15:00 - 16:00
Quantum optical Ising machines and their simulators for annealing and machine learning
The problem of annealing (finding low-energy states of strongly correlated systems) has a wide range of applications, from portfolio optimisation to machine learning and materials science. Quantum technology can help solving this problem more efficiently. While fully fault-tolerant quantum computing is still under development, some progress has been associated with noisy intermediate-scale quantum systems, such as the D-Wave quantum annealer and the optical coherent Ising machine. I will discuss the capabilities and shortcomings of these approaches and then present a new classical annealing algorithm, which is based on simulating the optical machine, and appears advantageous with respect to both physical methods. I will also show how this algorithm can be applied to the training of Boltzmann machines. Co-sponsored by ARC (Advanced Research Computing, https://www.dur.ac.uk/arc/).