Deep Learning Tactics Speed Quantum Simulations

Machine learning is emerging as a useful tool in the development of quantum computing platforms, particularly in optimizing the methodologies used to simulate quantum systems and speed the time it takes to create and test different approaches and designs.

A key challenge, however, is being able to prepare a quantum-mechanical system in its ground state – that is, its lowest-energy state. Solving the ground state preparation problem would enable researchers to investigate the existence of novel quantum phases of matter and open the door to innovative applications – including high-temperature superconductors, magnetic field sensors, and synthetic molecules – based on quantum phenomena.

To make this happen, researchers are looking at combinations of old and new mathematical and physical techniques to solve fundamental issues such as many-body control. A new paper published September 30 in Physical Review X outlines an algorithm that blends variational quantum algorithms with reinforcement learning, a powerful technique for training machine learning models, to make a sequence of decisions.

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