A subdynamics theory framework for describing multi coupled quantum computing systems is presented first. A general kinetic equation for the reduced system is given then, enabling a sufficient condition to be formula...A subdynamics theory framework for describing multi coupled quantum computing systems is presented first. A general kinetic equation for the reduced system is given then, enabling a sufficient condition to be formulated for constructing a pure coherent quantum computing system. This reveals that using multi coupled systems to perform quantum computing in Rigged Liouville Space opens the door to controlling or eliminating the intrinsic de coherence of quantum computing systems.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
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
As superconducting quantum computing continues to advance at an unprecedented pace,there is a compelling demand for the innovation of specialized electronic instruments that act as crucial conduits between quantum pro...As superconducting quantum computing continues to advance at an unprecedented pace,there is a compelling demand for the innovation of specialized electronic instruments that act as crucial conduits between quantum processors and host computers.Here,we introduce a microwave measurement and control system(M^(2)CS)dedicated to large-scale superconducting quantum processors.M^(2)CS features a compact modular design that balances overall performance,scalability and flexibility.Electronic tests of M^(2)CS show key metrics comparable to commercial instruments.Benchmark tests on transmon superconducting qubits further show qubit coherence and gate fidelities comparable to state-of-the-art results,confirming M^(2)CS's capability to meet the stringent requirements of quantum experiments running on intermediate-scale quantum processors.The compact and scalable nature of our design holds the potential to support over 1000 qubits after upgrade in stability and integration.The M^(2)CS architecture may also be adopted to a wider range of scenarios,including other quantum computing platforms such as trapped ions and silicon quantum dots,as well as more traditional applications like microwave kinetic inductance detectors and phased array radar systems.展开更多
It is well known that the time-dependent Schrrödinger equation can only be solved exactly in very rare cases,even for two-level quantum systems.Thus,finding the exact quantum dynamics under a time-dependent Hamil...It is well known that the time-dependent Schrrödinger equation can only be solved exactly in very rare cases,even for two-level quantum systems.Thus,finding the exact quantum dynamics under a time-dependent Hamiltonian is not only fundamentally important in quantum physics but also facilitates active quantum manipulations for quantum information processing.In this work,we present a method for generating nearly infinite analytically assisted solutions to the Schrödinger equation for a qubit under time-dependent driving.These analytically assisted solutions feature free parameters with only boundary restrictions,making them applicable in a variety of precise quantum manipulations.Due to the general form of the time-dependent Hamiltonian in our approach,it can be readily implemented in various experimental setups involving qubits.Consequently,our scheme offers new solutions to the Schrödinger equation,providing an alternative analytical framework for precise control over qubits.展开更多
Photonic hyper-parallel quantum gates play a critical role in high-capacity quantum communication and fast quantum computing.Here,based on photon scattering in onedimensional(1D)waveguides,we present some heralded sch...Photonic hyper-parallel quantum gates play a critical role in high-capacity quantum communication and fast quantum computing.Here,based on photon scattering in onedimensional(1D)waveguides,we present some heralded schemes for constructing four-qubit hyper-controlled-not(hyper-CNOT)gates in two-photon systems.The qubits are encoded on both the polarization and spatial-mode degrees of freedoms(DOFs)of the photons,which can simplify the quantum circuit and reduce the quantum resource consumption.In our schemes,the faulty scattering events between photons and emitters caused by system imperfections can be filtered out and discarded.That is,our protocols for hyper-CNOT gates work in a heralded way.Our calculations show that,with great progress in the emitter-waveguide systems,our photonic hyper-CNOT gates may be experimentally feasible.展开更多
Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-ve...Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-vector product,matrix-matrix product,the sum of two matrices,and the calculation of determinant and inverse matrix.We encode the matrix entries into the probability amplitudes of the pure initial states of senders.After applying proper unitary transformation to the complete quantum system,the desired result can be found in certain blocks of the receiver’s density matrix.These quantum protocols can be used as subroutines in other quantum schemes.Furthermore,we present an alternative quantum algorithm for solving linear systems of equations.展开更多
As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources ha...As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity,which in turn hampers users from achieving optimal satisfaction.Therefore,cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands.This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment.The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience.It introduces three innovative designs:1)Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks.2)Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud.In comparison to conventional task schedulers,our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud.3)The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.展开更多
