162 citations · 1.4k across the 47 of their papers we have counts for
49 papers
Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network
Haotian Zhang, Huifeng Zhao, Xujun Zhang +10
Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods. Deep learning-based lead optimizatio…
Neural network encoded variational quantum algorithms
Jiaqi Miao, Chang-Yu Hsieh, Shi-Xin Zhang
We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs), or NN-VQA for short, to address the challenges of implementing VQAs on no…
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
Tianyue Wang, Xujun Zhang, Odin Zhang +5
Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab i…
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
Yiheng Zhu, Zhenqiu Ouyang, Ben Liao +5
Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress h…
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Zaixi Zhang, Qi Liu, Chee-Kong Lee +2
Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine…
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Yiheng Zhu, Jialu Wu, Chaowen Hu +4
Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical s…
A non-Hermitian Ground State Searching Algorithm Enhanced by Variational Toolbox
Yu-Qin Chen, Shi-Xin Zhang, Chang-Yu Hsieh +1
Ground-state preparation for a given Hamiltonian is a common quantum-computing task of great importance and has relevant applications in quantum chemistry, computational material m…
Discrete time crystal enabled by Stark many-body localization
Shuo Liu, Shi-Xin Zhang, Chang-Yu Hsieh +2
Discrete time crystal (DTC) has recently attracted increasing attention, but most DTC models and their properties are only revealed after disorder average. In this Letter, we propo…
Variational Quantum Simulations of Finite-Temperature Dynamical Properties via Thermofield Dynamics
Chee Kong Lee, Shi-Xin Zhang, Chang-Yu Hsieh +2
The recent advancement of quantum computer hardware offers the potential to simulate quantum many-body systems beyond the capability of its classical counterparts. However, most cu…
TensorCircuit: a Quantum Software Framework for the NISQ Era
Shi-Xin Zhang, Jonathan Allcock, Zhou-Quan Wan +13
TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and bu…
Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer
Yue Wan, Benben Liao, Chang-Yu Hsieh +1
Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine lear…
Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors
Zaixi Zhang, Qi Liu, Shengyu Zhang +3
The search for new high-performance organic semiconducting molecules is challenging due to the vastness of the chemical space, machine learning methods, particularly deep learning…
Variational quantum simulation of the imaginary-time Lyapunov control for accelerating the ground-state preparation
Yu-Cheng Chen, Yu-Qin Chen, Alice Hu +2
Quantum computers have been widely speculated to offer significant advantages in obtaining the ground state of difficult Hamiltonian in chemistry and physics. In this work, we firs…
Variational Quantum-Neural Hybrid Error Mitigation
Shi-Xin Zhang, Zhou-Quan Wan, Chang-Yu Hsieh +2
Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers by suppressing quantum noise with moderate resources. It is a key factor for successfu…
Probing many-body localization by excited-state VQE
Shuo Liu, Shi-Xin Zhang, Chang-Yu Hsieh +2
Non-equilibrium physics including many-body localization (MBL) has attracted increasing attentions, but theoretical approaches of reliably studying non-equilibrium properties remai…
Variational Quantum Simulation of Chemical Dynamics with Quantum Computers
Chee-Kong Lee, Chang-Yu Hsieh, Shengyu Zhang +1
Classical simulation of real-space quantum dynamics is challenging due to the exponential scaling of computational cost with system dimensions. Quantum computer offers the potentia…
Fast Extraction of Word Embedding from Q-contexts
Junsheng Kong, Weizhao Li, Zeyi Liu +5
The notion of word embedding plays a fundamental role in natural language processing (NLP). However, pre-training word embedding for very large-scale vocabulary is computationally…
Simulation of Condensed-Phase Spectroscopy with Near-term Digital Quantum Computer
Chee-Kong Lee, Chang-Yu Hsieh, Shengyu Zhang +1
Spectroscopy is an indispensable tool in understanding the structures and dynamics of molecular systems. However computational modelling of spectroscopy is challenging due to the e…
Variational Quantum-Neural Hybrid Eigensolver
Shi-Xin Zhang, Zhou-Quan Wan, Chee-Kong Lee +3
The variational quantum eigensolver (VQE) is one of the most representative quantum algorithms in the noisy intermediate-size quantum (NISQ) era, and is generally speculated to del…
The prospects of Monte Carlo antibody loop modelling on a fault-tolerant quantum computer
Jonathan Allcock, Anna Vangone, Agnes Meyder +4
Quantum computing for the biological sciences is an area of rapidly growing interest, but specific industrial applications remain elusive. Quantum Markov chain Monte Carlo has been…
Neural Predictor based Quantum Architecture Search
Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang +1
Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum-classical hybrid computational paradigm in the ne…
Spectral Transfer Tensor Method for Non-Markovian Noise Characterization
Yu-Qin Chen, Yi-Cong Zheng, Shengyu Zhang +1
With continuing improvements on the quality of fabricated quantum devices, it becomes increasingly crucial to analyze noisy quantum process in greater details such as characterizin…
Differentiable Quantum Architecture Search
Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang +1
Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform wh…
A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics
Chee-Kong Lee, Pranay Patil, Shengyu Zhang +1
We propose a neural-network variational quantum algorithm to simulate the time evolution of quantum many-body systems. Based on a modified restricted Boltzmann machine (RBM) wavefu…