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Quantum circuit born machines trainability analyzed under Gaussian initialization

By PulseAugur Editorial · [1 sources] ·

Researchers have analyzed the trainability of Quantum Circuit Born Machines (QCBMs) that utilize Instantaneous Quantum Polynomial (IQP) circuits, specifically under Gaussian initialization schemes. The study employs Stein's lemma and Lipschitz concentration bounds to establish analytical lower bounds for gradient variance and probabilistic concentration bounds for gradient deviation. This work aims to identify conditions that lead to barren plateaus and offers strategies to either avoid or encourage exponential concentration in these quantum machine learning models. AI

RANK_REASON The cluster contains an academic paper detailing theoretical research on quantum machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Quantum circuit born machines trainability analyzed under Gaussian initialization

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The cluster contains an academic paper detailing theoretical research on quantum machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Gennaro De Luca ·

    Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization

    arXiv:2606.10179v1 Announce Type: cross Abstract: Quantum Circuit Born Machines (QCBMs) offer a natural approach to generative machine learning by leveraging the Born rule. Recent work has provided a method to classically train QCBMs with Instantaneous Quantum Polynomial (IQP) ci…

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