initial quantum circuit parameters.When applied to the MPC problems formulated as quadratic unconstrained binary optimization problems,this approach computes optimal controls to minimize the net energy consumption levels in buildings and promotes decarbonization while reducing the computational efforts required for the quantum approximate optimization algorithm as the building energy system trajectory progresses.The energy efficiency and the decarbonization benefits of the proposed quantum optimization-based MPC strategy are demonstrated on buildings at the Cornell University campus.The proposed quantum computing-based technique to address MPC problems in buildings demonstrates energy-efficient and low-carbon building operation with a 6.8% improvement over deterministic MPC and presents opportunities for scaling to larger control problems with a significant reduction in utilized quantum computing resources.A reduction of 41.2% in carbon emissions is also achieved with the proposed control strategy facilitated by efficiently managing battery energy storage and renewable generation sources to promote a push toward carbonneutral building operations.展开更多
In the current noisy intermediate-scale quantum(NISQ)era,a single quantum processing unit(QPU)is insufficient to implement large-scale quantum algorithms;this has driven extensive research into distributed quantum com...In the current noisy intermediate-scale quantum(NISQ)era,a single quantum processing unit(QPU)is insufficient to implement large-scale quantum algorithms;this has driven extensive research into distributed quantum computing(DQC).DQC involves the cooperative operation of multiple QPUs but is concurrently challenged by excessive communication complexity.To address this issue,this paper proposes a quantum circuit partitioning method based on spectral clustering.The approach transforms quantum circuits into weighted graphs and,through computation of the Laplacian matrix and clustering techniques,identifies candidate partition schemes that minimize the total weight of the cut.Additionally,a global gate search tree strategy is introduced to meticulously explore opportunities for merged transfer of global gates,thereby minimizing the transmission cost of distributed quantum circuits and selecting the optimal partition scheme from the candidates.Finally,the proposed method is evaluated through various comparative experiments.The experimental results demonstrate that spectral clustering-based partitioning exhibits robust stability and efficiency in runtime in quantum circuits of different scales.In experiments involving the quantum Fourier transform algorithm and Revlib quantum circuits,the transmission cost achieved by the global gate search tree strategy is significantly optimized.展开更多
In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s a...In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s algorithm hinges on the critical step of determining the period,a process that poses a substantial computational challenge.This article explores innovative quantum optimization solutions that aim to enhance the efficiency of Shor’s period finding algorithm.The article focuses on quantum development environments,such as Qiskit and Cirq.A detailed analysis is conducted on three notable tools:Qiskit Transpiler,BQSKit,and Mitiq.The performance of these tools is evaluated in terms of execution time,precision,resource utilization,the number of quantum gates,circuit synthesis optimization,error mitigation,and qubit fidelity.Through rigorous case studies,we highlight the strengths and limitations of these tools,shedding light on their potential impact on integer factorization and cybersecurity.Our findings underscore the importance of quantum optimization and lay the foundation for future developments in quantum algorithmic enhancements,particularly within the Qiskit and Cirq quantum development environments.展开更多
Preparing quantum superposition states is a crucial step in realizing quantum algorithms,which demands substantial resources.In this paper,we propose a new method for preparing quantum uniform superposition states via...Preparing quantum superposition states is a crucial step in realizing quantum algorithms,which demands substantial resources.In this paper,we propose a new method for preparing quantum uniform superposition states via quantum measurement,and design the bitwise implementation circuit,which only contains Hadamard,CNOT,and π/8 phase gates.Compared to the Shukla–Vedula method,the number of quantum gates required by both methods scales the same,while,the new method offers stronger fault tolerance,and the ancillary qubits employed during the implementation process can be reused,making it more suitable for implementation on real quantum computers.As an application,we provide the circuit for Shor's discrete logarithm quantum algorithm,based on the new method,demonstrating its technical advantage for implementation of quantum algorithms.展开更多
Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains...Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains are becoming increasingly prevalent.Given the restricted control technology,how implementing high-fidelity quantum gate operations is crucial.Many works in current pulse design optimization focus on ion–phonon and effective ion–ion couplings while ignoring the first-order derivative terms expansion impacts of these two terms brought on by experiment defects.This paper proposes a novel robust quantum control optimization method in trapped ions.By introducing the first-order derivative terms caused by the error into the optimization cost function,we generate an extremely robust Mølmer–Sørensen gate with infidelity below 10^(−3) under a drift noise range of±10 kHz,the relative robustness achieves a tolerance of±5%,compared to the 200-kHz frequency spacing between phonon modes,and for time noise drift,the tolerance reached to 2%.Our work reveals the vital role of the first-order derivative terms of coupling in trapped ion pulse control optimization,especially the first-order derivative terms of ion–ion coupling.It provides a robust optimization scheme for realizing more efficient entangled states in trapped ion platforms.展开更多
基金supported by the Major Project for the Integration of ScienceEducation and Industry (Grant No.2025ZDZX02)。
文摘Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.
