Natural Limitations of Quantum Computing

Adeh, Fayez Fok Al

doi:10.4172/2090-4908.1000152

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…So, the hybrid methodologies can be the novel approaches with a great potential to provide precise information for facilitating the experimental design of biosensors. In spite of their supreme advantages to design biosensors, the computational methods possess some limitations, as illustrated in Table 2 (Al Adeh, 2017; Allen, 2004; Cheatham & Kollman, 2000; Drummond, 2019; Ellis, 2012; Frenkel et al, 1997; Ghasemi et al, 2017; Hutter, 2018; Klebe, 2006; Lavecchia & Di Giovanni, 2013; Pons et al, 2010; Prieto‐Martínez et al, 2019; Sethi et al, 2019).…”

Section: Computational Methods As a Complement Of Experimental Technimentioning

confidence: 99%

Recent advances in computational methods for biosensor design

Khoshbin

Housaindokht

Izadyar

et al. 2020

Biotech & Bioengineering

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Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost‐effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast‐growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamics simulation, quantum mechanics calculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.

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Section: Computational Methods As a Complement Of Experimental Technimentioning

confidence: 99%

Recent advances in computational methods for biosensor design

Khoshbin

Housaindokht

Izadyar

et al. 2020

Biotech & Bioengineering

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“…Not only that, the workings of probabilities necessary to predict events in the quantum world leave quite a few uncertainties. However, due to the limitless potential of the infinite quantum world, one may be satisfied with the rather small probability of all predicted events actually occurring (Al Adeh, 2017). But in order for a quantum computer to truly function by the operations of quantum physics, total isolation is a requirement.…”

Section: The Practicality Of Quantum Computingmentioning

confidence: 99%

Insight Into Quantum Computing

Shrivastav

2022

J Stud Res

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Newtonian physics has provided an explanation for the interactions occurring in the everyday world while encouraging the development of technology, such as computers, that utilize our understanding of physics. However, classical computers based on Newtonian physics fail to operate on an advanced level necessary in the growing fields of dark matter or machine learning. As an alternative, quantum mechanics provide the potential for an improved computing system. The nonlinearity and superposition properties of quantum computing ensure that quantum computers will be better suited in certain matters, such as simulating quantum systems or factoring insurmountable numbers, when compared to classical computers. Advances in machine learning and dark matter detection with the use of quantum technology exemplify the true potential of quantum computing. While quantum computing has the capability to advance the world, the realistic means of creating a quantum computer is hindered by the difficulties of quantum mechanics. For example, measuring qubits through entanglement and isolating a quantum computer are both tasks posing as barriers in the path of quantum computing. Nevertheless, experiments overcoming these obstacles demonstrate a means to achieve the implementation of quantum computers to solve scientific mysteries. This paper will review the potential applications of quantum computing while also considering the problems it faces. It will stress the importance of research in quantum mechanics and a future focused on improving quantum technology.

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“…Despite the potential demonstrated by QC, this research field is still at its dawn, and the available quantum processors present some limitations in terms of performance and capability [4,27]. Issues such as the reduced size of the quantum processors, the inaccurate control of the quantum resources, or the limitations in material science should be faced by researchers and engineers on their effort towards fault-tolerant quantum-computing.…”

Section: Introductionmentioning

confidence: 99%

Hybrid quantum-classical heuristic for the bin packing problem

Andoin

Osaba

Oregi

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Optimization problems is one of the most challenging applications of quantum computers, as well as one of the most relevants. As a consequence, it has attracted huge efforts to obtain a speedup over classical algorithms using quantum resources. Up to now, many problems of different nature have been addressed through the perspective of this revolutionary computation paradigm, but there are still many open questions. In this work, a hybrid classical-quantum approach is presented for dealing with the one-dimensional Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one designed for being executed in different computational ecosystems. First, a quantum subroutine seeks a set of feasible bin configurations of the problem at hand. Secondly, a classical computation subroutine builds complete solutions to the problem from the subsets given by the quantum subroutine. Being a hybrid solver, we have called our method H-BPP. To test our algorithm, we have built 18 different 1dBPP instances as a benchmarking set, in which we analyse the fitness, the number of solutions and the performance of the QC subroutine. Based on these figures of merit we verify that H-BPP is a valid technique to address the 1dBPP.

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Natural Limitations of Quantum Computing

Cited by 5 publications

References 3 publications

Recent advances in computational methods for biosensor design

Recent advances in computational methods for biosensor design

Insight Into Quantum Computing

Hybrid quantum-classical heuristic for the bin packing problem

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