Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share
This work addresses the limitations of current quantum annealers in the NISQ era by exploring knowledge transfer, but it is incremental as it builds on existing reverse annealing methods without introducing a new paradigm.
The paper investigates whether reverse annealing, a quantum annealing technique for local refinement, can benefit from transferring knowledge between similar optimization problems, using the Knapsack Problem with 34 instances of 14 and 16 items as a benchmark.
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.