Quantum Machine Learning for Chemistry and Physics

Abstract

Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to conclude that ML and its close cousin deep learning (DL) have ushered unprecedented developments in all areas of physical sciences especially chemistry. Not only the classical variants of ML , even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionzed material design and performance of photo-voltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is to not only to foster exposition to the aforesaid techniques but also to empower and promote cross-pollination among future-research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

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Cited by in corpus (27)

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• Dequantizing quantum machine learning models using tensor networks
• A General Approach to Dropout in Quantum Neural Networks
• Permutation Invariant Encodings for Quantum Machine Learning with Point Cloud Data
• Towards Perturbation Theory Methods on a Quantum Computer
• Reducing the Quantum Many-electron Problem to Two Electrons with Machine Learning
• Variational Approach to Quantum State Tomography based on Maximal Entropy Formalism
• Quantum Extreme Learning of molecular potential energy surfaces and force fields
• Magnetic Phases of Spatially-Modulated Spin-1 Chains in Rydberg Excitons: Classical and Quantum Simulations
• An Iterative Method to Improve the Precision of Quantum Phase Estimation Algorithm
• Variational quantum computing for quantum simulation: principles, implementations, and challenges
• Hamiltonian learning from time dynamics using variational algorithms
• The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
• QuForge: A Library for Qudits Simulation
• Quantum machine learning for multiclass classification beyond kernel methods
• Random Projection using Random Quantum Circuits
• Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction
• A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors
• Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing
• Analogy between Boltzmann machines and Feynman path integrals
• Properties of fermionic systems with the Path-integral ground state method
• Subsampling Factorization Machine Annealing
• Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision
• Learning Minimal Representations of Fermionic Ground States