Quantum Scrambling Born Machine

Abstract:Quantum generative modeling, where the Born rule naturally defines probability distributions through measurement of parameterized quantum states, is a promising near-term application of quantum computing. We propose a Quantum Scrambling Born Machine in which a fixed entangling unitary -- acting as a scrambling reservoir -- provides multi-qubit entanglement, while only single-qubit rotations are optimized. We consider three entangling unitaries -- a Haar random unitary and two physically realizable approximations, a finite-depth brickwork random circuit and analog time evolution under nearest-neighbor spin-chain Hamiltonians -- and show that, for the benchmark distributions and system sizes considered, once the entangler produces near-Haar-typical entanglement the model learns the target distribution with weak sensitivity to the scrambler's microscopic origin. Finally, promoting the Hamiltonian couplings to trainable parameters casts the generative task as a variational Hamiltonian problem, with performance competitive with representative classical generative models at matched parameter count.

Quantum Physics (quant-ph); Machine Learning (cs.LG)

arXiv:2602.17281 [quant-ph]

(or arXiv:2602.17281v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2602.17281 Focus to learn more arXiv-issued DOI via DataCite

Phys. Rev. A 114, 012464 (2026)

https://doi.org/10.1103/qccv-flmq Focus to learn more DOI(s) linking to related resources

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