基金Project supported by the National Natural Science Foundation of China(Grant No.61573266)the Natural Science Basic Research Program of Shaanxi(Grant No.2021JM-133)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University(Grant No.YJSJ25009)。
文摘Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.
基金supported by the National Natural Science Foundation of China(Grants No.12274422)the Natural Science Foundation of Hubei Province(Grant No.2022CFA013)support from A*STAR(Grant Nos.C230917003 and C230917007)。
文摘Reservoir engineering has been widely used in various quantum technologies.Based on a cavity-QED(quantum electrodynamics)model,we propose a potentially practical scheme using squeezed-vacuum reservoir engineering to optimize the performance of a quantum battery(QB)located inside a cavity driven by a broadband squeezed laser,which acts as a squeezed-vacuum reservoir.Using the reduced master equation of the QB obtained via the adiabatic elimination method,we focus on the QB's charging dynamics under tunable squeezed reservoirs governed by parametrically controlled squeezing parameters,which dictate the efficiency of energy transfer and the extractable work(ergotropy)of the QB.We show that increasing the squeezing strength improves the charging rate and enables rapid energy transfer,whereas the steady-state energy of the QB saturates at specific values of the squeezing parameter.Notably,the ergotropy of the QB reaches its maximum at a critical squeezing strength and does not scale monotonically with the squeezing strength.This nonmonotonic behavior underscores the existence of optimal parameter regimes,through which the performance of the QB can be significantly enhanced.
文摘In the 9 December 2024 issue of Nature[1],a team of Google engineers reported breakthrough results using“Willow”,their lat-est quantum computing chip(Fig.1).By meeting a milestone“below threshold”reduction in the rate of errors that plague super-conducting circuit-based quantum computing systems(Fig.2),the work moves the field another step towards its promised super-charged applications,albeit likely still many years away.Areas expected to benefit from quantum computing include,among others,drug discovery,materials science,finance,cybersecurity,and machine learning.
基金supported by the National Science and Technology Major Project(2021YFF1201200)the National Natural Science Foundation of China(62372316)the Sichuan Science and Technology Program key project(2024YFHZ0091).
文摘As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large language models(LLMs)for Salmonella resistance prediction,data presentation,and data sharing.To overcome this issue,we firstly propose a two-step feature-selection process based on the chi-square test and conditional mutual information maximization to find the key Salmonella resistance genes in a pan-genomics analysis and develop an LLM-based Salmonella antimicrobial-resistance predictive(SARPLLM)algorithm to achieve accurate antimicrobial-resistance prediction,based on Qwen2 LLM and low-rank adaptation.Secondly,we optimize the time complexity to compute the sample distance from the linear to logarithmic level by constructing a quantum data augmentation algorithm denoted as QSMOTEN.Thirdly,we build up a user-friendly Salmonella antimicrobial-resistance predictive online platform based on knowledge graphs,which not only facilitates online resistance prediction for users but also visualizes the pan-genomics analysis results of the Salmonella datasets.
