We define the quantum utility performance metric and three milestones, which define quantum utility as measured in some limited context. Current and previous-generation annealing quantum systems can outperform classical solvers on a variety of input classes, on Milestones 0 and 1; our tests of Milestone 2 show positive results on a small set of inputs. Characterization of the input properties that drive these outcomes suggests that future tests will yield more widespread successes on these and more challenging milestones.
We define the quantum utility performance metric and three milestones, which define quantum utility as measured in some limited context. Current and previous-generation annealing quantum systems can outperform classical solvers on a variety of input classes, on Milestones 0 and 1; our tests of Milestone 2 show positive results on a small set of inputs. Characterization of the input properties that drive these outcomes suggests that future tests will yield more widespread successes on these and more challenging milestones.
We introduce quantum utility , a new approach to evaluating quantum performance that aims to capture the user experience by considering the overhead costs associated with a quantum computation. A demonstration of quantum utility by the quantum processing unit (QPU) shows that the QPU can outperform classical solvers at some tasks of interest to practitioners, when considering the costs of computational overheads. A milestone is a test of quantum utility that is restricted to a specific subset of overhead costs and input types. We illustrate this approach with a benchmark study of a D-Wave annealing-based QPU versus seven classical solvers for a variety of problems in heuristic optimization. We consider overhead costs that arise in standalone use of the D-Wave QPU (as opposed to a hybrid computation). We define three early milestones on the path to broad-scale quantum utility. Milestone 0 is the purely quantum computation with no overhead costs and is demonstrated implicitly by positive results on other milestones. We evaluate the performance of a D-Wave Advantage QPU with respect to milestones 1 and 2: 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. This approach of isolating subsets of overheads for separate analysis reveals distinct mechanisms in quantum versus classical performance, which explain the observed differences in patterns of success and failure. We present evidence-based arguments that these distinctions bode well for annealing quantum processors to support demonstrations of quantum utility on ever-expanding classes of inputs and with more challenging milestones in the very near future.
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.
Quantum computing has progressed from theoretical speculation to experimental realization, leading to milestone demonstrations that claim computational superiority over classical systems. Among these milestones, quantum supremacy experiments have attracted considerable attention by showcasing tasks that are infeasible for classical computation within reasonable time limits. However, the practical value of such demonstrations remains a subject of debate. This paper critically evaluates the distinction between quantum supremacy and quantum utility, emphasizing their conceptual differences, experimental foundations, and real-world relevance. By examining key experimental results, algorithmic developments, and benchmarking approaches, the study argues that quantum utility-defined by practical, application-oriented performance-provides a more meaningful metric for long-term progress. The paper highlights the limitations of supremacy-based demonstrations and discusses pathways toward achieving utility-driven quantum advantage in the near and long term.
Building a useful quantum computer is a grand science and engineering challenge, currently pursued intensely by teams around the world. In the 1980s, Richard Feynman and Yuri Manin observed independently that computers based on quantum mechanics might enable better simulations of quantum phenomena. Their vision remained an intellectual curiosity until Peter Shor published his famous quantum algorithm for integer factoring, and shortly thereafter a proof that errors in quantum computations can be corrected. Since then, quantum computing R&D has progressed rapidly, from small-scale experiments in university physics laboratories to well-funded industrial efforts and prototypes. Hype notwithstanding, quantum computers have yet to solve scientifically or practically important problems -- a target often called quantum utility. In this article, we describe the capabilities of contemporary quantum computers, compare them to the requirements of quantum utility, and illustrate how to track progress from today to utility. We highlight key science and engineering challenges on the road to quantum utility, touching on relevant aspects of our own research.
We propose a practical, physics-inspired benchmarking suite to challenge both quantum and classical computers by mapping real-time quantum dynamics to a common optimization format. Using a parallel-in-time encoding, we convert the real-time propagator of an n -qubit, possibly non-Hermitian, Hamiltonian into quadratic unconstrained binary optimization (QUBO) instances that are executable in a solver-agnostic manner on quantum annealers and classical optimizers alike. This enables direct, like-for-like performance comparisons across fundamentally different computational paradigms. To stress-test the framework, we consider eight representative dynamical models spanning single-qubit rotations, multiqubit entangling gates (Bell, Greenberger-Horne-Zeilinger, cluster), and P T -symmetric and other non-Hermitian generators, and evaluate success probability and time to solution as standard benchmarking metrics. Applying this method to two generations of D-Wave quantum annealers and to state-of-the-art classical solvers (simulated annealing and the graphics-processing unit–accelerated VeloxQ), we find that Advantage2 consistently outperforms its predecessor, while VeloxQ retains the shortest absolute run-times, reflecting the maturity of classical heuristics. We further extend the benchmarks to large-scale instances ( N ≈ 10 5 ), establishing a demanding classical baseline for future hardware. Together, these results position the parallel-in-time QUBO framework as a versatile and physically motivated test bed for quantitatively tracking progress toward quantum-competitive simulation of dynamical systems.
Several benchmarks have been proposed to holistically measure quantum computing performance. While some have focused on the end user’s perspective (e.g., in application-oriented benchmarks), the real industrial value taking into account the physical footprint of the quantum processor are not discussed. Different use-cases come with different requirements for size, weight, power consumption, or data privacy while demanding to surpass certain thresholds of fidelity, speed, problem size, or precision. This paper aims to incorporate these characteristics into a concept coined quantum utility, which demonstrates the effectiveness and practicality of quantum computers for various applications where quantum advantage – defined as either being faster, more accurate, or demanding less energy – is achieved over a classical machine of similar size, weight, and cost. To successively pursue quantum utility, a level-based classification scheme – constituted as application readiness levels (ARLs) – as well as extended classification labels are introduced. These are demonstratively applied to different quantum applications from the fields of quantum chemistry, quantum simulation, quantum machine learning, and data analysis followed by a brief discussion.