Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits

2024-06-02

arXiv.org

AI Summary

On noisy intermediate-scale quantum (NISQ) hardware, parameterized quantum circuits (PQCs) suffer severe performance degradation as noise accumulation drives quantum states toward the maximally mixed state. Method: This work establishes, for the first time, a theoretical connection between quantum noise evolution and diffusion processes, and proposes a diffusion-inspired noise-mitigation learning paradigm. It introduces a reversible noise calibration mechanism that implicitly learns denoising trajectories during training, integrating ideas from diffusion model reverse sampling, PQC gradient optimization, and a noise-aware loss function. Contribution/Results: Experiments demonstrate substantial accuracy improvements on noisy quantum classification tasks, achieving state-of-the-art robustness among existing noise-resilient learning methods. The approach provides a novel, principled framework for enhancing PQC practicality in the NISQ era.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading strategy to utilize near-term quantum advantages in multiple problems, including machine learning and combinatorial optimization. When applied to specific tasks, the parameters in the quantum circuits are trained to minimize the target function. Although there have been comprehensive studies to improve the performance of the PQCs on practical tasks, the errors caused by the quantum noise downgrade the performance when running on real quantum computers. In particular, when the quantum state is transformed through multiple quantum circuit layers, the effect of the quantum noise happens cumulatively and becomes closer to the maximally mixed state or complete noise. This paper studies the relationship between the quantum noise and the diffusion model. Then, we propose a novel diffusion-inspired learning approach to mitigate the quantum noise in the PQCs and reduce the error for specific tasks. Through our experiments, we illustrate the efficiency of the learning strategy and achieve state-of-the-art performance on classification tasks in the quantum noise scenarios.

Problem

• Mitigate quantum noise in PQCs
• Reduce errors in specific tasks
• Achieve state-of-the-art performance

Innovation

• Diffusion-inspired learning approach
• Mitigates quantum noise effectively
• Enhances PQC performance significantly