5 papers · 1 filter
A Theory of Finite-Noise Optima and Generalization in Quantum Machine Learning
Ziyu Zhang, Zikang Jia, Xiaosong Li +1
Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show moderate noise can reduce testi…
Rethinking Noise in Quantum Machine Learning: When Noise Improves Learning
Linghua Zhu, Yulong Dong, Ziyu Zhang +1
Quantum noise is conventionally viewed as a fundamental obstacle in near-term quantum computing, motivating extensive error correction and mitigation strategies. \REV{We present nu…
QuGStep: Refining Step Size Selection in Gradient Estimation for Variational Quantum Algorithms
Senwei Liang, Linghua Zhu, Xiaosong Li +1
Variational quantum algorithms (VQAs) offer a promising approach to solving computationally demanding problems by combining parameterized quantum circuits with classical optimizati…
Artificial-Intelligence-Driven Shot Reduction in Quantum Measurement
Senwei Liang, Linghua Zhu, Xiaolin Liu +2
Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However,…
Optimizing Shot Assignment in Variational Quantum Eigensolver Measurement
Linghua Zhu, Senwei Liang, Chao Yang +1
The rapid progress in quantum computing has opened up new possibilities for tackling complex scientific problems. Variational quantum eigensolver (VQE) holds the potential to solve…