The Quantum Tortoise and the Classical Hare: A simple framework for
understanding which problems quantum computing will accelerate (and which it
will not)
- URL: http://arxiv.org/abs/2310.15505v1
- Date: Tue, 24 Oct 2023 04:20:10 GMT
- Title: The Quantum Tortoise and the Classical Hare: A simple framework for
understanding which problems quantum computing will accelerate (and which it
will not)
- Authors: Sukwoong Choi, William S. Moses, Neil Thompson
- Abstract summary: Quantum computing promises transformational gains for solving some problems, but little to none for others.
Our analysis reveals that many problems, particularly those of small to moderate size that can be important for typical businesses, will not benefit from quantum computing.
Since very large algorithmic gains are rare in practice and theorized to be rare even in principle, our analysis suggests that the benefits from quantum computing will flow either to users of these rare cases, or practitioners processing very large data.
- Score: 1.3812010983144802
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computing promises transformational gains for solving some problems,
but little to none for others. For anyone hoping to use quantum computers now
or in the future, it is important to know which problems will benefit. In this
paper, we introduce a framework for answering this question both intuitively
and quantitatively. The underlying structure of the framework is a race between
quantum and classical computers, where their relative strengths determine when
each wins. While classical computers operate faster, quantum computers can
sometimes run more efficient algorithms. Whether the speed advantage or the
algorithmic advantage dominates determines whether a problem will benefit from
quantum computing or not. Our analysis reveals that many problems, particularly
those of small to moderate size that can be important for typical businesses,
will not benefit from quantum computing. Conversely, larger problems or those
with particularly big algorithmic gains will benefit from near-term quantum
computing. Since very large algorithmic gains are rare in practice and
theorized to be rare even in principle, our analysis suggests that the benefits
from quantum computing will flow either to users of these rare cases, or
practitioners processing very large data.
Related papers
- Benefits of non-adiabatic quantum control in quantum computation through spin qubit systems [0.0]
controllable quantum systems can be reliable building blocks for Quantum computation.
In the future, we hope to see a full fledged operationally stable quantum computer.
arXiv Detail & Related papers (2024-03-17T17:48:51Z) - A Quantum Algorithm Based Heuristic to Hide Sensitive Itemsets [1.8419202109872088]
We present a quantum approach to solve a well-studied problem in the context of data sharing.
We present results on experiments involving small datasets to illustrate how the problem could be solved using quantum algorithms.
arXiv Detail & Related papers (2024-02-12T20:44:46Z) - Disentangling Hype from Practicality: On Realistically Achieving Quantum
Advantage [13.163255711706864]
We argue that small data problems and quantum algorithms with super-quadratic speedups are essential to make quantum computers useful in practice.
While most of the proposed quantum algorithms and applications do not achieve the necessary speedups to be considered practical, we already see a huge potential in material science and chemistry.
arXiv Detail & Related papers (2023-07-02T09:14:32Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - 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) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Efficient algorithms for quantum information bottleneck [64.67104066707309]
We propose a new and general algorithm for the quantum generalisation of information bottleneck.
Our algorithm excels in the speed and the definiteness of convergence compared with prior results.
Notably, we discover that a quantum system can achieve strictly better performance than a classical system of the same size regarding quantum information bottleneck.
arXiv Detail & Related papers (2022-08-22T14:20:05Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - Systematic Literature Review: Quantum Machine Learning and its
applications [0.0]
This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023.
This study identified 94 articles that used quantum machine learning techniques and algorithms.
An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.
arXiv Detail & Related papers (2022-01-11T17:36:34Z) - Multiple Query Optimization using a Hybrid Approach of Classical and
Quantum Computing [1.7077661158850292]
We tackle the multiple query optimization problem (MQO) which is an important NP-hard problem in the area of data-intensive problems.
We propose a novel hybrid classical-quantum algorithm to solve the MQO on a gate-based quantum computer.
Our algorithm shows a qubit efficiency of close to 99% which is almost a factor of 2 higher compared to the state of the art implementation.
arXiv Detail & Related papers (2021-07-22T08:12:49Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z)
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.