Entanglement detection with artificial neural networks

Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology. Supplementary Information The online version contains supplementary material available at 10.1007/s12033-023-00765-4.

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“…It is also unique to quantum computational systems. Researchers have demonstrated that it has multiple theoretical quantum advantages [ 1 , 20 ].…”

Section: Quantum Computing Using Quantum Mechanics: New Notationsmentioning

confidence: 99%

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

et al. 2023

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“…This indicates that the deeper networks are more able to identify pure entanglement properties of the systems while the smaller ones can be tricked by other properties of the dataset as the rank of the density matrices. Interestingly, in Refs [26,27]…”

Section: Entanglement Detection For Two Qubits Systemsmentioning

confidence: 99%

“…Conversely, Ref. [27] develops a similar approach, albeit based on the results of coherence measurements. While both strategies yield noteworthy outcomes, their effectiveness is contingent upon prior knowledge about the system, delineating a clear boundary on their applicability and potential for further exploration in diverse system contexts.…”

Section: Introductionmentioning

confidence: 99%

Entanglement detection with classical deep neural networks

Ureña,

Sojo,

Bermejo-Vega

et al. 2024

Preprint

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In this study, we introduce an autonomous method for addressing the detection and classification of quantum entanglement, a core element of quantum mechanics that has yet to be fully understood. We employ a multi-layer perceptron to effectively identify entanglement in both twoand three-qubit systems. Our technique yields impressive detection results, achieving nearly perfect accuracy for two-qubit systems and over 90% accuracy for three-qubit systems. Additionally, our approach successfully categorizes three-qubit entangled states into distinct groups with a success rate of up to 77%. These findings indicate the potential for our method to be applied to larger systems, paving the way for advancements in quantum information processing applications.

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“…Hu and Fan [111] investigated the maximal coherence of steered states. Asif et al [112] have designed a classifier for detecting the separability and entanglement of quantum states, using supervised learning and Bell-type inequality for the entropy of coherence.…”

Section: Witnessing Quantum Correlationsmentioning

confidence: 99%

The application of quantum coherence as a resource

Liu 刘,

Fan 范

2023

Chinese Phys. B

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Quantum coherence is a basic concept in quantum mechanics, which represents one of the most fundamental characteristics that distinguishes quantum mechanics from classical physics. Quantum coherence is the basis for multi-particle interference and quantum entanglement. It is also the essential ingredient for various physical phenomena in quantum optics, quantum information, etc. In recent years, with the proposal of quantum coherence measure scheme based on resource theory framework, quantum coherence as a quantum resource has been extensively investigated. This article reviews the resource theories of quantum coherence and introduces the important applications of quantum coherence in quantum computing, quantum information, and interdisciplinary fields, particularly in quantum thermodynamics and quantum biology. The research and application of quantum coherence are still being explored and developed. We hope this review can provide inspiration for relevant research.

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Entanglement detection with artificial neural networks

Cited by 15 publications

References 32 publications

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

Entanglement detection with classical deep neural networks

The application of quantum coherence as a resource

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