Autonomous phase discovery
Understanding quantum phases of matter has long relied on physicists' intuition and mathematical tools such as symmetry and topology. Remarkably successful as these approaches have been, they provide no universal way to explore a Hamiltonian space whose organizing principle is not known in advance. In this work, we introduce a fully autonomous system combining differentiable programming and unsupervised learning for quantum phase discovery. The search evaluates ground-state data along an adaptive trajectory rather than on a predetermined parameter grid. We demonstrate the system with three different solvers and benchmark it against random sampling at equal ground-state-evaluation budgets....
| Authors | Shiyu Zhou, Yuxuan Zhang, Sebastian Wetzel et al. |
|---|---|
| Primary category | quant-ph |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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