Milestones on the Quantum Utility Highway

Abstract

We introduce quantum utility, a new approach to evaluating quantum performance that aims to capture the user experience by including overhead costs associated with the quantum computation. A demonstration of quantum utility by a quantum processing unit (QPU) shows that the QPU can outperform classical solvers at some tasks of interest to practitioners, when considering computational overheads. We consider overhead costs that arise in standalone use of the QPU (as opposed to a hybrid computation context). We define three early milestones on the path to broad-scale quantum utility that focus on restricted subsets of overheads: Milestone 0 considers pure anneal time (no overheads) and has been demonstrated in previous work; Milestone 1 includes overhead times to access the QPU (that is, programming and readout); and Milestone 2 incorporates an indirect cost associated with minor embedding.We evaluate the performance of a D-Wave Advantage QPU with respect to Milestones 1 and 2, using a testbed of 13 input classes and seven classical solvers implemented on CPUs and GPUs. For Milestone 1, the QPU outperformed all classical solvers in 99% of our tests. For Milestone 2, the QPU outperformed all classical solvers in 19% of our tests, and the scenarios in which the QPU found success correspond to cases where classical solvers most frequently failed.Analysis of test results on specific inputs reveals fundamentally distinct underlying mechanisms that explain the observed differences in quantum and classical performance profiles. We present evidence-based arguments that these distinctions bode well for future annealing quantum processors to support demonstrations of quantum utility on ever-expanding classes of inputs and for more challenging milestones.

Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

“…When all qubits representing a logical variable take the same value, the logical variable is assigned that value. All anneals are executed on the D-Wave Advantage system [26].…”

Benchmarking quantum annealing with maximum cardinality matching problems

“…(iii) Another aspect is the search for quantum speedup (Rønnow et al, 2014) and investigations of the performance of quantum processors in comparison to classical algorithms. Studies were performed for academic instances such as random spin glasses (Rønnow et al, 2014), specially crafted problems with or without planted solutions (Hen et al, 2015;King et al, 2015;Albash and Lidar, 2018;Vert et al, 2020;McLeod and Sasdelli, 2022), a variety of problems with different level of difficulty (Jünger et al, 2021;McGeoch and Farre, 2023) and problems with industrial application such as the multi-car paint shop problem (Yarkoni et al, 2021), job shop scheduling problem (Carugno et al, 2022), and Earth-observation satellite mission planning problem (Stollenwerk et al, 2021). Studies benchmarking QA against classical algorithms comprise annealing-like algorithms such as SA (Rønnow et al, 2014;Hen et al, 2015;King et al, 2015;Albash and Lidar, 2018;Vert et al, 2020;Yarkoni et al, 2021;Carugno et al, 2022;McLeod and Sasdelli, 2022;Ceselli and Premoli, 2023;McGeoch and Farre, 2023), parallel tempering (McGeoch and Farre, 2023), simulated QA and SVMC (Hen et al, 2015;Albash and Lidar, 2018), and heuristic algorithms such as Tabu search (McGeoch and Wang, 2013;Yarkoni et al, 2021;Carugno et al, 2022), Hamze-de Freitas-Selby algorithm (Hen et al, 2015;King et al, 2015), or greedy algorithms (Yarkoni et al, 2021;Carugno et al, 2022;McGeoch and Farre, 2023)...…”

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

“…These devices are called quantum annealers, and they combine a larger number of qubits, while being much noisier. Quantum annealers still have to prove their potential, that is claimed [12] by their creators. In [7] we introduced a connection between vector quantization and optimization problems, along other connections.…”