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

Eneko Osaba, Esther Villar-Rodriguez

TL;DR

This paper investigates whether knowledge transfer can enhance Reverse Annealing (RA) in quantum annealing, using the Knapsack Problem as a benchmark. By creating parent and descendant KP instances and evaluating RA with source solutions from similar tasks, it assesses the impact on solution quality and robustness. The findings show that knowledge transfer via RA can improve reliability, especially for harder instances, but there is no universal pattern; importantly, RA performance correlates more with the Hamming-distance similarity of encodings than with energy proximity. These results lay groundwork for transfer learning in quantum annealing and suggest future work on scalability, scheduling, and cross-problem transfer, with publicly available data to enable replication and extension.

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.

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

TL;DR

This paper investigates whether knowledge transfer can enhance Reverse Annealing (RA) in quantum annealing, using the Knapsack Problem as a benchmark. By creating parent and descendant KP instances and evaluating RA with source solutions from similar tasks, it assesses the impact on solution quality and robustness. The findings show that knowledge transfer via RA can improve reliability, especially for harder instances, but there is no universal pattern; importantly, RA performance correlates more with the Hamming-distance similarity of encodings than with energy proximity. These results lay groundwork for transfer learning in quantum annealing and suggest future work on scalability, scheduling, and cross-problem transfer, with publicly available data to enable replication and extension.

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.
Paper Structure (6 sections, 2 equations, 2 figures, 2 tables)

This paper contains 6 sections, 2 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Experimentation & Discussion
  4. RQ1: Is RA a paradigm that can benefit from knowledge transfer between problem instances with similar characteristics?
  5. RQ2: Can we infer the characteristics that an input solution should meet to help increase the probability of succeeding in a RA process?
  6. Conclusions & Further Work

Figures (2)

  • Figure 1: Results related to the resolution of s14 using as input the best solution found for each descendant-instance over 10 independent runs. The blue colored boxplots represent the baseline results obtained through forward annealing. The less the energy value, the better the solution.
  • Figure 2: Results related to the resolution of s16 using as input the best solution found for each descendant-instance over 10 independent runs. The blue colored boxplots represent the baseline results obtained through forward annealing. The less the energy value, the better the solution.