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About Learning Ground States of Quantum Hamiltonians With Graph Networks
This document discusses using graph neural networks to approximate the ground states of quantum Hamiltonians. It aims to learn distributed representations of wavefunction amplitudes that respect the underlying physical symmetries of the problem. The approach achieves state-of-the-art results on benchmark problems and works well on problems whose solutions are not positive-definite. It also generalizes to larger system sizes without retraining, allowing it to approximate solutions of Heisenberg Hamiltonians with hundreds of quantum degrees of freedom.
Detailed Information
| Author: | ['Jorge Vallejos'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 19 |
| Language: | English |
| Format: | |
| Price: | FREE |
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