Search

To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery due to absence of compatible optimization mapping methods. Here we show that by combining cluster expansion with a quantum-inspired superposition technique, we can lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties order 10-50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. Levering this, we search chemical space for discovery of acidic oxygen evolution reaction (OER) catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O$_2$. The best catalyst in this chemical family show a mass activity 8 times higher than state-of-art RuO$_2$ and maintain performance for 180 hours while operating at 10mA/cm$^2$ in acidic 0.5 M $H_2SO_4$ electrolyte.

We propose an algorithm inspired by optical coherent Ising machines to solve the problem of polynomial unconstrained binary optimization (PUBO). We benchmark the proposed algorithm against existing PUBO algorithms on the extended Sherrington-Kirkpatrick model and random third-degree polynomial pseudo-Boolean functions, and observe its superior performance. We also address instances of practically relevant computational problems such as protein folding and electronic structure calculations with problem sizes not accessible to existing quantum annealing devices. The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approximating the electronic structure problem by a PUBO problem, which, in turn, puts into question the applicability of the unconstrained binary optimization formulation, such as that of quantum annealers and coherent Ising machines, in this context., Comment: 13 pages, 8 figures