Quantum states and quantum computing

• URL: http://arxiv.org/abs/2409.15285v1
• Date: Tue, 3 Sep 2024 14:23:50 GMT
• Title: Quantum states and quantum computing
• Authors: Mohammad Vahid Takook, Ali Mohammad-Djafari,
• Abstract summary: In quantum theory, a quantum state $vert alpha,trangle$ is situated in an evolving within Hilbert space, portraying the system's reality with inherent uncertainty. This article aims to elucidate the fundamental concepts of quantum field theory and their interconnections with quantum computing.
• Score: 1.104960878651584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In classical theory, the physical systems are elucidated through the concepts of particles and waves, which aim to describe the reality of the physical system with certainty. In this framework, particles are mathematically represented by position vectors as functions of time, $\vec{x}(t)$, while waves are modeled by tensor fields in space-time, $\Phi(t, \vec{x})$. These functions are embedded in, and evolve within space-time. All information about the physical system are coded in these mathematical functions, upon which the classical technologies are developed. In contrast, quantum theory models the physical system using a quantum state $\vert \alpha ,t\rangle$, situated in an evolving within Hilbert space, portraying the system's reality with inherent uncertainty. Despite the probabilistic nature of reality observation, the quantum state $\vert \alpha ,t\rangle$ can be precisely determined due to the unitary principle, provided we know the initial state. Therefore, it can serve as a foundation for developing quantum technologies, which we call quantum state-tronics similar to electronics. This discussion focuses on quantum computation, given its expansive scope. One of the paramount challenges in quantum computing is the scarcity of individuals equipped with the requisite knowledge of quantum field theory and the training necessary for this field. This article aims to elucidate the fundamental concepts of quantum field theory and their interconnections with quantum computing, striving to simplify them for those engaged in quantum computing.

Related papers

• To be or not to be, but where? [0.0]
  Traditional approaches associate quantum systems with classical ones localized in spacetime. canonical linearized quantum gravity disrupts this framework by preventing the formation of gauge-in-variant local algebras. This presents a major obstacle for modeling early universe cosmology, gravity-entanglement experiments, and poses a significant roadblock toward a comprehensive theory of quantum gravity.
  arXiv  Detail & Related papers  (2024-05-31T17:22:39Z)
• Atomicity in Distributed Quantum Computing [7.042810171786408]
  Atomicity is a ubiquitous assumption in distributed computing, under which actions are indivisible and appear sequential. In quantum computing, although atomicity is still commonly assumed, it has not been seriously studied.
  arXiv  Detail & Related papers  (2024-04-29T11:04:43Z)
• 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: 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)
• On quantum neural networks [91.3755431537592]
  We argue that the concept of a quantum neural network should be defined in terms of its most general function. Our reasoning is based on the use of the Feynman path integral formulation in quantum mechanics.
  arXiv  Detail & Related papers  (2021-04-12T18:30:30Z)
• 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)
• Quantum Computation [0.0]
  We will discuss and summarized the core principles and practical application areas of quantum computation. The mapping of computation onto the behavior of physical systems is a historical challenge. We will evaluate the essential technology required for quantum computers to be able to function correctly.
  arXiv  Detail & Related papers  (2020-06-04T11:57:18Z)
• Quantum algorithms for quantum chemistry and quantum materials science [2.867517731896504]
  We briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics, that are of potential interest for solution on a quantum computer. We take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal state simulation, and analyze their strengths and weaknesses for future developments.
  arXiv  Detail & Related papers  (2020-01-10T22:49:56Z)

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.