One-way quantum computation focuses on initially generating an entangled cluster state followed by a sequence of measurements with classical communication of their individual outcomes.Recently,a delayed-measurement ap...One-way quantum computation focuses on initially generating an entangled cluster state followed by a sequence of measurements with classical communication of their individual outcomes.Recently,a delayed-measurement approach has been applied to replace classical communication of individual measurement outcomes.In this work,by considering the delayed-measurement approach,we demonstrate a modified one-way CNOT gate using the on-cloud superconducting quantum computing platform:Quafu.The modified protocol for one-way quantum computing requires only three qubits rather than the four used in the standard protocol.Since this modified cluster state decreases the number of physical qubits required to implement one-way computation,both the scalability and complexity of the computing process are improved.Compared to previous work,this modified one-way CNOT gate is superior to the standard one in both fidelity and resource requirements.We have also numerically compared the behavior of standard and modified methods in large-scale one-way quantum computing.Our results suggest that in a noisy intermediate-scale quantum(NISQ)era,the modified method shows a significant advantage for one-way quantum computation.展开更多
In distributed quantum computing(DQC),quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections.Meanwhile,quantum software tries its best to reduce the required number...In distributed quantum computing(DQC),quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections.Meanwhile,quantum software tries its best to reduce the required number of remote quantum gates between chips.However,this“hardware first,software follows”methodology may not fully exploit the potential of DQC.Inspired by classical software-hardware co-design,this paper explores the design space of application-specific DQC architectures.More specifically,we propose Auto Arch,an automated quantum chip network(QCN)structure design tool.With qubits grouping followed by a customized QCN design,AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms.Experimental results show that the DQC architecture generated by Auto Arch can outperform other general QCN architectures when executing target quantum algorithms.展开更多
Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits(qubits)can only interact effectively with their nearest neighbors.This constraint severely impacts...Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits(qubits)can only interact effectively with their nearest neighbors.This constraint severely impacts the design and efficiency of quantum algorithms,as arranging qubits optimally can significantly reduce circuit depth and improve computational performance.To tackle the layout synthesis challenge,we propose an algorithm based on integer linear programming(ILP).ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint.The algorithm aims to generate layouts that maximize qubit connectivity within the given physical constraints of the quantum device.For experimental validation,we outline a clear and feasible setup using real quantum devices.This includes specifying the type and configuration of the quantum hardware used,such as the number of qubits,connectivity constraints,and any technological limitations.The proposed algorithm is implemented on these devices to demonstrate its effectiveness in producing depth-optimal quantum circuit layouts.By integrating these elements,our research aims to provide practical solutions to enhance the efficiency and scalability of quantum computing systems,paving the way for advancements in quantum algorithm design and implementation.展开更多
Electric power systems provide the backbone of modern industrial societies.Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems.However,today’s power ...Electric power systems provide the backbone of modern industrial societies.Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems.However,today’s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources,as well as managing huge volumes of data accordingly.These unprecedented challenges call for transformative analytics to support the resilient operations of power systems.Recently,the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations.Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems,which may lead to ultra-scalable and efficient power grid analytics.This paper reviews the newly emerging application of quantum computing techniques in power systems.We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives,including static analysis,transient analysis,stochastic analysis,optimization,stability,and control.We thoroughly discuss the related quantum algorithms,their benefits and limitations,hardware implementations,and recommended practices.We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era.Finally,we discuss challenges and future research directions.This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.展开更多
