Bridging Quantum Mechanics and Computing: A Primer for Software Engineers
- URL: http://arxiv.org/abs/2502.16154v1
- Date: Sat, 22 Feb 2025 09:01:00 GMT
- Title: Bridging Quantum Mechanics and Computing: A Primer for Software Engineers
- Authors: Arvind W Kiwelekar,
- Abstract summary: As quantum technologies progress, software engineers must develop a conceptual understanding of quantum mechanics to grasp its implications for computing.<n>This article focuses on fundamental quantum mechanics principles for software engineers, including wave-particle duality, superposition, entanglement, quantum states, and quantum measurement.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum mechanics, the fundamental theory that governs the behaviour of matter and energy at microscopic scales, forms the foundation of quantum computing and quantum information science. As quantum technologies progress, software engineers must develop a conceptual understanding of quantum mechanics to grasp its implications for computing. This article focuses on fundamental quantum mechanics principles for software engineers, including wave-particle duality, superposition, entanglement, quantum states, and quantum measurement. Unlike traditional physics-oriented discussions, this article focuses on computational perspectives, assisting software professionals in bridging the gap between classical computing and emerging quantum paradigms.
Related papers
- A minimal Introduction to Quantum Computing [0.0]
We present an introduction to quantum computing tailored for computing professionals such as programmers, machine learning engineers, and data scientists.
Our approach abstracts away the physics underlying QC, and frames it as a model of computation similar to, for instance, Turing machines.
We introduce fundamental concepts such as basis states, quantum gates, and tensor products, illustrating how these form the building blocks of quantum computation.
arXiv Detail & Related papers (2025-04-01T17:33:28Z) - Quantum Computing in Transport Science: A Review [0.8437187555622164]
Quantum computing, leveraging the principles of quantum mechanics, has been found to significantly enhance computational capabilities in principle.
This paper explores quantum computing's potential to address complex, large-scale problems in transportation systems.
arXiv Detail & Related papers (2025-03-27T09:28:33Z) - Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning.
This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
arXiv Detail & Related papers (2024-11-14T12:27:50Z) - A Review of Quantum Scientific Computing Algorithms for Engineering Problems [0.0]
Quantum computing, leveraging quantum phenomena like superposition and entanglement, is emerging as a transformative force in computing technology.
This paper systematically explores the foundational concepts of quantum mechanics and their implications for computational advancements.
arXiv Detail & Related papers (2024-08-25T21:40:22Z) - 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 Machine Learning Implementations: Proposals and Experiments [0.0]
The article reviews specific high-impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors.
The field of quantum machine learning could be among the first quantum technologies producing results that are beneficial for industry and, in turn, to society.
arXiv Detail & Related papers (2023-03-11T01:02:16Z) - 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) - 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) - Standard Model Physics and the Digital Quantum Revolution: Thoughts
about the Interface [68.8204255655161]
Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress.
From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
arXiv Detail & Related papers (2021-07-10T06:12:06Z) - Quantum Computing: an undergraduate approach using Qiskit [0.0]
We present the Quantum Information Software Developer Kit - Qiskit, for teaching quantum computing to undergraduate students.
We focus on presenting the construction of the programs on any common laptop or desktop computer and their execution on real quantum processors.
The codes are made available throughout the text so that readers, even with little experience in scientific computing, can reproduce them.
arXiv Detail & Related papers (2021-01-26T18:19:23Z) - Selected topics of quantum computing for nuclear physics [0.24466725954625884]
Nuclear physics, whose underling theory is described by quantum gauge field coupled with matter, is fundamentally important.
Quantum computing provides a perhaps transformative approach for studying and understanding nuclear physics.
Digital quantum simulation approach for simulating quantum gauge fields and nuclear physics has gained lots of attentions.
arXiv Detail & Related papers (2020-11-03T02:35:18Z) - 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.