6 papers
Data-Efficient and Interpretable Classification of Circulating Tumor Cell Phenotypes in Microfluidic Devices via Deep Learning
Serena Su, Yifan Wang, Senwei Liang
Accurate classification of circulating tumor cell (CTC) phenotypes can provide valuable information for assessing metastatic potential. Label free microfluidic devices provide a hy…
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…
Exploring the Nexus of Many-Body Theories through Neural Network Techniques: the Tangent Model
Senwei Liang, Karol Kowalski, Chao Yang +1
In this paper, we present a physically informed neural network representation of the effective interactions associated with coupled-cluster downfolding models to describe chemical…
Effective Many-body Interactions in Reduced-Dimensionality Spaces Through Neural Network Models
Senwei Liang, Karol Kowalski, Chao Yang +1
Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head-on. Therefore, develop…
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,…
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations
Hardeep Bassi, Yuanran Zhu, Senwei Liang +4
In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-di…