Quantum Scrambling Born Machine

• URL: http://arxiv.org/abs/2602.17281v1
• Date: Thu, 19 Feb 2026 11:33:56 GMT
• Title: Quantum Scrambling Born Machine
• Authors: Marcin Płodzień,
• Abstract summary: Quantum generative modeling, where the Born rule naturally defines probability distributions, is a promising near-term application of quantum computing.<n>We propose a Quantum Scrambling Born Machine in which a fixed entangling unitary provides multi-qubit entanglement, while only single-qubit rotations are optimized.<n>We 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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