Quantum Computing (QC) is hailed as the future of computers. After Google’s claim of achieving Quantum Supremacy in 2019, several groups challenged the claim. Some QC experts attribute catastrophic risks that unrestr...Quantum Computing (QC) is hailed as the future of computers. After Google’s claim of achieving Quantum Supremacy in 2019, several groups challenged the claim. Some QC experts attribute catastrophic risks that unrestrained QC may cause in the future by collapsing the current cryptographic cybersecurity infrastructure. These predictions are relevant only if QC becomes commercially viable and sustainable in the future. No technology can be a one-way ticket to catastrophe, and neither can the definition of superiority of that technology be. If there are catastrophic risks, large-scale QC can never enter the public domain as a minimum viable product (MVP) unless there are safeguards in place. Those safeguards should obviously become an integral part of the definition of its superiority over the legacy systems. NIST (National Institute of Standards & Technology) is pursuing the standardization of Post Quantum Cryptography (PQC) as that safeguard. However, with all the 82 candidate PQCs failing and companies already offering QC as a service, there’s an urgent need for an alternate strategy to mitigate the impending Q-Day threat and render QC sustainable. Our research proposes a novel encryption-agnostic cybersecurity approach to safeguard QC. It articulates a comprehensive definition of an MVP that can potentially set a sustainable gold standard for defining commercially viable quantum advantage over classical computing.展开更多
The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand...The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand. However, traditional cloud computing frameworks face significant latency, scalability, and security issues. Quantum-Edge Cloud Computing (QECC) offers an innovative solution by integrating the computational power of quantum computing with the low-latency advantages of edge computing and the scalability of cloud computing resources. This study is grounded in an extensive literature review, performance improvements, and metrics data from Bangladesh, focusing on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. The discussion covers vital elements, including integrating quantum cryptography to enhance data security, the critical role of edge computing in reducing response times, and cloud computing’s ability to support large-scale IoT networks with its extensive resources. Through case studies such as the application of quantum sensors in autonomous vehicles, the practical impact of QECC is demonstrated. Additionally, the paper outlines future research opportunities, including developing quantum-resistant encryption techniques and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC has the potential to overcome the current limitations of IoT frameworks, setting a new standard for future IoT applications.展开更多
Quantum power system state estimation(QPSSE)offers an inspiring direction for tackling the challenge of state estimation through quantum computing.Nevertheless,the current bottlenecks originate from the scarcity of pr...Quantum power system state estimation(QPSSE)offers an inspiring direction for tackling the challenge of state estimation through quantum computing.Nevertheless,the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum(NISQ)era.This paper devises a NISQ−QPSSE algorithm that facilitates state estimation on real NISQ devices.Our new contributions include:(1)A variational quantum circuit(VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits;(2)A variational quantum linear solver(VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization;(3)An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers;(4)A noise-resilient method to alleviate the detrimental effects of noise disturbances.The encouraging test results on the simulator and real-scale systems affirm the precision,universal-ity,and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.展开更多
We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of ...We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.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
Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embed...Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.展开更多
Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum(NISQ)era,overlook the inter-layer operations can be further parall...Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum(NISQ)era,overlook the inter-layer operations can be further parallelized.Based on this,two quantum circuit scheduling optimization approaches are designed and integrated into the quantum circuit compilation process.Firstly,we introduce the Layered Topology Scheduling Approach(LTSA),which employs a greedy algorithm and leverages the principles of topological sorting in graph theory.LTSA allocates quantum gates to a layered structure,maximizing the concurrent execution of quantum gate operations.Secondly,the Layerwise Conflict Resolution Approach(LCRA)is proposed.LCRA focuses on utilizing directly executable quantum gates within layers.Through the insertion of SWAP gates and conflict resolution checks,it minimizes conflicts and enhances parallelism,thereby optimizing the overall computational efficiency.Experimental findings indicate that LTSA and LCRA individually achieve a noteworthy reduction of 51.1%and 53.2%,respectively,in the number of inserted SWAP gates.Additionally,they contribute to a decrease in hardware gate overhead by 14.7%and 15%,respectively.Considering the intricate nature of quantum circuits and the temporal dependencies among different layers,the amalgamation of both approaches leads to a remarkable 51.6%reduction in inserted SWAP gates and a 14.8%decrease in hardware gate overhead.These results underscore the efficacy of the combined LTSA and LCRA in optimizing quantum circuit compilation.展开更多
文摘A subdynamics theory framework for describing multi coupled quantum computing systems is presented first. A general kinetic equation for the reduced system is given then, enabling a sufficient condition to be formulated for constructing a pure coherent quantum computing system. This reveals that using multi coupled systems to perform quantum computing in Rigged Liouville Space opens the door to controlling or eliminating the intrinsic de coherence of quantum computing systems.
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.