文摘Distributed Quantum Computing(DQC)provides a means for scaling available quantum computation by interconnecting multiple quantum processor units(QPUs).A key challenge in this domain is efficiently allocating logical qubits from quantum circuits to the physical qubits within QPUs,a task known to be NP-hard.Traditional approaches,primarily focused on graph partitioning strategies,have sought to reduce the number of required Bell pairs for executing non-local CNOT operations,a form of gate teleportation.However,these methods have limitations in terms of efficiency and scalability.Addressing this,our work jointly considers gate and qubit teleportations introducing a novel meta-heuristic algorithm to minimise the network cost of executing a quantum circuit.By allowing dynamic reallocation of qubits along with gate teleportations during circuit execution,our method significantly enhances the overall efficacy and potential scalability of DQC frameworks.In our numerical analysis,we demonstrate that integrating qubit teleportations into our genetic algorithm for optimizing circuit blocking reduces the required resources,specifically the number of EPR pairs,compared to traditional graph partitioning methods.Our results,derived fromboth benchmark and randomly generated circuits,show that as circuit complexity increases—demanding more qubit teleportations—our approach effectively optimises these teleportations throughout the execution,thereby enhancing performance through strategic circuit partitioning.This is a step forward in the pursuit of a global quantum compiler which will ultimately enable the efficient use of a‘quantum data center’in the future.
文摘A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.
基金supported by the Basque Government through the ELKARTEK program for Research and Innovation,under the BRTAQUANTUM project(Grant Agreement No.KK-2022/00041)。
文摘Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely on the complexity of cryptographic operations,which are expected to be efficiently solved by quantum computers soon.This review explores how PPC can be built on top of quantum computing itself to alleviate these future threats.We analyze quantum proposals for Secure Multi-party Computation,Oblivious Transfer and Homomorphic Encryption from the last decade focusing on their maturity and the challenges they currently face.Our findings show a strong focus on purely theoretical works,but a rise on the experimental consideration of these techniques in the last 5 years.The applicability of these techniques to actual use cases is an underexplored aspect which could lead to the practical assessment of these techniques.
文摘Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology,including communication.In this review,we discuss the current state of quantum computing in communication and its potential applications in various areas such as network optimization,signal processing,and machine learning for communication.First,the basic principle of quantum computing,quantum physics systems,and quantum algorithms are analyzed.Then,based on the classification of quantum algorithms,several important basic quantum algorithms,quantum optimization algorithms,and quantum machine learning algorithms are discussed in detail.Finally,the basic ideas and feasibility of introducing quantum algorithms into communications are emphatically analyzed,which provides a reference to address computational bottlenecks in communication networks.
文摘With a paper published in the 19 February 2025 issue of Nature[1],Microsoft(Redmond,WA,USA)fanned the flames of its unique vision for quantum computing:a stable,error-resistant qubit based on the Majorana fermion,one of the strangest and most elusive particles in physics.The Microsoft Azure Quantum research team’s descriptions of a means to detect the as-yet theoretical particles[1]—called“an entirely new state of matter”by Microsoft’s chief executive officer[2]—and a design for a chip powered by them(Fig.1)[3]have refocused attention on the company’s ambition to build a topological quantum computer.The approach—if it works—could potentially leapfrog every other in the field.
基金funded by Researchers Supporting Project Number(RSPD2025R947)King Saud University,Riyadh,Saudi Arabia.
文摘Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.
文摘This paper investigatesWindfarmLayoutOptimization(WFLO),where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization(QUBO)problem.Wind energy plays a critical role in the transition toward sustainable power systems,but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions.With recent advances in quantum computing,there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks.We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver(VQE)implemented onQiskit’s quantum computer simulator,employing a quantum noise-free,gate-based circuit model.Three classical optimizers are discussed,with a detailed analysis of the two most effective approaches:Constrained Optimization BY Linear Approximation(COBYLA)and Bayesian Optimization(BO).We compare these simulated quantum results with two established classical optimization methods:Simulated Annealing(SA)and the Gurobi solver.The study focuses on 4×4 grid configurations(requiring 16 qubits),providing insights into near-term quantum algorithm applicability for renewable energy optimization.
