We experimentally demonstrate a qubit-efficient variational quantum eigensolver(VQE)algorithm using a superconducting quantum processor,employing minimal quantum resources with only a transmon qubit coupled to a high-...We experimentally demonstrate a qubit-efficient variational quantum eigensolver(VQE)algorithm using a superconducting quantum processor,employing minimal quantum resources with only a transmon qubit coupled to a high-coherence photonic qubit.By leveraging matrix product states to compress the quantum state representation,we simulate an N+1-spin circular Ising model with a transverse field.Furthermore,we develop an analog error mitigation approach through zero-noise extrapolation by introducing a precise noise injection technique for the transmon qubit.As a validation,we apply our error-mitigated qubit-efficient VQE in determining the ground state energies of a 4-spin Ising model.Our results demonstrate the feasibility of performing quantum algorithms with minimal quantum resources while effectively mitigating the impact of noise,offering a promising pathway to bridge the gap between theoretical advances and practical implementations on current noisy intermediate-scale quantum devices.展开更多
The problem of social workers visiting their patients at home is a class of combinatorial optimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to pro...The problem of social workers visiting their patients at home is a class of combinatorial optimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to provide an efficient solution in the best of cases. In this article, in addition to providing a detailed resolution of the social workers’ problem using the Quadratic Unconstrained Binary Optimization Problems (QUBO) formulation, an approach to mapping the inequality constraints in the QUBO form is given. Finally, we map it in the Hamiltonian of the Ising model to solve it with the Quantum Exact Solver and Variational Quantum Eigensolvers (VQE). The quantum feasibility of the algorithm will be tested on IBMQ computers.展开更多
The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using h...The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using hybrid quantum-classical frameworks.Initially proposed to estimate the ground state energies of quantum systems,VQE combines the quantum circuits with the classical optimization approaches,harnessing the strengths of both computational paradigms [1].展开更多
Quantum algorithms have been developed for efficiently solving linear algebra tasks.However,they generally require deep circuits and hence universal fault-tolerant quantum computers.In this work,we propose variational...Quantum algorithms have been developed for efficiently solving linear algebra tasks.However,they generally require deep circuits and hence universal fault-tolerant quantum computers.In this work,we propose variational algorithms for linear algebra tasks that are compatible with noisy intermediate-scale quantum devices.We show that the solutions of linear systems of equations and matrix–vector multiplications can be translated as the ground states of the constructed Hamiltonians.Based on the variational quantum algorithms,we introduce Hamiltonian morphing together with an adaptive ans?tz for efficiently finding the ground state,and show the solution verification.Our algorithms are especially suitable for linear algebra problems with sparse matrices,and have wide applications in machine learning and optimisation problems.The algorithm for matrix multiplications can be also used for Hamiltonian simulation and open system simulation.We evaluate the cost and effectiveness of our algorithm through numerical simulations for solving linear systems of equations.We implement the algorithm on the IBM quantum cloud device with a high solution fidelity of 99.95%.展开更多
We report a benchmark calculation for the Lipkin model in nuclear physics with a variational quantum eigensolver in quantum computing.Special attention is paid to the unitary coupled cluster(UCC)ansatz and structure l...We report a benchmark calculation for the Lipkin model in nuclear physics with a variational quantum eigensolver in quantum computing.Special attention is paid to the unitary coupled cluster(UCC)ansatz and structure learning(SL)ansatz for the trial wave function.Calculations with both the UCC and SL ansatz can reproduce the ground-state energy well;however,it is found that the calculation with the SL ansatz performs better than thatwith the UCC ansatz,and the SL ansatz has even fewer quantum gates than the UCC ansatz.展开更多
基金supported by the National Natural Science Foundation of China(Grants Nos.11925404,92165209,92365301,92265210,11890704,92365206,12474498,T2225018,92270107,12188101,T2121001,and 62173201)the Innovation Program for Quantum Science and Technology(Grant Nos.2021ZD0300200,and 2021ZD0301800)+2 种基金the National Key R&D Program(Grants No.2017YFA0304303)supported by the Fundamental Research Funds for the Central UniversitiesUSTC Research Funds of the Double First-Class Initiative。
文摘We experimentally demonstrate a qubit-efficient variational quantum eigensolver(VQE)algorithm using a superconducting quantum processor,employing minimal quantum resources with only a transmon qubit coupled to a high-coherence photonic qubit.By leveraging matrix product states to compress the quantum state representation,we simulate an N+1-spin circular Ising model with a transverse field.Furthermore,we develop an analog error mitigation approach through zero-noise extrapolation by introducing a precise noise injection technique for the transmon qubit.As a validation,we apply our error-mitigated qubit-efficient VQE in determining the ground state energies of a 4-spin Ising model.Our results demonstrate the feasibility of performing quantum algorithms with minimal quantum resources while effectively mitigating the impact of noise,offering a promising pathway to bridge the gap between theoretical advances and practical implementations on current noisy intermediate-scale quantum devices.
文摘The problem of social workers visiting their patients at home is a class of combinatorial optimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to provide an efficient solution in the best of cases. In this article, in addition to providing a detailed resolution of the social workers’ problem using the Quadratic Unconstrained Binary Optimization Problems (QUBO) formulation, an approach to mapping the inequality constraints in the QUBO form is given. Finally, we map it in the Hamiltonian of the Ising model to solve it with the Quantum Exact Solver and Variational Quantum Eigensolvers (VQE). The quantum feasibility of the algorithm will be tested on IBMQ computers.
文摘The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using hybrid quantum-classical frameworks.Initially proposed to estimate the ground state energies of quantum systems,VQE combines the quantum circuits with the classical optimization approaches,harnessing the strengths of both computational paradigms [1].
基金the Engineering and Physical Sciences Research Council National Quantum Technology Hub in Networked Quantum Information Technology(EP/M013243/1)Japan Student Services Organization(JASSO)Student Exchange Support Program(Graduate Scholarship for Degree Seeking Students)+1 种基金the National Natural Science Foundation of China(U1730449)the European Quantum Technology Flagship project AQTION。
文摘Quantum algorithms have been developed for efficiently solving linear algebra tasks.However,they generally require deep circuits and hence universal fault-tolerant quantum computers.In this work,we propose variational algorithms for linear algebra tasks that are compatible with noisy intermediate-scale quantum devices.We show that the solutions of linear systems of equations and matrix–vector multiplications can be translated as the ground states of the constructed Hamiltonians.Based on the variational quantum algorithms,we introduce Hamiltonian morphing together with an adaptive ans?tz for efficiently finding the ground state,and show the solution verification.Our algorithms are especially suitable for linear algebra problems with sparse matrices,and have wide applications in machine learning and optimisation problems.The algorithm for matrix multiplications can be also used for Hamiltonian simulation and open system simulation.We evaluate the cost and effectiveness of our algorithm through numerical simulations for solving linear systems of equations.We implement the algorithm on the IBM quantum cloud device with a high solution fidelity of 99.95%.
基金the financial support of Advanced Leading Graduate Course for Photon Sciencethe JSPS Grant-in-Aid for Early-Career Scientists (18K13549)the JSPS Grant-in-Aid for Scientific Research (S)(20H05648)
文摘We report a benchmark calculation for the Lipkin model in nuclear physics with a variational quantum eigensolver in quantum computing.Special attention is paid to the unitary coupled cluster(UCC)ansatz and structure learning(SL)ansatz for the trial wave function.Calculations with both the UCC and SL ansatz can reproduce the ground-state energy well;however,it is found that the calculation with the SL ansatz performs better than thatwith the UCC ansatz,and the SL ansatz has even fewer quantum gates than the UCC ansatz.
