Polynomial unconstrained binary optimisation inspired by optical simulation

Abstract

We propose an algorithm inspired by optical coherent Ising machines to solve the problem of polynomial unconstrained binary optimisation (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. In particular, we successfully find the lowest-energy conformation of lattice protein molecules containing up to eleven amino-acids. The application of our algorithm to quantum chemistry sheds light on the shortcomings of approximating the electronic structure problem by a PUBO problem, which, in turn, puts into question the applicability of quantum annealers in this context.

Quantum and quantum-inspired optimization for solving the minimum bin packing problem

“…Several architectures of quantum computing devices, which are of interests for solving optimization problems, have been developed [3]. Specifically, quantum annealing devices based on superconducting qubits, which are able to solve problems of a non-trivial size [8], have been used to tackle various industryrelevant tasks, including quantum chemistry calculations [9,10], (lattice) protein folding [11], genome assembly [12,13], solving polynomial [14] and linear systems of equations [14], financial optimization [15,16,17,18,19], traffic optimization [20,21,22], scheduling [23,24,25], and many others (for a review, see Ref. [3]).…”

Quantum and quantum-inspired optimization for solving the minimum bin packing problem

“…Several architectures of quantum computing devices, which are of interests for solving optimization problems, have been developed [3]. Specifically, quantum annealing devices based on superconducting qubits, which are able to solve problems of a non-trivial size [8], have been used to tackle various industry-relevant tasks, including quantum chemistry calculations [9,10], (lattice) protein folding [11,12], genome assembly [13,14], solving polynomial [15] and linear systems of equations [15], financial optimization [16][17][18][19][20][21][22][23][24], traffic optimization [25][26][27], scheduling [28][29][30][31][32][33], railway conflict management [32,33], and many others (for a review, see Ref. [3]).…”