文摘This study aims to develop a quantum computing-based neurocognitive architecture that allows an agent to perform autonomous behaviors.Therefore,we present a brain-inspired cognitive architecture for autonomous agents that integrates a prefrontal cortex-inspired model with modern deep learning(a transformer-based reinforcement learning module)and quantum algorithms.In particular,our framework incorporates quantum computational routines(Deutsch-Jozsa,Bernstein-Vazirani,and Grover’s search)to enhance decision-making efficiency.As a novelty of this research,this comprehensive computational structure is empowered by quantum computing operations so that superiority in speed and robustness of learning compared to classical methods can be demonstrated.Another main contribution is that the proposed architecture offers some features,such as meta-cognition and situation awareness.The meta-cognition aspect is responsible for hierarchically learning sub-tasks,enabling the agent to achieve the master goal.The situation-awareness property identifies how spatial-temporal reasoning activities related to the world model of the agent can be extracted in a dynamic simulation environment with unstructured uncertainties by quantum computation-based machine learning algorithms with the explainable artificial intelligence paradigm.In this research,the Minecraft game-based simulation environment is utilized for the experimental evaluation of performance and verification tests within complex,multi-objective tasks related to the autonomous behaviors of a smart agent.By implementing several interaction scenarios,the results of the system performance and comparative superiority over alternative solutions are presented,and it is discussed how these autonomous behaviors and cognitive skills of a smart agent can be improved in further studies.Results show that the quantum-enhanced agent achieves faster convergence to an 80%task 2×success rate in exploration tasks and approximately 15%higher cumulative rewards compared to a classical deep RL baseline.These findings demonstrate the potential of quantum algorithms to significantly improve learning and performance in cognitive agent architectures.However,advantages are task-specific and less pronounced under high-uncertainty,reactive scenarios.Limitations of the simulation environment are acknowledged,and a structured future research roadmap is proposed involving highfidelity simulation validation,hardware-in-the-loop robotic testing,and integration of advanced hybrid quantum-classical architectures.
基金supported in part by the China Postdoctoral Science Foundation(Grant No.2023M740874)。
文摘The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a fundamental problem in the field of power systems that aims to satisfy the power balance constraint with minimal cost.In this paper,we focus on the implementation of the UC solution using exact quantum algorithms based on the quantum neural network(QNN).This method is tested with a ten-unit system under the power balance constraint.In order to improve computing precision and reduce network complexity,we propose a knowledge-based partially connected quantum neural network(PCQNN).The results show that exact solutions can be obtained by the improved algorithm and that the depth of the quantum circuit can be reduced simultaneously.
基金Gerhard Hellstern is partly funded by the Ministry of Economic Affairs,Labour and Tourism Baden-Württemberg in the frame of the Competence Center Quantum Computing Baden-Württemberg(QORA Ⅱ).
文摘The rapid advancement of quantum computing has sparked a considerable increase in research attention to quantum technologies.These advances span fundamental theoretical inquiries into quantum information and the exploration of diverse applications arising from this evolving quantum computing paradigm.The scope of the related research is notably diverse.This paper consolidates and presents quantum computing research related to the financial sector.The finance applications considered in this study include portfolio optimization,fraud detection,and Monte Carlo methods for derivative pricing and risk calculation.In addition,we provide a comprehensive analysis of quantum computing’s applications and effects on blockchain technologies,particularly in relation to cryptocurrencies,which are central to financial technology research.As discussed in this study,quantum computing applications in finance are based on fundamental quantum physics principles and key quantum algorithms.This review aims to bridge the research gap between quantum computing and finance.We adopt a two-fold methodology,involving an analysis of quantum algorithms,followed by a discussion of their applications in specific financial contexts.Our study is based on an extensive review of online academic databases,search tools,online journal repositories,and whitepapers from 1952 to 2023,including CiteSeerX,DBLP,Research-Gate,Semantic Scholar,and scientific conference publications.We present state-of-theart findings at the intersection of finance and quantum technology and highlight open research questions that will be valuable for industry practitioners and academicians as they shape future research agendas.
文摘This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation systems.The learning-based parameter transfer scheme to realize adaptive quantum optimization leverages Bayesian optimization to predict initial quantum circuit parameters.When applied to the MPC problems formulated as quadratic unconstrained binary optimization problems,this approach computes optimal controls to minimize the net energy consumption levels in buildings and promotes decarbonization while reducing the computational efforts required for the quantum approximate optimization algorithm as the building energy system trajectory progresses.The energy efficiency and the decarbonization benefits of the proposed quantum optimization-based MPC strategy are demonstrated on buildings at the Cornell University campus.The proposed quantum computing-based technique to address MPC problems in buildings demonstrates energy-efficient and low-carbon building operation with a 6.8% improvement over deterministic MPC and presents opportunities for scaling to larger control problems with a significant reduction in utilized quantum computing resources.A reduction of 41.2% in carbon emissions is also achieved with the proposed control strategy facilitated by efficiently managing battery energy storage and renewable generation sources to promote a push toward carbonneutral building operations.
