We study the approximation of the imbedding of functions from anisotropic and generalized Sobolev classes into L q ([0, 1]d) space in the quantum model of computation. Based on the quantum algorithms for approximation...We study the approximation of the imbedding of functions from anisotropic and generalized Sobolev classes into L q ([0, 1]d) space in the quantum model of computation. Based on the quantum algorithms for approximation of finite imbedding from L p N to L q N , we develop quantum algorithms for approximating the imbedding from anisotropic Sobolev classes B(W p r ([0, 1] d )) to L q ([0, 1] d ) space for all 1 ? q,p ? ∞ and prove their optimality. Our results show that for p < q the quantum model of computation can bring a speedup roughly up to a squaring of the rate in the classical deterministic and randomized settings.展开更多
We study the approximation of functions from anisotropic Sobolev classes b（WpR（[0, 1]d）） and HSlder-Nikolskii classes B（HPr（[0, 1]d）） in the Lq （[0, 1]d） norm with q 〈 p in the quantum model of computation....We study the approximation of functions from anisotropic Sobolev classes b（WpR（[0, 1]d）） and HSlder-Nikolskii classes B（HPr（[0, 1]d）） in the Lq （[0, 1]d） norm with q 〈 p in the quantum model of computation. We determine the quantum query complexity of this problem up to logarithmic factors. It shows that the quantum algorithms are significantly better than the classical deterministic or randomized algorithms.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
As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and soluti...As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and solution optimality.To tackle this issue,we propose a problem-structure-informed quantum approximate optimization algorithm(QAOA)framework that fully exploits the quantum advantage under extremely limited quantum resources.Specifically,we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems,which are solvable in parallel by limited number of qubits.This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse.Consequently,our approach can be extended to future power systems that are larger and more complex.展开更多
The Quantum Approximate Optimization Algorithm(QAOA)is an algorithmic framework for finding approximate solutions to combinatorial optimization problems.It consists of interleaved unitary transformations induced by tw...The Quantum Approximate Optimization Algorithm(QAOA)is an algorithmic framework for finding approximate solutions to combinatorial optimization problems.It consists of interleaved unitary transformations induced by two operators labelled the mixing and problem Hamiltonians.To fit this framework,one needs to transform the original problem into a suitable form and embed it into these two Hamiltonians.In this paper,for the well-known NP-hard Traveling Salesman Problem(TSP),we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem Hamiltonian.Moreover,we map edges(routes)connecting each pair of cities to qubits,which decreases the search space significantly in comparison to other approaches.As a result,our method can achieve a higher probability for the shortest round-trip route with only half the number of qubits consumed compared to IBM Q’s approach.We argue the formalization approach presented in this paper would lead to a generalized framework for finding,in the context of QAOA,high-quality approximate solutions to NP optimization problems.展开更多
In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelera...In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelerate computations tailored to specific problems with shallow circuits.However,these algorithms encounter biases and iteration difficulties due to significant noise in quantum processors.These difficulties can only be partially addressed without error correction by optimizing hardware,reducing circuit complexity,or fitting and extrapolating.A compelling solution is applying probabilistic error cancellation(PEC),a quantum error mitigation technique that enables unbiased results without full error correction.Traditional PEC is challenging to apply in VQAs due to its variance amplification,contradicting iterative process assumptions.This paper proposes a novel noise-adaptable strategy that combines PEC with the quantum approximate optimization algorithm(QAOA).It is implemented through invariant sampling circuits(invariant-PEC,or IPEC)and substantially reduces iteration variance.This strategy marks the first successful integration of PEC and QAOA,resulting in efficient convergence.Moreover,we introduce adaptive partial PEC(APPEC),which modulates the error cancellation proportion of IPEC during iteration.We experimentally validate this technique on a superconducting quantum processor,cutting sampling cost by 90.1%.Notably,we find that dynamic adjustments of error levels via APPEC can enhance the ability to escape from local minima and reduce sampling costs.These results open promising avenues for executing VQAs with large-scale,low-noise quantum circuits,paving the way for practical quantum computing advancements.展开更多
