Learning adiabatic quantum algorithms for solving optimization problems

TL;DR

This paper introduces a hybrid quantum-classical approach for designing adiabatic quantum algorithms to solve optimization problems, focusing on learning problem Hamiltonians and ensuring convergence.

Contribution

It presents a novel iterative method to learn problem Hamiltonian encodings and demonstrates how to optimize adiabatic algorithms through classical-quantum integration.

Findings

Proposed a hybrid algorithm with proven convergence.

Technique for learning problem Hamiltonian encodings.

Algorithm can be used to derive efficient adiabatic algorithms from examples.

Abstract

An adiabatic quantum algorithm is essentially given by three elements: An initial Hamiltonian with known ground state, a problem Hamiltonian whose ground state corresponds to the solution of the given problem and an evolution schedule such that the adiabatic condition is satisfied. A correct choice of these elements is crucial for an efficient adiabatic quantum computation. In this paper we propose a hybrid quantum-classical algorithm to solve optimization problems with an adiabatic machine assuming restrictions on the class of available problem Hamiltonians. The scheme is based on repeated calls to the quantum machine into a classical iterative structure. In particular we present a technique to learn the encoding of a given optimization problem into a problem Hamiltonian and we prove the convergence of the algorithm. Moreover the output of the proposed algorithm can be used to learn efficient adiabatic algorithms from examples.