A Set-Transformer architecture with self-attention encodes Pauli-string correlations, optimizes via commutation objective, and finds symmetries with near-deterministic success on physical models like Ising and Toric code.
Clifford symmetries in quantum many-body systems
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Obtaining the symmetries of a model is a critical step towards developing an understanding and ultimately analytically or numerically solving the model. However, finding symmetries is generally extremely complicated, often being the result of insightful thinking. In this work, we complement human ingenuity with an algorithm. We leverage the classically efficient Clifford group to find symmetries for arbitrary many-body Hamiltonians via a graph representation. We demonstrate our method on random and physical Hamiltonians, with instances of up to one thousand qubits and demonstrate how our approach can provide deeper understanding of the model.
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
An algorithm encodes Clifford invariants of qudit Hamiltonians as graph properties so graph automorphisms yield Clifford symmetries up to phase checks, tested on models and extended to open systems.
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Attention-based optimizer for symmetry finding
A Set-Transformer architecture with self-attention encodes Pauli-string correlations, optimizes via commutation objective, and finds symmetries with near-deterministic success on physical models like Ising and Toric code.
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Graph automorphisms to obtain Clifford symmetries in open and closed qudit models
An algorithm encodes Clifford invariants of qudit Hamiltonians as graph properties so graph automorphisms yield Clifford symmetries up to phase checks, tested on models and extended to open systems.