Speaker
Description
The existence and unraveling of different phases of matter for various configurations of the same system constitutes a central task in quantum many body physics. Recent developments addressed this problem from data, by studying the differences between states, e. g. through fidelity measures, and inferring phase transitions from them. We introduce the IPR-kernel, a kernel method built for learning quantum phases of matter from inverse participation ratios (IPRs) in physically motivated bases. Unlike standard fidelity approaches, the IPR-kernel can identify states with physically-motivated similarity even when the are orthogonal. We demonstrate the method on ground states of the transverse-field Ising (TFIM) and Heisenberg XXZ models, and show for TFIM Gibbs states that the IPR-kernel retains phase-relevant structure at nonzero temperature, while fidelity-based approaches are insensitive to such structure.
| Topic | Quantum kernel methods and their applications |
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