I contributed to an article titled “Researchers Explore Quantum-Inspired Optimization” in the December 2021 edition of the Pure AI web site. See https://pureai.com/articles/2021/12/01/quantum-inspired-optimization.aspx.
Quantum-inspired optimization starts with a standard algorithm, such as particle swarm optimization or simulated annealing, and modifies the algorithm by using one of many ideas adapted from physics quantum behavior. Quantum-inspired optimization (QIO) is not the same as quantum computing (QC). QIO uses standard hardware and software, as opposed to QC which uses exotic hardware and highly specialized software.
Quantum-inspired annealing incorporates the idea of quantum particle tunneling. Briefly, a quantum particle will usually transition to an adjacent state. But sometimes a quantum particle will jump to a non-adjacent state. Motivated by this phenomenon, one possible realization of quantum-inspired annealing is:
create an initial random guess solution set large starting temperature set current time loop many times with small probability based on current time create a tunneling non-adjacent candidate else create an adjacent candidate solution if candidate is better than current then replace the current solution with candidate else-if candidate is worse replace current anyway with probability based on current temperature end-if reduce temperature slightly end-loop return best solution found
I’m quoted in the article:
Dr. James McCaffrey from Microsoft Research works with combinatorial optimization and swarm optimization. McCaffrey commented, “Quantum-inspired optimization techniques have the potential to make a big impact in several fields.” He added, “Some of my colleagues and I believe that when more compute processing power becomes available, quantum-inspired optimization may provide significant breakthroughs for training deep neural networks with billions or trillions of weights.”