Abstract
A novel quantum–classical hybrid scheme is proposed to efficiently solve large-scale combinatorial optimization problems. The key concept is to introduce a Hamiltonian dynamics of the classical flux variables associated with the quantum spins of the transverse-field Ising model. Molecular dynamics of the classical fluxes can be used as a powerful preconditioner to sort out the frozen and ambivalent spins for quantum annealers. The performance and accuracy of our smooth hybridization in comparison to the standard classical algorithms (the tabu search and the simulated annealing) are demonstrated by employing the MAX-CUT and Ising spin-glass problems.
Highlights
A novel quantum–classical hybrid scheme is proposed to efficiently solve large-scale combinatorial optimization problems
A large class of combinatorial optimization problems can be mapped onto the Ising model
We introduced a quantum–classical hybrid scheme (HQA-MD, or HQA for short) which utilizes the molecular dynamics as a preconditioner for quantum annealing
Summary
A novel quantum–classical hybrid scheme is proposed to efficiently solve large-scale combinatorial optimization problems. They utilize adiabatic evolution of quantum bits (qubits) to find the ground state of Ising spin-glass models.
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