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arXiv Machine Learning By Marcin P{\l}odzie\'n

Quantum Scrambling Born Machine

Read the original on arXiv Machine Learning →

The paper introduces the Quantum Scrambling Born Machine, a quantum generative model that uses a fixed entangling unitary—acting as a scrambling reservoir—to generate multi‑qubit entanglement while only optimizing single‑qubit rotations. Three types of entanglers are examined: a Haar random unitary, a finite‑depth brickwork random circuit, and analog time evolution under nearest‑neighbor spin‑chain Hamiltonians. The study finds that once the entangler achieves near‑Haar‑typical entanglement, the model can learn benchmark distributions with little sensitivity to the specific scrambler, and that making the Hamiltonian couplings trainable turns the task into a variational Hamiltonian problem with performance competitive with classical generative models at comparable parameter counts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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