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Note on the Universality of Parameterized IQP Circuits with Hidden Units for Generating Probability Distributions

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arxiv 2504.05997 v1 pith:LDHV74E3 submitted 2025-04-08 quant-ph

classification quant-ph
keywords modelquantumuniversalitycircuitsdistributionsgeneratinghiddennote
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In a series of recent works, an interesting quantum generative model based on parameterized instantaneous polynomial quantum (IQP) circuits has emerged as they can be trained efficiently classically using any loss function that depends only on the expectation values of observables of the model. The model is proven not to be universal for generating arbitrary distributions, but it is suspected that marginals can be - much like Boltzmann machines achieve universality by utilizing hidden (traced-out in quantum jargon) layers. In this short note, we provide two simple proofs of this fact. The first is near-trivial and asymptotic, and the second shows universality can be achieved with a reasonable number of additional qubits.

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Cited by 2 Pith papers

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

  1. Trainability and Mode Separation of Mixed IQP-QCBMs

    quant-ph 2026-07 conditional novelty 6.0 of 10

    A polynomially-branched mixture of IQP circuits is locally trainable, but its branches must specialize to distinct modes, best seeded by cluster initialization, to outperform a single circuit.

  2. Universality of Many-body Projected Ensemble for Learning Quantum Data Distribution

    quant-ph 2026-01 conditional novelty 4.0 of 10

    Many-body projected ensembles are universal: with enough ancilla qubits, an engineered wave function can approximate any distribution over pure quantum states to any 1-Wasserstein error.

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