NetKet
A Python library built on JAX for studying many-body quantum systems using neural networks and machine learning.
What is NetKet?
NetKet is an open-source Python library built on JAX that delivers cutting-edge methods for studying many-body quantum systems using artificial neural networks and machine learning techniques. It provides a comprehensive toolkit to represent quantum wavefunctions with neural networks and estimate quantum observables through variational Monte Carlo. The project bridges quantum physics and machine learning by offering efficient, scalable algorithms to make complex quantum systems more computationally tractable.
Researchers and practitioners in quantum physics and computational science who need to simulate and analyze many-body quantum systems with advanced neural network techniques. This includes academics, scientists, and engineers working on quantum mechanics, condensed matter physics, or quantum computing who require high-performance tools for variational methods.
Developers choose NetKet for its integration of modern machine learning frameworks like JAX, enabling automatic differentiation and GPU acceleration for high-performance computing on quantum problems. Its unique selling point is providing a specialized, scalable toolkit that combines neural quantum states with variational Monte Carlo methods, which is not commonly found in general-purpose quantum simulation libraries.
Overview
Machine learning algorithms for many-body quantum systems
Use Cases
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