571 citations · 861 across the 18 of their papers we have counts for
19 papers
Learning Restricted Boltzmann Machines with greedy quantum search
Liming Zhao, Aman Agrawal, Patrick Rebentrost
Restricted Boltzmann Machines (RBMs) are widely used probabilistic undirected graphical models with visible and latent nodes, playing an important role in statistics and machine le…
Provable learning of quantum states with graphical models
Liming Zhao, Naixu Guo, Ming-Xing Luo +1
The complete learning of an -qubit quantum state requires samples exponentially in . Several works consider subclasses of quantum states that can be learned in polynomial sam…
MomentDiff: Generative Video Moment Retrieval from Random to Real
Pandeng Li, Chen-Wei Xie, Hongtao Xie +5
Video moment retrieval pursues an efficient and generalized solution to identify the specific temporal segments within an untrimmed video that correspond to a given language descri…
Quantum algorithm for Markov Random Fields structure learning by information theoretic properties
Liming Zhao, Lin-chun Wan, Ming-Xing Luo
Probabilistic graphical models play a crucial role in machine learning and have wide applications in various fields. One pivotal subset is undirected graphical models, also known a…
Quantum algorithm for structure learning of Markov Random Fields
Liming Zhao, Siyi Yang, Patrick Rebentrost
Markov random fields (MRFs) appear in many problems in machine learning and statistics. From a computational learning theory point of view, a natural problem of learning MRFs arise…
Quantum-classical algorithms for skewed linear systems with optimized Hadamard test
Bujiao Wu, Maharshi Ray, Liming Zhao +2
The solving of linear systems provides a rich area to investigate the use of nearer-term, noisy, intermediate-scale quantum computers. In this work, we discuss hybrid quantum-class…
Weakly Supervised Learning with Side Information for Noisy Labeled Images
Lele Cheng, Xiangzeng Zhou, Liming Zhao +5
In many real-world datasets, like WebVision, the performance of DNN based classifier is often limited by the noisy labeled data. To tackle this problem, some image related side inf…
Bayesian inference based process design and uncertainty analysis of simulated moving bed chromatographic systems
Qiao-Le He, Liming Zhao
Prominent features of simulated moving bed (SMB) chromatography processes in the downstream processing is based on the determination of operating conditions. However, effects of di…
Model-based process design of a ternary protein separation using multi-step gradient ion-exchange SMB chromatography
Qiao-Le He, Eric von Lieres, Zhaoxi Sun +1
Model-based process design of ion-exchange simulated moving bed (IEX-SMB) chromatography for center-cut separation of proteins is studied. Use of nonlinear binding models that desc…
Compiling basic linear algebra subroutines for quantum computers
Liming Zhao, Zhikuan Zhao, Patrick Rebentrost +1
Efficiently processing basic linear algebra subroutines is of great importance for a wide range of computational problems. In this paper, we consider techniques to implement matrix…
Variational Quantum Circuit Model for Knowledge Graphs Embedding
Yunpu Ma, Volker Tresp, Liming Zhao +1
In this work, we propose the first quantum Ansätze for the statistical relational learning on knowledge graphs using parametric quantum circuits. We introduce two types of variatio…
Measurement-driven quantum computing: Performance of a 3-SAT solver
Simon C. Benjamin, Liming Zhao, Joseph F. Fitzsimons
We investigate the performance of a quantum algorithm for solving classical 3-SAT problems. A cycle of post-selected measurements drives the computer's register monotonically towar…
Deeply-Learned Part-Aligned Representations for Person Re-Identification
Liming Zhao, Xi Li, Jingdong Wang +1
In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human…
A measurement driven analog of adiabatic quantum computation for frustration-free Hamiltonians
Liming Zhao, Carlos A. Perez-Delgado, Simon C. Benjamin +1
The adiabatic quantum algorithm has drawn intense interest as a potential approach to accelerating optimization tasks using quantum computation. The algorithm is most naturally rea…
Deep Convolutional Neural Networks with Merge-and-Run Mappings
Liming Zhao, Jingdong Wang, Xi Li +2
A deep residual network, built by stacking a sequence of residual blocks, is easy to train, because identity mappings skip residual branches and thus improve information flow. To f…
Fast graph operations in quantum computation
Liming Zhao, Carlos A. Pérez-Delgado, Joseph F. Fitzsimons
The connection between certain entangled states and graphs has been heavily studied in the context of measurement-based quantum computation as a tool for understanding entanglement…
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
Xi Li, Liming Zhao, Lina Wei +5
A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner. In this paper, we propose a multi-task dee…
Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking
Liming Zhao, Xi Li, Jun Xiao +2
As an important and challenging problem in computer vision and graphics, keypoint-based object tracking is typically formulated in a spatio-temporal statistical learning framework.…
Genuine Correlations of Tripartite System
Liming Zhao, Xueyuan Hu, R. -H. Yue +1
We define genuine total, classical and quantum correlations in tripartite systems. The measure we propose is based on the idea that genuine tripartite correlation exists if and onl…