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A new proof for the existence of mutually unbiased bases

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arxiv quant-ph/0103162 v3 pith:CNVF6S26 submitted 2001-03-29 quant-ph

A new proof for the existence of mutually unbiased bases

classification quant-ph
keywords basesmutuallyunbiasedexistencedimensionmatricesproofcommuting
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We develop a strong connection between maximally commuting bases of orthogonal unitary matrices and mutually unbiased bases. A necessary condition of the existence of mutually unbiased bases for any finite dimension is obtained. Then a constructive proof of the existence of mutually unbiased bases for dimensions which are power of a prime is presented. It is also proved that in any dimension d the number of mutually unbiased bases is at most d+1. An explicit representation of mutually unbiased observables in terms of Pauli matrices are provided for d=2^m.

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Cited by 3 Pith papers

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

  1. Mutually Unbiased Bases for Variational Quantum Initialization: Basis-Union Optimality and Adaptive Family Search

    quant-ph 2026-05 unverdicted novelty 7.0

    Complete MUB ensembles are optimal for isotropic Gaussian random-Hamiltonian width among d+1 basis unions, enabling adaptive MUB-XRot QAOA that is non-worse than standard QAOA in 80% of 1500 benchmark cases.

  2. Mutually Unbiased Bases for Variational Quantum Initialization: Basis-Union Optimality and Adaptive Family Search

    quant-ph 2026-05 unverdicted novelty 7.0

    Complete MUB ensembles are optimal for isotropic Gaussian random-Hamiltonian width among d+1 basis unions, and adaptive MUB-XRot QAOA is non-worse than standard QAOA in 80% of 1500 benchmark cases across MaxCut, MIS, ...

  3. Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries

    quant-ph 2024-04 unverdicted novelty 6.0

    A gradient estimator for Lie-symmetric PQCs expresses the gradient as a linear combination of Hadamard-test expectations whose coefficients are estimated via shadow tomography, yielding logarithmic shot scaling.