We propose a quantum scrambling Born machine (QSBM) that decouples entanglement generation from the trainable degrees of freedom: a fixed, nontrainable scrambling unitary provides multiqubit 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. Because the scrambler requires only single-shot calibration, the entire trainable parameter budget is devoted to single-qubit rotations, eliminating the need for trainable multiqubit entangling gates on near-term hardware. 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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• Quantum circuits
• Quantum information processing
• Quantum simulation