Exponential enhancement of the efficiency of quantum annealing by non-stoquastic Hamiltonians

Hidetoshi Nishimori, Kabuki Takada

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Non-stoquastic Hamiltonians have both positive and negative signs in off-diagonal elements in their matrix representation in the standard computational basis and thus cannot be simulated efficiently by the standard quantum Monte Carlo method due to the sign problem. We describe our analytical studies of this type of Hamiltonians with infinite-range non-random as well as random interactions from the perspective of possible enhancement of the efficiency of quantum annealing or adiabatic quantum computing. It is shown that multi-body transverse interactions like XX and XXXXX with positive coefficients appended to a stoquastic transverse-field Ising model render the Hamiltonian non-stoquastic and reduce a first-order quantum phase transition in the simple transverse-field case to a second-order transition. This implies that the efficiency of quantum annealing is exponentially enhanced, because a first-order transition has an exponentially small energy gap (and therefore exponentially long computation time) whereas a second-order transition has a polynomially decaying gap (polynomial computation time). The examples presented here represent rare instances where strong quantum effects, in the sense that they cannot be efficiently simulated in the standard quantum Monte Carlo, have analytically been shown to exponentially enhance the efficiency of quantum annealing for combinatorial optimization problems.

Original languageEnglish
Article number2
JournalFrontiers in ICT
Volume4
Issue numberFEB
DOIs
Publication statusPublished - 2017 Jan 1
Externally publishedYes

Keywords

  • Exponential speedup
  • Non-stoquastic Hamiltonian
  • Quantum adiabatic algorithm
  • Quantum annealing
  • Stoquastic Hamiltonian

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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