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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• Quantum Machine Learning: from physics to software engineering
• Integrating Quantum Computing Resources into Scientific HPC Ecosystems
• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Imaginary components of out-of-time correlators and information scrambling for navigating the learning landscape of a quantum machine learning model
• 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
• Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
• Variational quantum computing for quantum simulation: principles, implementations, and challenges
• Hamiltonian learning from time dynamics using variational algorithms
• An Iterative Method to Improve the Precision of Quantum Phase Estimation Algorithm
• Quantum machine learning for multiclass classification beyond kernel methods
• QuForge: A Library for Qudits Simulation
• The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
• Random Projection using Random Quantum Circuits
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• Analogy between Boltzmann machines and Feynman path integrals
• Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision
• Subsampling Factorization Machine Annealing
• Learning Minimal Representations of Fermionic Ground States