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

arXiv: https://arxiv.org/abs/2501.15865 · HTML: https://mlsanity.com/paper/2501.15865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results](https://mlsanity.com/paper/1706.03470/) (2017)
- [Reverse Annealing for Nonnegative/Binary Matrix Factorization](https://mlsanity.com/paper/2007.05565/) (2020)
- [D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis](https://mlsanity.com/paper/2410.07980/) (2024)
- [Bridging Classical and Quantum with SDP initialized warm-starts for QAOA](https://mlsanity.com/paper/2010.14021/) (2020)
- [Warm-Started QAOA with Custom Mixers Provably Converges and Computationally Beats Goemans-Williamson's Max-Cut at Low Circuit Depths](https://mlsanity.com/paper/2112.11354/) (2021)
- [Travel time optimization on multi-AGV routing by reverse annealing](https://mlsanity.com/paper/2204.11789/) (2022)
- [Adiabatic Quantum Optimization Fails to Solve the Knapsack Problem](https://mlsanity.com/paper/2008.07456/) (2020)
- [On the Transfer of Knowledge in Quantum Algorithms](https://mlsanity.com/paper/2501.14120/) (2025)

## Abstract (arXiv)

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.