Identify, Transform, Benchmark, Show Quantum Advantage (ITBQ): Evaluating use cases for quantum computers. We introduce a four-step framework for assessing quantum computing applications -- from identifying relevant industry problems to demonstrating quantum advantage -- addressing steps often overlooked in the literature, such as rigorous benchmarking against classical solutions and the challenge of translating real-world tasks onto quantum hardware. Applying this framework to cases like NMR, multireference chemistry, and radicals reveals both significant opportunities and key barriers on the path to practical advantage. Our results highlight the need for transparent, structured criteria to focus research, guide investment, and accelerate meaningful quantum progress.
Identify, Transform, Benchmark, Show Quantum Advantage (ITBQ): Evaluating use cases for quantum computers. We introduce a four-step framework for assessing quantum computing applications -- from identifying relevant industry problems to demonstrating quantum advantage -- addressing steps often overlooked in the literature, such as rigorous benchmarking against classical solutions and the challenge of translating real-world tasks onto quantum hardware. Applying this framework to cases like NMR, multireference chemistry, and radicals reveals both significant opportunities and key barriers on the path to practical advantage. Our results highlight the need for transparent, structured criteria to focus research, guide investment, and accelerate meaningful quantum progress.
Quantum computing potentially enables the acceleration of key problems in science, engineering, and industry. However, between domain experts capable of supplying relevant use cases and quantum computing researchers possessing the expertise to implement novel solutions, there exists a 'know how-knowledge gap'. Therefore, methodologies need to be developed to identify quantum computing relevant use cases by bridging the divide and enabling co-development for near-term quantum hardware. We present a strategy using short-term, results oriented projects to quickly gauge the potential of proposed use cases to find opportunities for robust future quantum advantage.
This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior.
This compendium presents a structured collection of computational problems with credible prospects for realizing meaningful quantum advantage, focused on describing problem characteristics rather than specific algorithms. It is intended for non-quantum researchers and practitioners with strong computational backgrounds, particularly domain experts, seeking to assess the relevance of quantum methods to real-world problems. The compendium focuses on enumerating core problems for which there is good evidence that a quantum computer can realize more than a quadratic speedup in offline non-relativized time-complexity (e.g., as opposed to in oracular settings, streaming models, or communication advantages). Entries characterize the structural constraints that real-world instances must satisfy to realize those advantages in practice. The intention is to provide a reference that can be leveraged to connect quantum algorithms to practical use cases.
Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances in quantum computing toward practical applications, the need to analyze and compare different quantum solutions increases. While different low-level benchmarks for QC exist, these benchmarks do not provide sufficient insights into real-world application-level performance. We propose an application-centric benchmark method and the QUantum computing Application benchmaRK (QUARK) framework to foster the investigation and creation of application benchmarks for QC. This paper establishes three significant contributions: (1) it makes a case for application-level benchmarks and provides an in-depth "pen and paper" benchmark formulation of two reference problems: robot path and vehicle option optimization from the industrial domain; (2) it proposes the open-source QUARK framework for designing, implementing, executing, and analyzing benchmarks; (3) it provides multiple reference implementations for these two reference problems based on different known, and where needed, extended, classical and quantum algorithmic approaches and analyzes their performance on different types of infrastructures.
Quantum computing has recently achieved experimental demonstrations of quantum advantage, marking a major milestone toward beyond-classical computation. However, translating these demonstrations into practical utility remains a central challenge. In this perspective, we argue that this transition is fundamentally limited by three interconnected gaps: the utility gap between benchmark tasks and real-world applications, the algorithmic gap between asymptotic quantum speedups and realistic performance, and the hardware gap imposed by noisy intermediate-scale devices. We further highlight that recent progress is beginning to narrow these gaps through the rapid maturation of quantum hardware, the emergence of resource-aware and practically grounded quantum algorithms, and the increasing integration of classical computation into the quantum workflow. Finally, we discuss potential early application domains for practical quantum advantage, while emphasizing that its ultimate realization will likely emerge from the co-evolution of hardware, algorithms, and application-driven discovery rather than any single breakthrough.
Quantum computers have the potential to provide an advantage over classical computers in a number of areas. Numerous metrics to benchmark the performance of quantum computers, ranging from their individual hardware components to entire applications, have been proposed over the years. Objective comparisons are further hampered in practice as different variations of the same metric are used, and the data disclosed together with a reported metric value is often not sufficient to reproduce the measurements. In this poster, we present our recent article, where we address these challenges by providing a review of metrics and benchmarks for quantum computers [1], including 1) a comprehensive collection of benchmarks allowing holistic comparisons of quantum computers, 2) a consistent format of the definitions across all metrics including a transparent description of the methodology and of the main assumptions and limitations, and 3) a reproducible approach by linking the metrics to open-source software used to evaluate them . We identify five areas where international standardization working groups could be established, namely: i) the identification and agreement on the categories of metrics that comprehensively benchmark device performance; ii) the identification and agreement on a set of well-established metrics that together comprehensively benchmark performance; iii) the identification of metrics specific to hardware platforms, including non-gate-based quantum computers; iv) inter-laboratory comparison studies to develop best practice guides for measurement methodology; and v) agreement on what data and software should be reported together with a metric value to ensure trust, transparency and reproducibility. We provide potential routes to advancing these areas. We expect that this work will accelerate the progress of quantum computing hardware towards quantum advantage. [1] D. Lall et al., A Review and Collection of Metrics and Benchmarks for Quantum Computers: Definitions, Methodologies and Software, arXiv:2502.06717.