Benchmarking fault-tolerant quantum computing hardware via QLOPS

• URL: http://arxiv.org/abs/2507.12024v1
• Date: Wed, 16 Jul 2025 08:31:51 GMT
• Title: Benchmarking fault-tolerant quantum computing hardware via QLOPS
• Authors: Linghang Kong, Fang Zhang, Jianxin Chen,
• Abstract summary: To run quantum algorithms, it is essential to develop scalable quantum hardware with low noise levels.<n>Various fault-tolerant quantum computing schemes have been developed for different hardware platforms.<n>We propose Quantum Logical Operations Per Second (QLOPS) as a metric for assessing the performance of FTQC schemes.
• Score: 2.0464713282534848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is widely recognized that quantum computing has profound impacts on multiple fields, including but not limited to cryptography, machine learning, materials science, etc. To run quantum algorithms, it is essential to develop scalable quantum hardware with low noise levels and to design efficient fault-tolerant quantum computing (FTQC) schemes. Currently, various FTQC schemes have been developed for different hardware platforms. However, a comprehensive framework for the analysis and evaluation of these schemes is still lacking. In this work, we propose Quantum Logical Operations Per Second (QLOPS) as a metric for assessing the performance of FTQC schemes on quantum hardware platforms. This benchmarking framework will integrate essential relevant factors, e.g., the code rates of quantum error-correcting codes, the accuracy, throughput, and latency of the decoder, and reflect the practical requirements of quantum algorithm execution. This framework will enable the identification of bottlenecks in quantum hardware, providing potential directions for their development. Moreover, our results will help establish a comparative framework for evaluating FTQC designs. As this benchmarking approach considers practical applications, it may assist in estimating the hardware resources needed to implement quantum algorithms and offers preliminary insights into potential timelines.

Related papers

• Advances in Machine Learning: Where Can Quantum Techniques Help? [0.0]
  Quantum Machine Learning (QML) represents a promising frontier at the intersection of quantum computing and artificial intelligence.<n>This review explores the potential of QML to address the computational bottlenecks of classical machine learning.
  arXiv  Detail & Related papers  (2025-07-11T07:47:47Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits.<n>Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• 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)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QuBEC: Boosting Equivalence Checking for Quantum Circuits with QEC Embedding [4.15692939468851]
  We propose a Decision Diagram-based quantum equivalence checking approach, QuBEC, that requires less latency compared to existing techniques. Our proposed methodology reduces verification time on certain benchmark circuits by up to $271.49 times$.
  arXiv  Detail & Related papers  (2023-09-19T16:12:37Z)
• 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)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• 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)
• Quantum Volume for Photonic Quantum Processors [15.3862808585761]
  Defining metrics for near-term quantum computing processors has been an integral part of the quantum hardware research and development efforts. Most metrics such as randomized benchmarking and quantum volume were originally introduced for circuit-based quantum computers. We present a framework to map physical noises and imperfections in MBQC processes to logical errors in equivalent quantum circuits.
  arXiv  Detail & Related papers  (2022-08-24T18:05:16Z)
• Full-stack quantum computing systems in the NISQ era: algorithm-driven and hardware-aware compilation techniques [1.3496450124792878]
  We will provide an overview on current full-stack quantum computing systems. We will emphasize the need for tight co-design among adjacent layers as well as vertical cross-layer design.
  arXiv  Detail & Related papers  (2022-04-13T13:26:56Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.