155 citations · 592 across the 17 of their papers we have counts for
19 papers
Matrix-product-state GPLHR for non-Hermitian many-body eigenproblems
Xingyao Wu, Zhangyu Yang, Yanbin Chen +2
Computing multiple eigenstates of non-Hermitian many-body operators plays an important role in investigating excitation spectra, spectral properties, and relaxation dynamics in int…
Modeling and Forecasting Realized Volatility with Multivariate Fractional Brownian Motion
Markus Bibinger, Jun Yu, Chen Zhang
A multivariate fractional Brownian motion (mfBm) with component-wise Hurst exponents is used to model and forecast realized volatility (RV). We investigate the interplay between co…
Quantum Compiling with Reinforcement Learning on a Superconducting Processor
Z. T. Wang, Qiuhao Chen, Yuxuan Du +16
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a…
TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework
Yaocheng Chen, Chung-Yun Kuo, Yuxuan Du +2
TeD-Q is an open-source software framework for quantum machine learning, variational quantum algorithm (VQA), and simulation of quantum computing. It seamlessly integrates classica…
Recent Advances for Quantum Neural Networks in Generative Learning
Jinkai Tian, Xiaoyu Sun, Yuxuan Du +11
Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is thr…
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
Qiuhao Chen, Yuxuan Du, Qi Zhao +3
Efficient quantum compiling tactics greatly enhance the capability of quantum computers to execute complicated quantum algorithms. Due to its fundamental importance, a plethora of…
Quantum circuit architecture search on a superconducting processor
Kehuan Linghu, Yang Qian, Ruixia Wang +14
Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. How…
The dilemma of quantum neural networks
Yang Qian, Xinbiao Wang, Yuxuan Du +2
The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bound than their classical counterparts to ensure better reliab…
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Justyna P. Zwolak, Sandesh S. Kalantre, Xingyao Wu +2
Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery…
Multiparty quantum data hiding with enhanced security and remote deletion
Xingyao Wu, Jianxin Chen
One of the applications of quantum technology is to use quantum states and measurements to communicate which offers more reliable security promises. Quantum data hiding, which give…
Machine Learning techniques for state recognition and auto-tuning in quantum dots
Sandesh S. Kalantre, Justyna P. Zwolak, Stephen Ragole +4
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good…
Exponential improvements for quantum-accessible reinforcement learning
Vedran Dunjko, Yi-Kai Liu, Xingyao Wu +1
Quantum computers can offer dramatic improvements over classical devices for data analysis tasks such as prediction and classification. However, less is known about the advantages…
Geometry of the set of quantum correlations
Koon Tong Goh, Jędrzej Kaniewski, Elie Wolfe +5
It is well known that correlations predicted by quantum mechanics cannot be explained by any classical (local-realistic) theory. The relative strength of quantum and classical corr…
Non-local games and optimal steering at the boundary of the quantum set
Yi-Zheng Zhen, Koon Tong Goh, Yu-Lin Zheng +4
The boundary between classical and quantum correlations is well characterised by linear constraints called Bell inequalities. It is much harder to characterise the boundary of the…
Device-independent parallel self-testing of two singlets
Xingyao Wu, Jean-Daniel Bancal, Matthew McKague +1
Device-independent self-testing is the possibility of certifying the quantum state and the measurements, up to local isometries, using only the statistics observed by querying unch…
All the self-testings of the singlet for two binary measurements
Yukun Wang, Xingyao Wu, Valerio Scarani
Self-testing refers to the possibility of characterizing uniquely (up to local isometries) the state and measurements contained in quantum devices, based only on the observed input…
Robust self testing of the 3-qubit state
Xingyao Wu, Yu Cai, Tzyh Haur Yang +3
Self-testing is a device independent method which can be used to determine the nature of a physical system or device, without knowing any detail of the inner mechanism or the physi…
Maximal tree size of few-qubit states
Huy Nguyen Le, Yu Cai, Xingyao Wu +2
Tree size () is an interesting measure of complexity for multiqubit states: not only is it in principle computable, but one can obtain lower bounds for it. In this way, it…
Tree-size complexity of multiqubit states
Huy Nguyên Lê, Yu Cai, Xingyao Wu +1
Complexity is often invoked alongside size and mass as a characteristic of macroscopic quantum objects. In 2004, Aaronson introduced the \textit{tree size} (TS) as a computable mea…