Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2401. 07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in quantum information processing.
arXiv:2512. 06695v3 Announce Type: replace Abstract: Quantum generative models exploit quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data.
Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is constrained by the qubit cost of state encoding and the computational burden of simulating large density operators.
arXiv:2606. 27561v1 Announce Type: new Abstract: Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and efficiency.
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.
arXiv:2607. 07072v1 Announce Type: new Abstract: Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states.