The Road to Hybrid Quantum Programs: Characterizing the Evolution from Classical to Hybrid Quantum Software
- URL: http://arxiv.org/abs/2503.11450v3
- Date: Fri, 25 Apr 2025 10:21:43 GMT
- Title: The Road to Hybrid Quantum Programs: Characterizing the Evolution from Classical to Hybrid Quantum Software
- Authors: Vincenzo De Maio, Ivona Brandic, Ewa Deelman, Jürgen Cito,
- Abstract summary: Efforts to identify quantum candidate code fragments that can meaningfully execute on quantum machines primarily rely on static code analysis.<n>This paper aims to systematically formalize the process of identifying quantum candidates and their proper encoding within classical programs.
- Score: 3.1240846678117546
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. However, not all computational tasks can be efficiently executed on quantum machines, giving rise to hybrid systems, where some portions of an application run on classical machines, while others utilize quantum resources. Efforts to identify quantum candidate code fragments that can meaningfully execute on quantum machines primarily rely on static code analysis. Yet, the state-of-the-art in static code analysis for quantum candidates remains in its infancy, with limited applicability to specific frameworks and languages, and a lack of generalizability. Existing methods often involve a trial-and-error approach, relying on the intuition and expertise of computer scientists, resulting in varying identification durations ranging from minutes to days for a single application. This paper aims to systematically formalize the process of identifying quantum candidates and their proper encoding within classical programs. Our work addresses the critical initial step in the development of automated reasoning techniques for code-to-code translation, laying the foundation for more efficient quantum software engineering. Particularly, this study investigates a sociotechnical phenomenon where the starting point is not a problem directly solvable with QC, but rather an existing classical program that addresses the problem. In doing so, it underscores the interdisciplinary nature of QC application development, necessitating collaboration between domain experts, computer scientists, and physicists to harness the potential of quantum computing effectively.
Related papers
- Quantum Computer Does Not Need Coherent Quantum Access for Advantage [0.0]
A majority of quantum speedups rely on a subroutine in which classical information can be accessed in a coherent quantum manner.<n>We develop a quantum gradient descent algorithm for optimization, which is a fundamental technique that enjoys a wide range of applications.
arXiv Detail & Related papers (2025-03-04T11:24:28Z) - LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder [7.945302052915863]
A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data.
We propose LatentQGAN, a novel quantum model that uses a hybrid quantum-classical GAN coupled with an autoencoder.
arXiv Detail & Related papers (2024-09-22T23:18:06Z) - State-Averaged Orbital-Optimized VQE: A quantum algorithm for the
democratic description of ground and excited electronic states [0.0]
The SA-OO-VQE package aims to answer both problems with its hybrid quantum-classical conception based on a typical Variational Quantum Eigensolver approach.
The SA-OO-VQE has the ability to treat degenerate (or quasi-degenerate) states on the same footing, thus avoiding known numerical optimization problems around avoided crossings or conical intersections.
arXiv Detail & Related papers (2024-01-22T12:16:37Z) - Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
The anticipated applications of quantum computers span across science and industry.
We present a survey of several potential application areas of quantum algorithms.
We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - 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) - 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) - Variational Quantum Algorithms for Computational Fluid Dynamics [0.0]
Variational quantum algorithms are particularly promising since they are comparatively noise tolerant.
We show how variational quantum algorithms can be utilized in computational fluid dynamics.
We argue that a quantum advantage over classical computing methods could be achieved by the end of this decade.
arXiv Detail & Related papers (2022-09-11T18:49:22Z) - 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) - 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) - Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems.
We propose an algorithm to implement imaginary time propagation on a quantum computer.
arXiv Detail & Related papers (2021-02-24T12:48:00Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - 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.