The classical adiabatic approximation theory gives an adiabatic approximate solution to the Schr6dinger equation （SE） by choosing a single eigenstate of the Hamiltonian as the initial state. The superposition princi...The classical adiabatic approximation theory gives an adiabatic approximate solution to the Schr6dinger equation （SE） by choosing a single eigenstate of the Hamiltonian as the initial state. The superposition principle of quantum states enables us to mathematically discuss the exact solution to the SE starting from a superposition of two different eigenstates of the time-dependent Hamiltonian H（0）. Also, we can construct an approximate solution to the SE in terms of the corresponding instantaneous eigenstates of H（t）. On the other hand, any physical experiment may bring errors so that the initial state （input state） may be a superposition of different eigenstates, not just at the desired eigenstate. In this paper, we consider the generalized adiabatic evolution of a quantum system starting from a superposition of two different eigenstates of the Hamiltonian at t = 0. A generalized adiabatic approximate solution （GAAS） is constructed and an upper bound for the generalized adiabatic approximation error is given. As an application, the fidelity of the exact solution and the GAAS is estimated.展开更多
Since novel optoelectronic devices based on the peculiar behaviors of the tunneling probability, e.g., resonant tunneling devices (RTD) and band-pass filter, are steadily proposed, the analytic transfer matrix (ATM) m...Since novel optoelectronic devices based on the peculiar behaviors of the tunneling probability, e.g., resonant tunneling devices (RTD) and band-pass filter, are steadily proposed, the analytic transfer matrix (ATM) method is extended to study these devices. For several examples, we explore the effect of the scattered subwaves on tunneling; it is shown that the resonant or band-pass structures in tunneling probability are determined by the phase shift results from the scattered subwaves.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 10501026, 60675010,10626029 and 60572113)the China Postdoctoral Science Foundation (Grant No. 20070420708)
文摘We study the approximation of the imbedding of functions from anisotropic and generalized Sobolev classes into L q ([0, 1]d) space in the quantum model of computation. Based on the quantum algorithms for approximation of finite imbedding from L p N to L q N , we develop quantum algorithms for approximating the imbedding from anisotropic Sobolev classes B(W p r ([0, 1] d )) to L q ([0, 1] d ) space for all 1 ? q,p ? ∞ and prove their optimality. Our results show that for p < q the quantum model of computation can bring a speedup roughly up to a squaring of the rate in the classical deterministic and randomized settings.
基金Supported by the Natural Science Foundation of China(10501026,60675010,10971251)
文摘We study the approximation of functions from anisotropic Sobolev classes b（WpR（[0, 1]d）） and HSlder-Nikolskii classes B（HPr（[0, 1]d）） in the Lq （[0, 1]d） norm with q 〈 p in the quantum model of computation. We determine the quantum query complexity of this problem up to logarithmic factors. It shows that the quantum algorithms are significantly better than the classical deterministic or randomized algorithms.
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.
文摘As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and solution optimality.To tackle this issue,we propose a problem-structure-informed quantum approximate optimization algorithm(QAOA)framework that fully exploits the quantum advantage under extremely limited quantum resources.Specifically,we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems,which are solvable in parallel by limited number of qubits.This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse.Consequently,our approach can be extended to future power systems that are larger and more complex.
基金This work is supported by the Natural Science Foundation,China(Grant No.61802002)Natural Science Foundation of Anhui Province,China(Grant No.1708085MF162).
文摘The Quantum Approximate Optimization Algorithm(QAOA)is an algorithmic framework for finding approximate solutions to combinatorial optimization problems.It consists of interleaved unitary transformations induced by two operators labelled the mixing and problem Hamiltonians.To fit this framework,one needs to transform the original problem into a suitable form and embed it into these two Hamiltonians.In this paper,for the well-known NP-hard Traveling Salesman Problem(TSP),we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem Hamiltonian.Moreover,we map edges(routes)connecting each pair of cities to qubits,which decreases the search space significantly in comparison to other approaches.As a result,our method can achieve a higher probability for the shortest round-trip route with only half the number of qubits consumed compared to IBM Q’s approach.We argue the formalization approach presented in this paper would lead to a generalized framework for finding,in the context of QAOA,high-quality approximate solutions to NP optimization problems.
