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

2025-01-27

AI Summary

This study investigates whether knowledge transfer via reverse annealing can enhance quantum optimization performance in the NISQ era. Focusing on similar knapsack instances (34 cases across 14- and 16-item variants), it presents the first systematic empirical validation—on a D-Wave quantum annealer—of reusing high-quality prior solutions. The methodology integrates classical presolving, solution quality assessment, and statistical significance testing to identify two critical features of input solutions—energy neighborhood quality and structural consistency—that govern transfer success. Experiments demonstrate that well-matched prior solutions increase the probability of sampling optimal solutions by up to 37%. The work establishes both the feasibility and mechanistic underpinnings of knowledge transfer in reverse annealing, and proposes generalizable, feature-based criteria for solution suitability. These findings introduce a novel paradigm for prior-informed quantum optimization in NISQ devices.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction
• Machine Learning: Quantum Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Reverse Annealing
• Quantum Computing
• NISQ Era

Innovation

• Quantum Annealing
• Backpack Problem
• NISQ Devices