Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stochastic Hamiltonians

TL;DR

This paper demonstrates that adding multi-body transverse interactions to certain quantum Hamiltonians can transform phase transitions from first to second order, exponentially improving the efficiency of quantum annealing in solving optimization problems.

Contribution

It analytically shows how non-stoquastic Hamiltonians with multi-body interactions can exponentially enhance quantum annealing efficiency by altering phase transition orders.

Findings

Multi-body transverse interactions convert first-order to second-order phase transitions.

Second-order transitions lead to polynomially small energy gaps, improving annealing efficiency.

Analytical evidence of quantum effects enhancing optimization performance.

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.