Readiness of Quantum Optimization Machines for Industrial Applications

Abstract

There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current classical optimization algorithms implemented on CMOS technologies. The benchmarking of these devices has been controversial. Initially, random spin-glass problems were used, however, these were quickly shown to be not well suited to detect any quantum speedup. Subsequently, benchmarking shifted to carefully crafted synthetic problems designed to highlight the quantum nature of the hardware while (often) ensuring that classical optimization techniques do not perform well on them. Even worse, to date a true sign of improved scaling with the number of problem variables remains elusive when compared to classical optimization techniques. Here, we analyze the readiness of quantum annealing machines for real-world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimization techniques, both classical and quantum alike. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers. We find that the instances generated from real data in multiplier circuits are harder than other representative random spin-glass benchmarks with a comparable number of variables. Although our results show that transverse-field quantum annealing is outperformed by state-of-the-art classical optimization algorithms, these benchmark instances are hard and small in the size of the input, therefore representing the first industrial application ideally suited for testing near-term quantum annealers and other quantum algorithmic strategies for optimization problems.

Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices

“…III. We would like to stress that the algorithm can be carried out on other quantum annealing architectures [48,49], and on more general gate-based quantum computers. Implementations in these architectures may require further, or fewer, engineering steps, and could allow more general quantum distributions.…”

How Small-World Interactions Can Lead to Improved Quantum Annealer Designs

“…Recently, multiple types of synthetic benchmark problems have emerged [14][15][16][17][18] in an effort to demonstrate that D-Wave quantum annealing machines can outperform algorithms on classical CMOS hardware. Although to date only a verifi-able constant speedup has been found [19] and applications have yielded mixed results [20][21][22][23][24][25], much effort is still be expended to elucidate the application scope of the D-Wave device. While synthetic benchmarks designed to "break" classical algorithms are excellent benchmark problems, their mostprominent drawback is the need to encode a hard logical problem into the physical hardware layout of the quantum annealing processing unit (QAPU).…”

Finding Maximum Cliques on the D-Wave Quantum Annealer

“…In Thulasidasan (2016), the author proposes to run Markov Chain Monte Carlo using samples generated by DW from a suitable Boltzmann distribution. The question whether random spin-glass problems are a suitable type of problem to detect a quantum advantage over classical approaches is considered in Perdomo-Ortiz et al (2017), who also study the problem of benchmarking quantum annealing vs. classical CMOS computation.…”