arXiv subjects

Zheping Wu

Publications and source records attributed to Zheping Wu.

3 recordsLinked to original sources

arXiv2026

Identification of quantum generative circuits with parallel quantum neural network

The rapid emergence of quantum technology has raised new challenges in distinguishing various quantum circuits of similar functions. In this work, we propose parallel quantum embedding neural network (ParaQuanNet) for the efficient identification of quantum generative circuits via classifications of the corresponding output data. Specifically, we generated W-like states with eight generative quantum circuits realizing the generative quantum denoising diffusion probabilistic models (QDDPM). Our ParaQuanNet can classify these eight classes of generated quantum data with an accuracy of {$99.5\%$}, even though all of them are trained to generate the same types of quantum data. With a novel design of parallel quantum embedding unit (PQEU) in our neural networks, our ParaQuanNet enables the quantum kernel circuit parallelly process all the receptive fields of quantum data, which empowers the quantum data processing efficiency. We also integrate the mutually unbiased measurements into our ParaQuanNet and further improve its performance. We apply our ParaQuanNet on the classification of classical data sets and demonstrate a good performance of quantum neural networks on these tasks. Our approach demonstrates good robustness to noisy data and the circuit-level noise with a Python realization in a classical GPU. Our results highlight ParaQuanNet as a scalable and effective framework for quantum circuits identification, contributing to the broader development of quantum machine intelligence.

quant-ph↗

arXiv2025

Autonomous Floquet Engineering of Bosonic Codes via Reinforcement Learning

Bosonic codes represent a promising route toward quantum error correction in continuous-variable systems, with direct relevance to experimental platforms such as circuit QED and optomechanics. However, their preparation and stabilization remain highly challenging, requiring ultra-precise control of nonlinear interactions to create entangled superpositions, suppress decoherence, and mitigate dynamic errors. Here, we introduce a reinforcement-learning-assisted Floquet engineering approach for the autonomous preparation of bosonic codes that is general, efficient, and noise-resilient. By leveraging machine learning to optimize Floquet driving parameters, our method achieves over two orders of magnitude reduction in evolution time-requiring only about one percent of that in conventional adiabatic schemes-while maintaining high-fidelity state generation even under strong dissipative and dephasing noise. This approach not only demonstrates the power of artificial intelligence in quantum control but also establishes a scalable and experimentally feasible route toward fault-tolerant bosonic quantum computation. Beyond the specific application to bosonic code preparation, our results suggest a general paradigm for integrating machine learning and Floquet engineering to overcome decoherence challenges in next-generation quantum technologies.

quant-ph↗

arXiv2024

Quantum versatility in PageRank

Quantum mechanics empowers the emergence of quantum advantages in various fields, including quantum algorithms. Quantum PageRank is a promising tool for a future quantum internet. Recently, arbitrary phase rotations (APR) have been introduced in the underlying Szegedy's quantum walk of quantum PageRank algorithm. In this work, we thoroughly study the role APR plays in quantum PageRank. We discover the versatility resulting from quantumness. Specifically, we discover the emergence of a cluster phenomenon in rankings considering the rotation phases, i.e. the existence of similar clusters in the distribution of the rankings and their fidelity with the corresponding classical PageRanks, the ranking distribution variance, the coherence and entanglement of PageRank states, and the power law parameter in the ranking distributions on a scale-free network concerning the two rotation phases. Furthermore, we propose an alternate quantum PageRank with APR which provides an extra tunnel for the analysis of PageRank. We also study the PageRank on the trackback graph of a scale-free graph for the investigation of network information traffic tracking. We demonstrate the rich cluster diversity formed in our alternate quantum PageRank, which offers a novel perspective on the quantum versatility of PageRank. Our results present the quantum-enabled perspective for PageRanking and shed light on the design and application of practical quantum PageRank algorithms.

quant-ph↗