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Note on the Universality of Parameterized IQP Circuits with Hidden Units for Generating Probability Distributions
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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.
Forward citations
Cited by 2 Pith papers
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Trainability and Mode Separation of Mixed IQP-QCBMs
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.
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Universality of Many-body Projected Ensemble for Learning Quantum Data Distribution
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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