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Quantum Computing to Enhance Performance of Machine Learning Algorithms
Published in B. K. Mishra, Samarjeet Borah, Hemant Kasturiwale, Computing and Communications Engineering in Real-Time Application Development, 2023
Shiwani Gupta, Namrata D. Deshmukh
Quantum computing (QC) is defined as the computing using the concepts of quantum mechanics, such as superposition and entanglement. The QC is study of the nonclassical computational model. In the early 1980s, study of QC began; that time a mechanical quantum model of the Turing machine was proposed by physicist Paul Benioff. It is suggested by Richard Feynman and Yuri Manin that a quantum computer had the potential to imitate things that could not be done by a simple classical computer.1 In order to do improvements in secured communications, a quantum algorithm was developed by Peter Shor in 1994, which included factorization of integers that had the ability for decryption of guarded communications. Despite current experimental work progressing since1990s, most of the researchers feel that “fault-tolerant QC is still a dream.” Google AI published a paper on 23rd October 2019 where they claimed that they have achieved quantum supremacy and this work was in partnership with NASA.2 Some still debate this claim saying that it is still a big landmark in the history of QC.
Overview of Bohmian Mechanics
Published in Xavier Oriols, Jordi Mompart, Applied Bohmian Mechanics, 2019
The quantum potential is at the origin of all quantum correlations, that is, entanglement, that can imply (nonlocal) faster-than-light interactions when two distant particles are involved. As mentioned in Section 1.1.5, this “spooky action at a distance” is what bothered Einstein about Bohmian mechanics (and quantum mechanics, in general). In 1964, Bell elaborated his famous theorem that established clear experimentally testable mathematical inequalities that would be fulfilled by local theories but would be violated by nonlocal ones [36]. All experimental results obtained so far confirm that Bell’s inequalities are violated. Therefore, contrarily to Einstein’s belief, we have to accept the real existence, in nature, of faster-than-light causation.a Entanglement is an intrinsic correlation in quantum mechanics (whose complexity and potentialities eventually come from the fact that a N-particle wave function lives in a N-dimensional configuration space) and is at the core of quantum information science, which makes teleportation, quantum communication, quantum cryptography, and quantum computing possible.
Physically Defined Coupled Silicon Quantum Dots Containing a Few Electrons for Electron Spin Qubits
Published in Simon Deleonibus, Emerging Devices for Low-Power and High-Performance Nanosystems, 2018
Tetsuo Kodera, Kosuke Horibe, Shunri Oda
A bit in quantum computing, termed a “quantum bit” (qubit), is realized by the superposition of two-level quantum states. Quantum computing requires two well-defined and controllable states to qualify a qubit, which possesses the stability and ability to construct a quantum logic gate. So far, various two-level systems have been investigated theoretically and experimentally to build qubits, such as NMR on molecules in solution [2], trapped ions, and linear optical quantum effects [17]. However, for the purpose of large-scale integration, qubits based on solid-state quantum systems are more promising. In particular, charges or spins of electrons, confined in semiconductor QDs, have attracted attention as candidate state variables for defining qubits.
Faster quantum concentration via Grover's search
Published in International Journal of Parallel, Emergent and Distributed Systems, 2023
The promise of quantum computing in speeding up computations continues to attract research into exploring quantum algorithms for problems that arise in computer science, mathematics, and other scientific fields of study beyond searching and factorization. Indeed, several quantum algorithms and circuits have been reported for a wide range of classical problems extending from algebraic computations to pattern matching and many problems in graph theory. See for example [1,2] for a survey of quantum algorithms for Abelian and non-Abelian discrete Fourier transform, hidden subgroup problem, computing discrete logarithms, and several other problems in number theory, cryptography, and group theory. Another article by Montanaro provides an overview of quantum algorithms, and in particular surveys the complexity of quantum searching and optimization algorithms [3]. In graph theory, Grover's search algorithm and quantum walk techniques have been used to solve matching and network flow problems [4–6] and graph traversals [7].
Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm
Published in IETE Journal of Research, 2023
Tarun Kumar, Dilip Kumar, Gurmohan Singh
Quantum computing offers a different perspective on information processing. Unlike classical computers, which are constructed by physically implementing two states i.e. 1 and 0, quantum computers operate using quantum states and their linear combinations [5]. The fundamental unit of representing information in quantum systems is referred to as Quantum-bit (Qubit). Quantum computers gained the power to manipulate and handle multiple states simultaneously from the quantum mechanic principle of superposition and entanglement [6]. Computations executed by following the laws of quantum mechanics are generally called quantum computing. Quantum computing offers the manipulation of information with the help of quantum algorithms/circuits [6,7].
Optimization of diesel engine dual-variable geometry turbocharger regulated two-stage turbocharging system based on radial basis function neural network-quantum genetic algorithm
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
Guangmeng Zhou, Ruilin Liu, Zhongjie Zhang, Chunhao Yang, Haojian Ding
Quantum genetic algorithm (QGA) is a new evolutionary algorithm based on quantum computing theory and genetic algorithm ideas (Chen et al., 2005). Quantum computing uses the properties of superposition, entanglement, and interference of quantum states to solve problems that are difficult to solve in the traditional computing field. The quantum genetic algorithm introduces quantum state vector into genetic coding, and applies quantum probability amplitude representation to chromosome coding, so that one chromosome can express the superposition of multiple states, and quantum gate is used to realize population evolution, so as to realize optimal solution of the problem. Qubits draw on quantum theory and can be expressed as the intermediate state of and :