Quantum-Machine-Learning-Enhanced Hybrid Algorithms for Efficient Many-Body Physics Simulations and Quantum State Reconstruction

Abstract

Authors: Dr. Ananya R. Mehta, Dr. Karthik V. Narayanan

ABSTRACT: The intersection of quantum computing and machine learning has given rise to a powerful paradigm known as Quantum Machine Learning (QML), offering transformative potential for solving problems in many-body physics. Classical computational approaches often fail to handle the exponential complexity of quantum systems, especially in modeling entanglement and correlations. This paper explores the application of QML algorithms—such as variational quantum eigensolvers (VQEs), quantum neural networks (QNNs), and hybrid quantum-classical frameworks—in simulating many-body systems. It analyzes the theoretical underpinnings, computational advantages, and implementation challenges associated with QML in physics simulations. The study also investigates emerging hybrid algorithms that combine classical deep learning with quantum circuit optimization to improve scalability, accuracy, and interpretability. Furthermore, the paper highlights open research challenges, future opportunities, and practical implications in condensed matter physics, quantum chemistry, and materials science.

KEYWORDS: Quantum Machine Learning, Many-Body Physics, Quantum Neural Networks, Variational Quantum Eigensolver, Quantum Simulation, Hybrid Computing, Quantum State Reconstruction.