5 papers
Advancing quantum imaging through learning theory
Yunkai Wang, Changhun Oh, Junyu Liu +2
We study quantum imaging by applying the resolvable expressive capacity (REC) formalism developed for physical neural networks (PNNs). In this paradigm of quantum learning, the ima…
SU(d)-Symmetric Random Unitaries: Quantum Scrambling, Error Correction, and Machine Learning
Zimu Li, Han Zheng, Yunfei Wang +3
Quantum information processing in the presence of continuous symmetry is of wide importance and exhibits many novel physical and mathematical phenomena. SU(d) is a continuous group…
Quantum-data-driven dynamical transition in quantum learning
Bingzhi Zhang, Junyu Liu, Liang Jiang +1
Quantum neural networks, parameterized quantum circuits optimized under a specific cost function, provide a paradigm for achieving near-term quantum advantage in quantum informatio…
Designs from Local Random Quantum Circuits with SU(d) Symmetry
Zimu Li, Han Zheng, Junyu Liu +2
The generation of -designs (pseudorandom distributions that emulate the Haar measure up to moments) with local quantum circuit ensembles is a problem of fundamental importan…
Dynamical transition in controllable quantum neural networks with large depth
Bingzhi Zhang, Junyu Liu, Xiao-Chuan Wu +2
Understanding the training dynamics of quantum neural networks is a fundamental task in quantum information science with wide impact in physics, chemistry and machine learning. In…