基金supported by the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant Nos.KQTD20210811090049034,RCBS20231211090824040,and RCBS20231211090815032)the National Natural Science Foundation of China(Grant Nos.12174178,12204228,12374474,and 123b2071)+2 种基金the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301703)the Shenzhen-Hong Kong Cooperation Zone for Technology and Innovation(Grant No.HZQB-KCZYB-2020050)Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2024A1515011714 and 2022A1515110615)。
文摘As superconducting quantum computing continues to advance at an unprecedented pace,there is a compelling demand for the innovation of specialized electronic instruments that act as crucial conduits between quantum processors and host computers.Here,we introduce a microwave measurement and control system(M^(2)CS)dedicated to large-scale superconducting quantum processors.M^(2)CS features a compact modular design that balances overall performance,scalability and flexibility.Electronic tests of M^(2)CS show key metrics comparable to commercial instruments.Benchmark tests on transmon superconducting qubits further show qubit coherence and gate fidelities comparable to state-of-the-art results,confirming M^(2)CS's capability to meet the stringent requirements of quantum experiments running on intermediate-scale quantum processors.The compact and scalable nature of our design holds the potential to support over 1000 qubits after upgrade in stability and integration.The M^(2)CS architecture may also be adopted to a wider range of scenarios,including other quantum computing platforms such as trapped ions and silicon quantum dots,as well as more traditional applications like microwave kinetic inductance detectors and phased array radar systems.
基金supported by the National Natural Science Foundation of China(Grant No.12275090)the Guangdong Provincial Quantum Science Strategic Initiative(Grant No.GDZX2203001)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302303)。
文摘It is well known that the time-dependent Schrrödinger equation can only be solved exactly in very rare cases,even for two-level quantum systems.Thus,finding the exact quantum dynamics under a time-dependent Hamiltonian is not only fundamentally important in quantum physics but also facilitates active quantum manipulations for quantum information processing.In this work,we present a method for generating nearly infinite analytically assisted solutions to the Schrödinger equation for a qubit under time-dependent driving.These analytically assisted solutions feature free parameters with only boundary restrictions,making them applicable in a variety of precise quantum manipulations.Due to the general form of the time-dependent Hamiltonian in our approach,it can be readily implemented in various experimental setups involving qubits.Consequently,our scheme offers new solutions to the Schrödinger equation,providing an alternative analytical framework for precise control over qubits.
基金supported by the Tianjin Natural Science Foundation under Grant No.23JCQNJC00560the Natural Science Funds of Tianjin Normal University under Grant No.YJRC202421+2 种基金the National Natural Science Foundation of China under Grants Nos.12004281 and 62371038the Scientific Research the Natural Science Basic Research Program of Shaanxi under Grant No.2023-JC-QN-0092the Program Funded by Education Department of Shaanxi Provincial Government under Grant No.23JK0705。
文摘Photonic hyper-parallel quantum gates play a critical role in high-capacity quantum communication and fast quantum computing.Here,based on photon scattering in onedimensional(1D)waveguides,we present some heralded schemes for constructing four-qubit hyper-controlled-not(hyper-CNOT)gates in two-photon systems.The qubits are encoded on both the polarization and spatial-mode degrees of freedoms(DOFs)of the photons,which can simplify the quantum circuit and reduce the quantum resource consumption.In our schemes,the faulty scattering events between photons and emitters caused by system imperfections can be filtered out and discarded.That is,our protocols for hyper-CNOT gates work in a heralded way.Our calculations show that,with great progress in the emitter-waveguide systems,our photonic hyper-CNOT gates may be experimentally feasible.
基金supported by the National Natural Science Foundation of China(Grant No.12031004 and Grant No.12271474,61877054)the Fundamental Research Foundation for the Central Universities(Project No.K20210337)+1 种基金the Zhejiang University Global Partnership Fund,188170+194452119/003partially funded by a state task of Russian Fundamental Investigations(State Registration No.FFSG-2024-0002)。
文摘Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-vector product,matrix-matrix product,the sum of two matrices,and the calculation of determinant and inverse matrix.We encode the matrix entries into the probability amplitudes of the pure initial states of senders.After applying proper unitary transformation to the complete quantum system,the desired result can be found in certain blocks of the receiver’s density matrix.These quantum protocols can be used as subroutines in other quantum schemes.Furthermore,we present an alternative quantum algorithm for solving linear systems of equations.
文摘As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity,which in turn hampers users from achieving optimal satisfaction.Therefore,cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands.This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment.The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience.It introduces three innovative designs:1)Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks.2)Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud.In comparison to conventional task schedulers,our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud.3)The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.
基金the valuable discussions.Project supported by the National Natural Science Foundation of China(Grant Nos.92265207 and T2121001)Beijing Natural Science Foundation(Grant No.Z200009).
