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Quantum Machine Learning

Comment on "Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency": Polynomial Evaluation of the Triplet-Block Readout

arXiv

Authors: Erfan Amidi

Year

2026

Paper ID

75457

Status

Preprint

Abstract Read

~2 min

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Abstract

We examine the classical-cost claim for the triplet-block two-body readout in arXiv:2607.24014v1. The Gaussian-state expansion used there gives an O$2^2k/3poly(n$) classical algorithm, but it is not necessary for fixed-body observables. The triplet-block input has an explicitly computable diagonal two-particle reduced density matrix, which passive fermionic linear optics propagates through bigwedge² W. This gives a deterministic O$n⁴$ algorithm for the complete correlator vector $langle n_i n_jrangle_{i

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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We examine the classical-cost claim for the triplet-block two-body readout in arXiv:2607.24014v1.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2608.20435 [2] arXiv https://arxiv.org/abs/2608.20435 [3] Publisher https://arxiv.org/abs/2608.20435

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