基金supported by the National Natural Science Foundation of China(Grant No.62072259)in part by the Natural Science Foundation of Jiangsu Province(Grant No.BK20221411)+1 种基金the PhD Start-up Fund of Nantong University(Grant No.23B03)the Postgraduate Research&Practice Innovation Program of School of Information Science and Technology,Nantong University(Grant No.NTUSISTPR2405).
文摘In the current noisy intermediate-scale quantum(NISQ)era,a single quantum processing unit(QPU)is insufficient to implement large-scale quantum algorithms;this has driven extensive research into distributed quantum computing(DQC).DQC involves the cooperative operation of multiple QPUs but is concurrently challenged by excessive communication complexity.To address this issue,this paper proposes a quantum circuit partitioning method based on spectral clustering.The approach transforms quantum circuits into weighted graphs and,through computation of the Laplacian matrix and clustering techniques,identifies candidate partition schemes that minimize the total weight of the cut.Additionally,a global gate search tree strategy is introduced to meticulously explore opportunities for merged transfer of global gates,thereby minimizing the transmission cost of distributed quantum circuits and selecting the optimal partition scheme from the candidates.Finally,the proposed method is evaluated through various comparative experiments.The experimental results demonstrate that spectral clustering-based partitioning exhibits robust stability and efficiency in runtime in quantum circuits of different scales.In experiments involving the quantum Fourier transform algorithm and Revlib quantum circuits,the transmission cost achieved by the global gate search tree strategy is significantly optimized.
文摘In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s algorithm hinges on the critical step of determining the period,a process that poses a substantial computational challenge.This article explores innovative quantum optimization solutions that aim to enhance the efficiency of Shor’s period finding algorithm.The article focuses on quantum development environments,such as Qiskit and Cirq.A detailed analysis is conducted on three notable tools:Qiskit Transpiler,BQSKit,and Mitiq.The performance of these tools is evaluated in terms of execution time,precision,resource utilization,the number of quantum gates,circuit synthesis optimization,error mitigation,and qubit fidelity.Through rigorous case studies,we highlight the strengths and limitations of these tools,shedding light on their potential impact on integer factorization and cybersecurity.Our findings underscore the importance of quantum optimization and lay the foundation for future developments in quantum algorithmic enhancements,particularly within the Qiskit and Cirq quantum development environments.
基金supported by National Key Research and Development Program of China(Grant No.2020YFA0309702)the National Natural Science Foundation of China(Grant No.61502526)+1 种基金NSAF(Grant No.U2130205)the Natural Science Foundation of Henan Province,China(Grant Nos.202300410532 and 252300421818)。
文摘Preparing quantum superposition states is a crucial step in realizing quantum algorithms,which demands substantial resources.In this paper,we propose a new method for preparing quantum uniform superposition states via quantum measurement,and design the bitwise implementation circuit,which only contains Hadamard,CNOT,and π/8 phase gates.Compared to the Shukla–Vedula method,the number of quantum gates required by both methods scales the same,while,the new method offers stronger fault tolerance,and the ancillary qubits employed during the implementation process can be reused,making it more suitable for implementation on real quantum computers.As an application,we provide the circuit for Shor's discrete logarithm quantum algorithm,based on the new method,demonstrating its technical advantage for implementation of quantum algorithms.
文摘Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains are becoming increasingly prevalent.Given the restricted control technology,how implementing high-fidelity quantum gate operations is crucial.Many works in current pulse design optimization focus on ion–phonon and effective ion–ion couplings while ignoring the first-order derivative terms expansion impacts of these two terms brought on by experiment defects.This paper proposes a novel robust quantum control optimization method in trapped ions.By introducing the first-order derivative terms caused by the error into the optimization cost function,we generate an extremely robust Mølmer–Sørensen gate with infidelity below 10^(−3) under a drift noise range of±10 kHz,the relative robustness achieves a tolerance of±5%,compared to the 200-kHz frequency spacing between phonon modes,and for time noise drift,the tolerance reached to 2%.Our work reveals the vital role of the first-order derivative terms of coupling in trapped ion pulse control optimization,especially the first-order derivative terms of ion–ion coupling.It provides a robust optimization scheme for realizing more efficient entangled states in trapped ion platforms.
