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Many important challenges in science and technology can be cast as optimization problems. When viewed in a statistical physics framework, these can be tackled by simulated annealing, where a gradual cooling procedure helps search for groundstate solutions of a target Hamiltonian. While powerful, simulated annealing is known to have prohibitively slow sampling dynamics when the optimization landscape is rough or glassy. Here we show that by generalizing the target distribution with a parameterized model, an analogous annealing framework based on the variational principle can be used to search for groundstate solutions. Modern autoregressive models such as recurrent neural networks provide ideal parameterizations since they can be exactly sampled without slow dynamics even when the model encodes a rough landscape. We implement this procedure in the classical and quantum settings on several prototypical spin glass Hamiltonians, and find that it significantly outperforms traditional simulated annealing in the asymptotic limit, illustrating the potential power of this yet unexplored route to optimization.

Bio: Estelle Inack is the first recipient of the Francis Allotey Fellowship, which honours the late distinguished Ghanaian mathematician, at the Perimeter Institute in Waterloo, Canada. She is working at the intersection of quantum computing and artificial intelligence at the Perimeter institute Quantum Intelligence Lab. Her research focuses in developing quantum-inspired algorithms to tackle real-world optimization problems using state-of-art machine learning techniques. Estelle obtained an MSc degree in Physics at the University of Buea (2013), a postgraduate diploma in Condensed Matter Physics at ICTP (2014) and a joint PhD degree in Statistical Physics from ICTP and SISSA (2018).