文摘One-way quantum computation focuses on initially generating an entangled cluster state followed by a sequence of measurements with classical communication of their individual outcomes.Recently,a delayed-measurement approach has been applied to replace classical communication of individual measurement outcomes.In this work,by considering the delayed-measurement approach,we demonstrate a modified one-way CNOT gate using the on-cloud superconducting quantum computing platform:Quafu.The modified protocol for one-way quantum computing requires only three qubits rather than the four used in the standard protocol.Since this modified cluster state decreases the number of physical qubits required to implement one-way computation,both the scalability and complexity of the computing process are improved.Compared to previous work,this modified one-way CNOT gate is superior to the standard one in both fidelity and resource requirements.We have also numerically compared the behavior of standard and modified methods in large-scale one-way quantum computing.Our results suggest that in a noisy intermediate-scale quantum(NISQ)era,the modified method shows a significant advantage for one-way quantum computation.
基金Project supported by the National Key R&D Program of China(Grant No.2023YFA1009403)the National Natural Science Foundation of China(Grant Nos.62072176 and 62472175)the“Digital Silk Road”Shanghai International Joint Lab of Trustworthy Intelligent Software(Grant No.22510750100)。
文摘In distributed quantum computing(DQC),quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections.Meanwhile,quantum software tries its best to reduce the required number of remote quantum gates between chips.However,this“hardware first,software follows”methodology may not fully exploit the potential of DQC.Inspired by classical software-hardware co-design,this paper explores the design space of application-specific DQC architectures.More specifically,we propose Auto Arch,an automated quantum chip network(QCN)structure design tool.With qubits grouping followed by a customized QCN design,AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms.Experimental results show that the DQC architecture generated by Auto Arch can outperform other general QCN architectures when executing target quantum algorithms.
基金supported by National Science and Technology Council,Taiwan,NSTC 112-2221-E-024-004.
文摘Layout synthesis in quantum computing is crucial due to the physical constraints of quantum devices where quantum bits(qubits)can only interact effectively with their nearest neighbors.This constraint severely impacts the design and efficiency of quantum algorithms,as arranging qubits optimally can significantly reduce circuit depth and improve computational performance.To tackle the layout synthesis challenge,we propose an algorithm based on integer linear programming(ILP).ILP is well-suited for this problem as it can formulate the optimization objective of minimizing circuit depth while adhering to the nearest neighbor interaction constraint.The algorithm aims to generate layouts that maximize qubit connectivity within the given physical constraints of the quantum device.For experimental validation,we outline a clear and feasible setup using real quantum devices.This includes specifying the type and configuration of the quantum hardware used,such as the number of qubits,connectivity constraints,and any technological limitations.The proposed algorithm is implemented on these devices to demonstrate its effectiveness in producing depth-optimal quantum circuit layouts.By integrating these elements,our research aims to provide practical solutions to enhance the efficiency and scalability of quantum computing systems,paving the way for advancements in quantum algorithm design and implementation.
基金supported in part by the Advanced Grid Modeling Program under U.S.Department of Energy’s Office of Electricity under Agreement No.37533(P.Z.)in part by Stony Brook Uni-versity’s Office of the Vice President for Research through a Quantum Information Science and Technology Seed Grant(P.Z.)in part by the National Science Foundation under Grant No.PHY 1915165(T.-C.W.).
文摘Electric power systems provide the backbone of modern industrial societies.Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems.However,today’s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources,as well as managing huge volumes of data accordingly.These unprecedented challenges call for transformative analytics to support the resilient operations of power systems.Recently,the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations.Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems,which may lead to ultra-scalable and efficient power grid analytics.This paper reviews the newly emerging application of quantum computing techniques in power systems.We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives,including static analysis,transient analysis,stochastic analysis,optimization,stability,and control.We thoroughly discuss the related quantum algorithms,their benefits and limitations,hardware implementations,and recommended practices.We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era.Finally,we discuss challenges and future research directions.This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.
文摘Quantum Computing (QC) is hailed as the future of computers. After Google’s claim of achieving Quantum Supremacy in 2019, several groups challenged the claim. Some QC experts attribute catastrophic risks that unrestrained QC may cause in the future by collapsing the current cryptographic cybersecurity infrastructure. These predictions are relevant only if QC becomes commercially viable and sustainable in the future. No technology can be a one-way ticket to catastrophe, and neither can the definition of superiority of that technology be. If there are catastrophic risks, large-scale QC can never enter the public domain as a minimum viable product (MVP) unless there are safeguards in place. Those safeguards should obviously become an integral part of the definition of its superiority over the legacy systems. NIST (National Institute of Standards & Technology) is pursuing the standardization of Post Quantum Cryptography (PQC) as that safeguard. However, with all the 82 candidate PQCs failing and companies already offering QC as a service, there’s an urgent need for an alternate strategy to mitigate the impending Q-Day threat and render QC sustainable. Our research proposes a novel encryption-agnostic cybersecurity approach to safeguard QC. It articulates a comprehensive definition of an MVP that can potentially set a sustainable gold standard for defining commercially viable quantum advantage over classical computing.
