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Higher moment theory and learnability of bosonic states

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

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quant-ph 3

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2026 3

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UNVERDICTED 3

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representative citing papers

Quantum state isomorphism problems for groups

quant-ph · 2026-05-12 · unverdicted · novelty 8.0

Quantum state isomorphism under group actions is BQP-hard for pure states across nontrivial groups and QSZK-complete for mixed states with finite groups; Pauli group version is BQP-complete and Clifford is GI-hard, ruling out efficient quantum algorithms for abelian mixed-state HS unless QSZK=BQP.

Exponentially-improved effective descriptions of physical bosonic systems

quant-ph · 2026-04-20 · unverdicted · novelty 8.0

A natural energy condition satisfied by most physical bosonic states, including outputs of universal bosonic circuits, allows the effective dimension for ε-approximations to scale as log(1/ε) instead of 1/ε², enabling improved learning and classical simulation algorithms.

Advances in quantum learning theory with bosonic systems

quant-ph · 2026-05-08 · unverdicted · novelty 2.0

A concise review of sample complexities and methods for tomography and learning in continuous-variable quantum systems, with emphasis on Gaussian versus non-Gaussian states.

citing papers explorer

Showing 3 of 3 citing papers.

  • Quantum state isomorphism problems for groups quant-ph · 2026-05-12 · unverdicted · none · ref 31

    Quantum state isomorphism under group actions is BQP-hard for pure states across nontrivial groups and QSZK-complete for mixed states with finite groups; Pauli group version is BQP-complete and Clifford is GI-hard, ruling out efficient quantum algorithms for abelian mixed-state HS unless QSZK=BQP.

  • Exponentially-improved effective descriptions of physical bosonic systems quant-ph · 2026-04-20 · unverdicted · none · ref 48

    A natural energy condition satisfied by most physical bosonic states, including outputs of universal bosonic circuits, allows the effective dimension for ε-approximations to scale as log(1/ε) instead of 1/ε², enabling improved learning and classical simulation algorithms.

  • Advances in quantum learning theory with bosonic systems quant-ph · 2026-05-08 · unverdicted · none · ref 38

    A concise review of sample complexities and methods for tomography and learning in continuous-variable quantum systems, with emphasis on Gaussian versus non-Gaussian states.