Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share

TL;DR

This paper explores the potential of reverse annealing in quantum computing for transferring knowledge between similar problems, using the Knapsack Problem as a benchmark to identify conditions that improve success probability.

Contribution

It provides the first experimental analysis of knowledge transfer benefits in reverse annealing for quantum optimization problems.

Findings

Reverse annealing can benefit from knowledge transfer between similar problems.

Certain input solution characteristics increase success probability.

Experimental results on Knapsack instances demonstrate potential advantages.

Abstract

Being immersed in the NISQ-era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed a mechanism called Reverse Annealing, a specific type of quantum annealing designed to perform local refinement of good states found elsewhere. Despite the research activity around Reverse Annealing, none has theorized about the possible benefits related to the transfer of knowledge under this paradigm. This work moves in that direction and is driven by experimentation focused on answering two key research questions: i) is reverse annealing a paradigm that can benefit from knowledge transfer between similar problems? and ii) can we infer the characteristics that an input solution should meet to help increase the probability of success? To properly guide the tests in this paper, the well-known Knapsack Problem has been chosen for benchmarking purposes, using a total of 34 instances composed of 14 and 16 items.