文摘The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand. However, traditional cloud computing frameworks face significant latency, scalability, and security issues. Quantum-Edge Cloud Computing (QECC) offers an innovative solution by integrating the computational power of quantum computing with the low-latency advantages of edge computing and the scalability of cloud computing resources. This study is grounded in an extensive literature review, performance improvements, and metrics data from Bangladesh, focusing on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. The discussion covers vital elements, including integrating quantum cryptography to enhance data security, the critical role of edge computing in reducing response times, and cloud computing’s ability to support large-scale IoT networks with its extensive resources. Through case studies such as the application of quantum sensors in autonomous vehicles, the practical impact of QECC is demonstrated. Additionally, the paper outlines future research opportunities, including developing quantum-resistant encryption techniques and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC has the potential to overcome the current limitations of IoT frameworks, setting a new standard for future IoT applications.
基金supported in part by the National Science Foundation under Grant No.ITE-2134840.This work relates to Department of Navy award N00014-23-1-2124 issued by the Office of Naval Research.The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein.
文摘Quantum power system state estimation(QPSSE)offers an inspiring direction for tackling the challenge of state estimation through quantum computing.Nevertheless,the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum(NISQ)era.This paper devises a NISQ−QPSSE algorithm that facilitates state estimation on real NISQ devices.Our new contributions include:(1)A variational quantum circuit(VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits;(2)A variational quantum linear solver(VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization;(3)An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers;(4)A noise-resilient method to alleviate the detrimental effects of noise disturbances.The encouraging test results on the simulator and real-scale systems affirm the precision,universal-ity,and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.
基金supported by the National Natural Science Foundation of China(Grant Nos.12441502,12122506,12204230,and 12404554)the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0300404)+6 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2021B1515020070)Shenzhen Science and Technology Program(Grant No.RCYX20200714114522109)China Postdoctoral Science Foundation(CPSF)(2024M762114)Postdoctoral Fellowship Program of CPSF(GZC20231727)supported by the National Natural Science Foundation of China(Grant Nos.92165206 and 11974330)Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301603)the Fundamental Research Funds for the Central Universities。
文摘We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.
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.
文摘Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.
基金funded by the Natural Science Foundation of Heilongjiang Province(Grant No.LH2022F035)the Cultivation Programme for Young Innovative Talents in Ordinary Higher Education Institutions of Heilongjiang Province(Grant No.UNPYSCT-2020212)the Cultivation Programme for Young Innovative Talents in Scientific Research of Harbin University of Commerce(Grant No.2023-KYYWF-0983).
文摘Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum(NISQ)era,overlook the inter-layer operations can be further parallelized.Based on this,two quantum circuit scheduling optimization approaches are designed and integrated into the quantum circuit compilation process.Firstly,we introduce the Layered Topology Scheduling Approach(LTSA),which employs a greedy algorithm and leverages the principles of topological sorting in graph theory.LTSA allocates quantum gates to a layered structure,maximizing the concurrent execution of quantum gate operations.Secondly,the Layerwise Conflict Resolution Approach(LCRA)is proposed.LCRA focuses on utilizing directly executable quantum gates within layers.Through the insertion of SWAP gates and conflict resolution checks,it minimizes conflicts and enhances parallelism,thereby optimizing the overall computational efficiency.Experimental findings indicate that LTSA and LCRA individually achieve a noteworthy reduction of 51.1%and 53.2%,respectively,in the number of inserted SWAP gates.Additionally,they contribute to a decrease in hardware gate overhead by 14.7%and 15%,respectively.Considering the intricate nature of quantum circuits and the temporal dependencies among different layers,the amalgamation of both approaches leads to a remarkable 51.6%reduction in inserted SWAP gates and a 14.8%decrease in hardware gate overhead.These results underscore the efficacy of the combined LTSA and LCRA in optimizing quantum circuit compilation.
