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
This paper investigates Windfarm Layout Optimization(WFLO),where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization(QUBO)problem.Wind energy plays a critical role ...This paper investigates Windfarm Layout Optimization(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
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
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].
文摘This paper investigates Windfarm Layout Optimization(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.
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.
