Quantum utility -- definition and assessment of a practical quantum
advantage
- URL: http://arxiv.org/abs/2303.02138v2
- Date: Fri, 2 Jun 2023 09:25:05 GMT
- Title: Quantum utility -- definition and assessment of a practical quantum
advantage
- Authors: Nils Herrmann, Daanish Arya, Marcus W. Doherty, Angus Mingare, Jason
C. Pillay, Florian Preis, Stefan Prestel
- Abstract summary: Different use-cases come with different requirements for size, weight, power consumption, or data privacy.
This paper aims to incorporate these characteristics into a concept coined quantum utility.
It demonstrates the effectiveness and practicality of quantum computers for various applications.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - Quantum Algorithms and Applications for Open Quantum Systems [1.7717834336854132]
We provide a succinct summary of the fundamental theory of open quantum systems.
We then delve into a discussion on recent quantum algorithms.
We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.
arXiv Detail & Related papers (2024-06-07T19:02:22Z) - QuAS: Quantum Application Score for benchmarking the utility of quantum computers [0.0]
This paper presents a revised holistic scoring method called the Quantum Application Score (QuAS)
We discuss how to integrate both and thereby obtain an application-level metric that better quantifies the practical utility of quantum computers.
We evaluate the new metric on different hardware platforms such as D-Wave and IBM as well as quantum simulators of Quantum Inspire and Rigetti.
arXiv Detail & Related papers (2024-06-06T09:39:58Z) - Review on Quantum Walk Computing: Theory, Implementation, and Application [0.30723404270319693]
The quantum walk is proposed as an important theoretical model for quantum computing.
Quantum walks and their variety have been extensively studied for achieving beyond classical computing power.
Recent progress has been achieved in implementing a wide variety of quantum walks and quantum walk applications.
arXiv Detail & Related papers (2024-04-05T15:45:35Z) - 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) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Quantum information processing with superconducting circuits: a
perspective [0.0]
Key issues involve how to achieve quantum advantage in useful applications for quantum optimization and materials science.
Recent work on applications of variational quantum algorithms for optimization and electronic structure determination.
Current work and ideas about how to scale up to competitive quantum systems.
arXiv Detail & Related papers (2023-02-09T10:49:56Z) - 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) - Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems.
Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
arXiv Detail & Related papers (2021-08-30T23:50:05Z) - Fisher Information in Noisy Intermediate-Scale Quantum Applications [0.0]
The classical and quantum Fisher information are rooted in the field of quantum sensing.
Their utility in the study of other applications of noisy intermediate-scale quantum devices has only been discovered recently.
This article aims to further popularize classical and quantum Fisher information as useful tools for near-term applications beyond quantum sensing.
arXiv Detail & Related papers (2021-03-28T18:11:15Z)
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.