Quantum Alchemy and Universal Orthogonality Catastrophe in One-Dimensional Anyons
Naim E. Mackel, Jing Yang, Adolfo del Campo

TL;DR
This paper explores the geometry and orthogonality properties of one-dimensional anyonic quantum states, revealing a universal orthogonality catastrophe governed by statistical factors, with implications for quantum simulation.
Contribution
It introduces a continuous transformation framework for anyonic states, characterizes their geometric properties, and uncovers a universal orthogonality catastrophe independent of microscopic details.
Findings
States with different statistics have finite overlaps with a universal decay form.
Orthogonality catastrophe is governed by a fundamental statistical factor.
Quantum speed limits relate to the flow of the statistical parameter .
Abstract
Many-particle quantum systems with intermediate anyonic exchange statistics are supported in one spatial dimension. In this context, the anyon-anyon mapping is recast as a continuous transformation that generates shifts of the statistical parameter . We characterize the geometry of quantum states associated with different values of , i.e., different quantum statistics. While states in the bosonic and fermionic subspaces are always orthogonal, overlaps between anyonic states are generally finite and exhibit a universal form of the orthogonality catastrophe governed by a fundamental statistical factor, independent of the microscopic Hamiltonian. We characterize this decay using quantum speed limits on the flow of , illustrate our results with a model of hard-core anyons, and discuss possible experiments in quantum simulation.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Mechanics and Applications · Markov Chains and Monte Carlo Methods · Complex Network Analysis Techniques