基金supported by the Innovation Program for Quantum Science and Technology(Grant Nos.2021ZD0301702,and 2024ZD0302000)the Natural Science Foundation of Jiangsu Province(Grant No.BK20232002)+1 种基金the National Natural Science Foundation of China(Grant Nos.U21A20436,and 12074179)the Natural Science Foundation of Shandong Province(Grant No.ZR2023LZH002)。
文摘In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelerate computations tailored to specific problems with shallow circuits.However,these algorithms encounter biases and iteration difficulties due to significant noise in quantum processors.These difficulties can only be partially addressed without error correction by optimizing hardware,reducing circuit complexity,or fitting and extrapolating.A compelling solution is applying probabilistic error cancellation(PEC),a quantum error mitigation technique that enables unbiased results without full error correction.Traditional PEC is challenging to apply in VQAs due to its variance amplification,contradicting iterative process assumptions.This paper proposes a novel noise-adaptable strategy that combines PEC with the quantum approximate optimization algorithm(QAOA).It is implemented through invariant sampling circuits(invariant-PEC,or IPEC)and substantially reduces iteration variance.This strategy marks the first successful integration of PEC and QAOA,resulting in efficient convergence.Moreover,we introduce adaptive partial PEC(APPEC),which modulates the error cancellation proportion of IPEC during iteration.We experimentally validate this technique on a superconducting quantum processor,cutting sampling cost by 90.1%.Notably,we find that dynamic adjustments of error levels via APPEC can enhance the ability to escape from local minima and reduce sampling costs.These results open promising avenues for executing VQAs with large-scale,low-noise quantum circuits,paving the way for practical quantum computing advancements.
基金supported by the National Natural Science Foundation of China(Grant Nos.11371012,11171197 and 11401359)the Innovation Fund Project for Graduate Program of Shaanxi Normal University(GrantNo.2013CXB012)+2 种基金the Fundamental Research Funds for the Central Universities(Grant Nos.GK201301007 and GK201404001)the Science Foundation of Weinan Normal University(Grant No.14YKS006)the Foundation of Mathematics Subject of Shaanxi Province(Grant No.14SXZD009)
文摘The classical adiabatic approximation theory gives an adiabatic approximate solution to the Schr6dinger equation （SE） by choosing a single eigenstate of the Hamiltonian as the initial state. The superposition principle of quantum states enables us to mathematically discuss the exact solution to the SE starting from a superposition of two different eigenstates of the time-dependent Hamiltonian H（0）. Also, we can construct an approximate solution to the SE in terms of the corresponding instantaneous eigenstates of H（t）. On the other hand, any physical experiment may bring errors so that the initial state （input state） may be a superposition of different eigenstates, not just at the desired eigenstate. In this paper, we consider the generalized adiabatic evolution of a quantum system starting from a superposition of two different eigenstates of the Hamiltonian at t = 0. A generalized adiabatic approximate solution （GAAS） is constructed and an upper bound for the generalized adiabatic approximation error is given. As an application, the fidelity of the exact solution and the GAAS is estimated.
基金supported by the State Key Laboratory of Advanced Optical Communication Systems and Networks (Grant No. 2008SH05)
文摘Since novel optoelectronic devices based on the peculiar behaviors of the tunneling probability, e.g., resonant tunneling devices (RTD) and band-pass filter, are steadily proposed, the analytic transfer matrix (ATM) method is extended to study these devices. For several examples, we explore the effect of the scattered subwaves on tunneling; it is shown that the resonant or band-pass structures in tunneling probability are determined by the phase shift results from the scattered subwaves.
