Fair Benchmarking of Optimisation Applications

• URL: http://arxiv.org/abs/2512.07915v1
• Date: Mon, 08 Dec 2025 10:36:56 GMT
• Title: Fair Benchmarking of Optimisation Applications
• Abstract summary: Quantum optimisation is emerging as a promising approach alongside classicals and specialised hardware.<n>Traditional benchmarking methods do not directly capture the continuous dynamics, probabilistic outcomes, and workflow overheads of quantum and hybrid systems.<n>This paper proposes principles and protocols for fair benchmarking of quantum optimisation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum optimisation is emerging as a promising approach alongside classical heuristics and specialised hardware, yet its performance is often difficult to assess fairly. Traditional benchmarking methods, rooted in digital complexity theory, do not directly capture the continuous dynamics, probabilistic outcomes, and workflow overheads of quantum and hybrid systems. This paper proposes principles and protocols for fair benchmarking of quantum optimisation, emphasising end-to-end workflows, transparency in tuning and reporting, problem diversity, and avoidance of speculative claims. By extending lessons from classical benchmarking and incorporating application-driven and energy-aware metrics, we outline a framework that enables practitioners to evaluate quantum methods responsibly, ensuring reproducibility, comparability, and trust in reported results.

Related papers

• Learnable yet not simulable: a quantum resource theory of learning models [57.695822250391906]
  It remains unclear which quantum resource governs the classical learnability of quantum circuits.<n>We study the expectation-value functions of families of tunable quantum circuits.<n>These results establish a quantitative resource-theoretic framework for delineating the boundary between classical simulation and learning.
  arXiv  Detail & Related papers  (2026-08-03T14:48:03Z)
• Non-variational supervised quantum kernel methods: a review [4.483208369550461]
  Quantum kernel methods (QKMs) have emerged as a prominent framework for supervised quantum machine learning.<n>This review aims to clarify the regimes in which QKMs may offer genuine advantages, and to delineate the conceptual, methodological, and technical obstacles that must be overcome for practical quantum-enhanced learning.
  arXiv  Detail & Related papers  (2026-04-09T07:11:24Z)
• Methods for non-variational heuristic quantum optimisation [0.5586191108738564]
  We introduce a novel class of quantum optimisations that forgo this variational framework in favour of a hybrid quantum-classical approach.<n>These algorithms are expected to exhibit inherent robustness to noise and support parallel execution across both quantum and classical resources.
  arXiv  Detail & Related papers  (2026-02-01T17:46:57Z)
• Error-mitigation aware benchmarking strategy for quantum optimization problems [3.026585988755882]
  entropy benchmarking does not account for finite-shot effects or for quantum error mitigation.<n>We develop a benchmarking framework that explicitly incorporates finite-shot statistics and the resource overhead induced by QEM.<n>Our framework quantifies quantum advantage through the confidence that an estimated energy lies within an interval defined by the best-known classical upper and lower bounds.
  arXiv  Detail & Related papers  (2026-01-26T16:55:47Z)
• Quantum Computing -- Strategic Recommendations for the Industry [4.489537192567809]
  This paper synthesizes hardware trajectories from different quantum architectures and providers.<n>It assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework.<n>The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications.
  arXiv  Detail & Related papers  (2026-01-13T13:58:42Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Deep Unfolding: Recent Developments, Theory, and Design Guidelines [99.63555420898554]
  This article provides a tutorial-style overview of deep unfolding, a framework that transforms optimization algorithms into structured, trainable ML architectures.<n>We review the foundations of optimization for inference and for learning, introduce four representative design paradigms for deep unfolding, and discuss the distinctive training schemes that arise from their iterative nature.
  arXiv  Detail & Related papers  (2025-12-03T13:16:35Z)
• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
  We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Resource-Efficient Hadamard Test Circuits for Nonlinear Dynamics on a Trapped-Ion Quantum Computer [1.2063443893298391]
  We propose a low-depth implementation of a class of Hadamard test circuits.<n>We develop a parameterized quantum ansatz specifically tailored for variational algorithms.<n>Our findings demonstrate a significant reduction in single- and two-qubit gate counts.
  arXiv  Detail & Related papers  (2025-07-25T13:16:54Z)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• An Accurate and Efficient Analytic Model of Fidelity Under Depolarizing Noise Oriented to Large Scale Quantum System Design [1.80755313284025]
  We present a comprehensive theoretical framework to predict the fidelity of quantum circuits under depolarizing noise.<n>We propose an efficient fidelity estimation algorithm based on device calibration data.<n>The proposed approach provides a scalable and practical tool for benchmarking quantum hardware.
  arXiv  Detail & Related papers  (2025-03-09T16:59:24Z)
• Pseudo-Bayesian Optimization [7.556071491014536]
  We study an axiomatic framework that elicits the minimal requirements to guarantee black-box optimization convergence.<n>We show how using simple local regression, and a suitable "randomized prior" construction to quantify uncertainty, not only guarantees convergence but also consistently outperforms state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2023-10-15T07:55:28Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.