Quantum annealing: near future applications

Thomas Plehn, Katrin Bugelnig, Silvana Tumminello +2 more

Quantum annealing has the potential to provide practical quantum advantage for complex optimization tasks. Here, we present a systematic assessment of an integrated factorization-machine and quantum-annealing workflow (FM+QA) for a technologically relevant application: multi-objective Pareto optimization in metal up-cycling through alloy design. To address the non-convex nature of the Pareto front, we employ the recently proposed data-driven Tchebycheff scalarization (DDTS) scheme. Our results show that FM+QA extends the applicability of QUBO-based optimization to data-driven materials discovery problems with multiple competing objectives. In particular, we analyze the scaling behavior of the approach and compare quantum annealing with classical simulated annealing using both regular binary encoding and one-hot encoding. Finally, we provide a critical perspective on the problem sizes and encoding strategies for which quantum-annealing-based optimization may become practically beneficial in the near future.

真之 大関, Masayuki Ohzeki, 秀稔 西森 +2 more

Marco Donald Migliore

The objective of this paper is to introduce the application of quantum annealing (QA) to electromagnetic (EM) engineering. We demonstrate that numerous EM design and inverse problems naturally admit a mathematical formulation in terms of binary quadratic interactions. By utilizing didactic, simplified examples, we illustrate how this underlying physical structure allows for a direct mapping of EM problems onto Quadratic Unconstrained Binary Optimization (QUBO) models and equivalent Ising Hamiltonians. All numerical experiments are conducted using Simulated Annealing as a classical proxy, since the primary contribution of this work is the QUBO formulation itself rather than its execution on quantum hardware. Our results suggest that a broad class of EM problems—including array thinning, reconfigurable intelligent surface optimization, subarray partitioning, and electromagnetic inverse scattering—are suitable candidates for optimization using near-future quantum annealing architectures, once hardware connectivity and noise floors reach the required specifications.

Sei Suzuki

G. Aeppli, T. F. Rosenbaum

Juan Carlos Criado

Abstract The quantum annealing framework provides a general technique for the solution of a wide variety of optimization problems. Recent technological developments have made it possible to build quantum annealing devices with thousands of qubits, bringing them within range to tackle real-world problems in the near future. In this paper, I review this framework, and present a method for its use in the minimization of functions of continuous variables. Then, I discuss how this method can be employed to solve optimization problems with applications in high-energy physics, including training neural networks for classification, solving partial differential equations, and fitting effective field theories to experimental data.