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On estimates of trace-norm distance between quantum Gaussian states

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arxiv 2408.11400 v6 pith:4CRHBTWA submitted 2024-08-21 quant-ph math-phmath.MP

classification quant-phmath-phmath.MP
keywords statesestimatesgaussiandistancequantumarxivdifferentleading
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In the paper of F.A. Mele, A.A. Mele, L. Bittel, J. Eisert, V. Giovannetti, L. Lami, L. Leone, S.F.E. Oliviero, ArXiv:2405.01431, estimates for the trace-norm distance between two quantum Gaussian states in terms of the mean vectors and covariance matrices were derived and used to evaluate the sample complexity of learning quantum energy-constrained Gaussian states. In the present paper we obtain different estimates; our proof is based on a fidelity-like quantity which we call states overlap, and is more straightforward leading to estimates which are sometimes even more stringent, especially in the cases of pure or gauge-invariant states. They do not depend on number of modes and hence can be extended to the case of bosonic field with infinite number of modes. These derivations are not aimed to replace the useful inequalities from ArXiv:2405.01431; they just show an alternative approach to the problem leading to different results. In the Appendix we briefly recall our results concerning estimates of the overlap for general fermionic Gaussian states of CAR. The problem studied in this paper can be considered as a noncommutative analog of estimation of the total variance distance between Gaussian probability distributions in the classical probability theory.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Energy-independent tomography of Gaussian states

    quant-ph 2025-08 unverdicted novelty 7.0 of 10

    A tomography protocol estimates Gaussian states in trace distance with sample complexity independent of energy (up to doubly logarithmic factors), a doubly exponential improvement over prior methods.

  2. Advances in quantum learning theory with bosonic systems

    quant-ph 2026-05 unverdicted novelty 2.0 of 10

    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.

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