Year: 2025
Type: article
Abstract: Generative quantum machine learning has gained significant attention for its ability to produce quantum states with desired distributions. Among various quantum generative models, quantum denoising diffusion probabilistic models (QuDDPMs) [Zhang et al., Phys. Rev. Lett. 132, 100602 (2024)] provide a promising approach with stepwise learning that resolves the training issues. However, the requirement of high-fidelity scrambling unitaries in QuDDPMs poses a challenge to near-term implementation. We propose a mixed-state quantum denoising diffusion probabilistic model (MSQuDDPM) to eliminate the need for scrambling unitaries. Our approach focuses on adapting the quantum noise channels to the model architecture, which integrates depolarizing noise channels in the forward diffusion process and parameterized quantum circuits with projective measurements in the backward denoising steps. We also introduce several techniques to improve MSQuDDPM, including a cosine-exponent schedule of noise interpolation, the use of a single-qubit random ancilla, and superfidelity-based cost functions to enhance the convergence. We evaluate MSQuDDPM on quantum ensemble generation tasks, demonstrating its successful performance.
Source: Physical Review A
Authors: Gino Kwun, Bingzhi Zhang, Quntao Zhuang
Institution: University of Southern California
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1770695805, G1872762428, G3877413663, G6667866919, QuSeC-TAQS: Sensing-Intelligence on The Move: Quantum-Enhanced Optical Diagnosis of Crop Diseases, CAREER: Using Entanglement to Enhance Communication and Sensing in the Presence of Noise, Collaborative Research: NeTS: Small: Models and Algorithms for Hybrid Continuous-Discrete Variable Quantum Networking, G8876996369
OpenAlex ID: W4408446894