Quantum dropout: On and over the hardness of quantum approximate optimization algorithm

Wang, Zhenduo; Zheng, Pei-Lin; Wu, Biao; Zhang, Yi

doi:10.1103/physrevresearch.5.023171

Cited by 9 publications

(1 citation statement)

References 37 publications

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“…These algorithms have been briefly introduced in Section 2.1. It has been observed that the ruggedness of the search or optimization landscape creates amenability for quantum advantage − (see Section 4 of the Supporting Information for a detailed discussion). The quantum advantage appears when there is a least amount of information or a highest amount of uncertainty about the search space which is also known as the “worst case” in algorithmic complexity analysis.…”

Section: Quantum Computing: a Brief Introductionmentioning

confidence: 99%

A Perspective on Protein Structure Prediction Using Quantum Computers

Doga,

Raubenolt,

Cumbo

et al. 2024

J. Chem. Theory Comput.

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Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

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Section: Quantum Computing: a Brief Introductionmentioning

confidence: 99%

A Perspective on Protein Structure Prediction Using Quantum Computers

Doga,

Raubenolt,

Cumbo

et al. 2024

J. Chem. Theory Comput.

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A General Approach to Dropout in Quantum Neural Networks

Scala,

Ceschini,

Panella

et al. 2023

Adv Quantum Tech

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In classical machine learning (ML), “overfitting” is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in ML is the so called “dropout,” which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of quantum neural networks (QNNs) as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here, a generalized approach is presented to apply the dropout technique in QNN models, defining and analyzing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. This study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the QNN models, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations and may pave the way to efficiently employing deep quantum machine learning (QML) models based on state‐of‐the‐art QNNs.

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A brief review on quantum computing based drug design

Das,

Ray,

Bhattacharyya

et al. 2024

WIREs Data Min & Knowl

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Design and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug molecule to its commercialization in the market. One of the most critical steps in drug design is to find the molecular interactions between the target (infected) molecule and the drug molecule. Several complex chemical equations need to be solved to determine the molecular interactions. In the late 20th Century, the advancement of computational technologies has made the solution of chemical equations relatively easier and faster. Moreover, the design of drug molecules involves multi‐criteria optimization. Classical computational methodologies have been used for drug design since the end of the 20th Century. However, nowadays, more advanced computational methodologies are inevitable in designing drugs for new diseases and drugs with fewer side effects. In this context, the quantum computing paradigm has proved beneficial in drug design due to its advanced computational capabilities. This paper presents a state‐of‐the‐art comprehensive review of the quantum computing‐based methodologies involved in drug design. A comparative study is made about the different quantum‐aided drug design methods, stating each methodology's merits and demerits. The review work presented in this manuscript will help new researchers assess the present state‐of‐the‐art concept of quantum‐based drug design.This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Computational Intelligence Application Areas > Health Care

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Quantum dropout: On and over the hardness of quantum approximate optimization algorithm

Cited by 9 publications

References 37 publications

A Perspective on Protein Structure Prediction Using Quantum Computers

A Perspective on Protein Structure Prediction Using Quantum Computers

A General Approach to Dropout in Quantum Neural Networks

A brief review on quantum computing based drug design

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