Quantum Generative Diffusion Model
Chuangtao Chen Qinglin Zhao
This paper introduces the Quantum Generative Diffusion Model (QGDM), a fully
quantum-mechanical model for generating quantum state ensembles, inspired by
Denoising Diffusion Probabilistic Models. QGDM features a diffusion process
that introduces timestep-dependent noise into quantum states, paired with a
denoising mechanism trained to reverse this contamination. This model
efficiently evolves a completely mixed state into a target quantum state
post-training. Our comparative analysis with Quantum Generative Adversarial
Networks demonstrates QGDM's superiority, with fidelity metrics exceeding 0.99
in numerical simulations involving up to 4 qubits. Additionally, we present a
Resource-Efficient version of QGDM (RE-QGDM), which minimizes the need for
auxiliary qubits while maintaining impressive generative capabilities for tasks
involving up to 8 qubits. These results showcase the proposed models' potential
for tackling challenging quantum generation problems.