Deep Variational Quantum Eigensolver: A Divide-And-Conquer Method for Solving a Larger Problem with Smaller Size Quantum Computers
Year: 2022
Type: article
Abstract: We propose a divide-and-conquer method for the quantum-classical hybrid algorithm to solve larger problems with small-scale quantum computers.Specifically, we concatenate a variational quantum eigensolver (VQE) with a reduction in the system dimension, where the interactions between divided subsystems are taken as an effective Hamiltonian expanded by the reduced basis.Then the effective Hamiltonian is further solved by the VQE, which we call deep VQE.Deep VQE allows us to apply quantum-classical hybrid algorithms on small-scale quantum computers to large systems with strong intrasubsystem interactions and weak intersubsystem interactions, or strongly correlated spin models on large regular lattices.As proof-of-principle numerical demonstrations, we use the proposed method for quasi-one-dimensional models, including one-dimensionally coupled 12-qubit Heisenberg antiferromagnetic models on kagome lattices as well as two-dimensional Heisenberg antiferromagnetic models on square lattices.The largest problem size of 64 qubits is solved by simulating 20-qubit quantum computers with a reasonably good accuracy approximately a few %.The proposed scheme enables us to handle the problems of > 1000 qubits by concatenating VQEs with a few tens of qubits.While it is unclear how accurate ground-state energy can be obtained for such a large system, our numerical results on a 64-qubit system suggest that deep VQE provides a good approximation (discrepancy within a few percent) and has room for further improvement.Therefore, deep VQE provides us a promising pathway to solve practically important problems on noisy intermediate-scale quantum computers.
Source: PRX Quantum
Institutions: RIKEN Center for Quantum Computing, Institute for Open and Transdisciplinary Research Initiatives, Center for Quantum Information and Quantum Biology, The University of Osaka, The University of Osaka, Kyoto University, Japan Science and Technology Agency, RIKEN Center for Computational Science, QunaSys
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,450
Funders: Foundation for Computational Science, Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science, RIKEN, Core Research for Evolutional Science and Technology, Precursory Research for Embryonic Science and Technology, Exploratory Research for Advanced Technology, Co-creation place formation support program
Awards: G1257341445, G1771559835, Quantum-classical hybrid quantum computing for quantum many-body systems, G2335984322, G2409532160, G2719264401, G2906058014, G2923841080, G3033043436, G3195972262, G3303923392, G3999249018, G4023031295, G4094554497, G4627739467, 共鳴的周期駆動がもたらす非平衡特有の物性現象に関する研究, G5248268020, G5523010673, G5567475573, Investigation of excited state dynamics of carotenoid via multi-reference perturbation theory using the Unsold approximation., G6278363646, G6718509927, G6951505830, G7492538774, G7621726476, G7879866177, G8418054547, Advancement of density matrix renormalization group - Adaptation to point group symmetry
OpenAlex ID: W3045458491