A Framework for Quantum Advantage

• URL: http://arxiv.org/abs/2506.20658v2
• Date: Mon, 14 Jul 2025 18:34:04 GMT
• Title: A Framework for Quantum Advantage
• Authors: Olivia Lanes, Mourad Beji, Antonio D. Corcoles, Constantin Dalyac, Jay M. Gambetta, Loic Henriet, Ali Javadi-Abhari, Abhinav Kandala, Antonio Mezzacapo, Christopher Porter, Sarah Sheldon, John Watrous, Christa Zoufal, Alexandre Dauphin, Borja Peropadre,
• Abstract summary: We aim to articulate an operational definition for quantum advantage that is both platform-agnostic and empirically verifiable.<n>We outline our vision for the near future, in which quantum computers enhance existing high-performance computing platforms.
• Score: 30.07551633400823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As quantum computing approaches the threshold where certain tasks demonstrably outpace their classical machines, the need for a precise, clear, consensus-driven definition of quantum advantage becomes essential. Rapid progress in the field has blurred this term across companies, architectures, and application domains. Here, we aim to articulate an operational definition for quantum advantage that is both platform-agnostic and empirically verifiable. Building on this framework, we highlight the algorithmic families most likely to achieve early advantage. Finally, we outline our vision for the near future, in which quantum computers enhance existing high-performance computing platforms, enabling new frontiers in chemistry, materials discovery, optimization, and beyond.

Related papers

• Performance-centric roadmap for building a superconducting quantum computer [0.0]
  We identify four distinct phases for quantum hardware and enabling technology development.<n>The aim is to improve performance as we scale and increase the algorithmic complexity the quantum hardware is capable of running.
  arXiv  Detail & Related papers  (2025-06-29T10:25:50Z)
• Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications [59.0413662882849]
  Quantum computing is poised to redefine the algorithmic foundations of communication systems.<n>This article outlines the fundamentals of quantum computing in a style familiar to the communications society.<n>We highlight a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers.
  arXiv  Detail & Related papers  (2025-06-25T22:25:47Z)
• A Survey of Quantum Transformers: Architectures, Challenges and Outlooks [82.4736481748099]
  Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing.<n>Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications.<n>This paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models.
  arXiv  Detail & Related papers  (2025-04-04T05:40:18Z)
• Quantum Computing in Logistics and Supply Chain Management an Overview [0.0]
  The work explores the integration of quantum computing into logistics and supply chain management.<n>The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems.
  arXiv  Detail & Related papers  (2024-02-27T14:04:08Z)
• Quantum Generative Adversarial Networks: Bridging Classical and Quantum Realms [0.6827423171182153]
  We explore the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs) Our objective is to seamlessly integrate quantum computational elements into the conventional GAN architecture, thereby unlocking novel pathways for enhanced training processes. This research is positioned at the forefront of quantum-enhanced machine learning, presenting a critical stride towards harnessing the computational power of quantum systems.
  arXiv  Detail & Related papers  (2023-12-15T16:51:36Z)
• The QUATRO Application Suite: Quantum Computing for Models of Human Cognition [49.038807589598285]
  We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling. We release QUATRO, a collection of quantum computing applications from cognitive models.
  arXiv  Detail & Related papers  (2023-09-01T17:34:53Z)
• A Practitioner's Guide to Quantum Algorithms for Optimisation Problems [0.0]
  NP-hard optimisation problems are common in industrial areas such as logistics and finance. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques. It focuses on their near-term potential for noisy intermediate scale quantum devices.
  arXiv  Detail & Related papers  (2023-05-12T08:57:36Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Assessing requirements to scale to practical quantum advantage [56.22441723982983]
  We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications. We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
  arXiv  Detail & Related papers  (2022-11-14T18:50:27Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools [5.557009030881896]
  We conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing. We discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
  arXiv  Detail & Related papers  (2022-04-04T21:21:12Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)

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.