Quantum Scrambling Born Machine
This work addresses quantum generative modeling for near-term quantum computing applications, presenting an incremental improvement by simplifying optimization in quantum circuits.
The authors tackled the problem of quantum generative modeling by proposing a Quantum Scrambling Born Machine that uses fixed entangling unitaries as scrambling reservoirs with only single-qubit rotations optimized, showing it learns target distributions effectively once near-Haar-typical entanglement is achieved and achieving performance competitive with classical generative models at matched parameter counts.
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.