Quantum computation and quantum information†

Nagy, Marius; Akl, Selim G.

doi:10.1080/17445760500355678

Cited by 30 publications

(8 citation statements)

References 143 publications

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“…For example, from a perspective of states, eigenstates of different qubits can be orthogonal; from a perspective of phase, on the Bloch sphere, the rotation operators about the x, ŷ and ẑ axes, i.e., R x (𝜃 x ), R y (𝜃 y ), and R z (𝜃 z ) are simply a rotation of the sphere about the corresponding axis by 𝜃 x , 𝜃 y , and 𝜃 z respectively, and the planes of these angles, which are parameters of quantum neural networks, are orthogonal to each other no matter what the angles are. [17] Based on the points discussed above, we assume that the characteristic of quantum states and orthogonality relations in quantum computing may partially explain why the generalizability of our quantum hybrid models has been improved compared to that of the classical ones.…”

Section: Performance Of Quantum Hybrid Deep Learning Modelsmentioning

confidence: 99%

Prediction of Protein‐Ligand Binding Affinity by a Hybrid Quantum‐Classical Deep Learning Algorithm

Dong

Li²,

Liu

et al. 2023

Adv Quantum Tech

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Rapid and accurate prediction of protein‐ligand binding affinity plays a vital role in high‐throughput drug screening. With the development of deep learning, increasingly accurate prediction models have been established. Deep learning may have ushered in an era of quantization, but the practical use of this theory for protein‐ligand binding affinity is still infrequent. Here, the introduction of the quantum algorithm into classical deep learning is described, which enables it to reliably predict protein‐ligand binding affinity using simple sequence information. Based on different deep learning models, including graph neural networks (GNN) and convolutional neural networks (CNN), corresponding quantum hybrid deep learning models have been constructed and compared to the classical models. This study has shown that the quantum algorithm can achieve considerable accuracy and good generalization, and show potential to balance between accuracy and generalization, although the parameters used in the model have been remarkably reduced. These models based on quantum hybrid deep learning (QDL) show robust predictions on four benchmark datasets, and exhibit the practical application power in drug screening for targets related to human liver cirrhosis. This work highlights the potential of the hybrid quantum deep learning algorithm in solving complex problems in bioinformatics.

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Section: Performance Of Quantum Hybrid Deep Learning Modelsmentioning

confidence: 99%

Prediction of Protein‐Ligand Binding Affinity by a Hybrid Quantum‐Classical Deep Learning Algorithm

Dong

Li²,

Liu

et al. 2023

Adv Quantum Tech

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“…Several researchers have contributed to the successful growth and implementations of quantum computing [12], [13]. It began in 1980 when the a quantum mechanical framework of the Turing machine was examined [14].…”

Section: B Real-time Optimisation Framework For Wireless Networkmentioning

confidence: 99%

Quantum-Inspired Real-Time Optimization for 6G Networks: Opportunities, Challenges, and the Road Ahead

Duong

Nguyen

Narottama

et al. 2022

IEEE Open J. Commun. Soc.

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It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and connected people to connected intelligence. The main goal of 6G networks is to support massive connectivity for time-sensitive and computation-sensitive services in mission-critical applications. The creation of real-time optimisation (RTO) enabled by the fast growing data analytic and machine learning will seize the opportunities for 6G wireless networks to support such immersive services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and tactile Internet. Recently, with the rapid development of quantum computers, quantum-inspired optimisation and machine learning algorithms have been exploited as efficient solutions for future wireless networks. In this article, we provide a comprehensive view on the new concept of quantum-inspired RTO and its application to the optimal resource allocation for 6G wireless networks. Our main contributions are to introduce some of the initial research results and introduce the potentiality of quantum-inspired RTO on some 6G emerging technologies. Not only do we review the fundamental principles; we also explore the challenges and opportunities of this exciting research direction.

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“…The quantum computing model has become a hot topic in recent years, and it was first proposed by the American physicist Feynman in 1982 [1]. The famous Moore's Law states that computer performance will double every 2-3 years [2]. However, Moore's Law cannot hold forever with the electronic components cannot shrink indefinitely.…”

Section: Introductionmentioning

confidence: 99%

The quantum realization of image linear gray enhancement

Wei

2023

Quantum Mach. Intell.

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Linear gray enhancement is a spatial domain image enhancement technique commonly used in classical computers, mainly including image negative, image contrast stretching, and piecewise linear gray transformation. In order to realize these three linear gray enhancement techniques in the quantum computers, this paper proposes three types of linear gray transformation schemes for quantum images based on the generalized model of novel enhanced quantum image representation(GNEQR), and the quantum circuits that realize these three transformation methods are constructed according to the schemes. The proposed circuits take advantage of efficient quantum arithmetic operations and parallel Controlled-NOT modules to factor classical transformations into basic unitary operators such as the Controlled-NOT gates and the Toffoli gates. The results show that the linear gray enhancement algorithm for quantum images is better than the classical algorithm in both spatial complexity and time complexity.

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Quantum computation and quantum information†

Cited by 30 publications

References 143 publications

Prediction of Protein‐Ligand Binding Affinity by a Hybrid Quantum‐Classical Deep Learning Algorithm

Prediction of Protein‐Ligand Binding Affinity by a Hybrid Quantum‐Classical Deep Learning Algorithm

Quantum-Inspired Real-Time Optimization for 6G Networks: Opportunities, Challenges, and the Road Ahead

The quantum realization of image linear gray enhancement

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