Papers
Live trends in quantum computing research, updated daily from arXiv.
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Revealing the working mechanism of quantum neural networks by mutual information
Xin Zhang, Yuexian Hou·Apr 30, 2024
Quantum neural networks (QNNs) is a parameterized quantum circuit model, which can be trained by gradient-based optimizer, can be used for supervised learning, regression tasks, combinatorial optimization, etc. Although many works have demonstrated t...
Red-QAOA: Efficient Variational Optimization through Circuit Reduction
Meng Wang, B. Fang, Ang Li +1 more·Apr 27, 2024
The Quantum Approximate Optimization Algorithm (QAOA) addresses combinatorial optimization challenges by converting inputs to graphs. However, the optimal parameter searching process of QAOA is greatly affected by noise. Larger problems yield bigger ...
Optimal depth and a novel approach to variational unitary quantum process tomography
Vladlen Galetsky, Pol Julià Farré, Soham Ghosh +2 more·Apr 25, 2024
In this work, we present two new methods for variational quantum circuit (VQC) process tomography (PT) onto n qubits systems: unitary PT based on VQCs (PT_VQC) and unitary evolution-based variational quantum singular value decomposition (U-VQSVD). Co...
Quantum-Enhanced Neural Exchange-Correlation Functionals
Igor O. Sokolov, Gert-Jan Both, Art D. Bochevarov +6 more·Apr 22, 2024
Kohn-Sham Density Functional Theory (KS-DFT) provides the exact ground state energy and electron density of a molecule, contingent on the as-yet-unknown universal exchange-correlation (XC) functional. Recent research has demonstrated that neural netw...
Multi-Class Quantum Convolutional Neural Networks
Marco Mordacci, Davide Ferrari, Michele Amoretti·Apr 19, 2024
Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The m...
A hybrid Quantum-Classical Algorithm for Mixed-Integer Optimization in Power Systems
P. Ellinas, Samuel Chevalier, Spyros Chatzivasileiadis·Apr 16, 2024
Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and Optimal Transmission Switching to verifying Neural Networks for power ...
Symmetry-guided gradient descent for quantum neural networks
Ka Bian, Shitao Zhang, Fei Meng +2 more·Apr 9, 2024
Many supervised learning tasks have intrinsic symmetries, such as translational and rotational symmetry in image classifications. These symmetries can be exploited to enhance performance. We formulate the symmetry constraints into a concise mathemati...
Quantum Graph Optimization Algorithm
Yuhan Huang, Ferris Prima Nugraha, Siyuan Jin +3 more·Apr 9, 2024
Quadratic unconstrained binary optimization (QUBO) tasks are very important in chemistry, finance, job scheduling, and so on, which can be represented using graph structures, with the variables as nodes and the interaction between them as edges. Vari...
Hamiltonian learning using machine-learning models trained with continuous measurements
Kris Tucker, Amit Kiran Rege, Conor Smith +2 more·Apr 8, 2024
We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the weak measure...
Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification
Zuyu Xu, Kang Shen, Pengnian Cai +8 more·Apr 1, 2024
The recent emergence of the hybrid quantum-classical neural network (HQCNN) architecture has garnered considerable attention due to the potential advantages associated with integrating quantum principles to enhance various facets of machine learning ...
Muon/pion identification at BESIII based on variational quantum classifier
Zhipeng Yao, Xingtao Huang, Teng Li +3 more·Apr 1, 2024
In collider physics experiments, particle identification (PID), i.e., the identification of the charged particle species in the detector is usually one of the most crucial tools in data analysis. In the past decade, machine learning techniques have g...
Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
Changwon Lee, Israel F. Araujo, Dongha Kim +4 more·Mar 28, 2024
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This approach is particularly attractive due to the absence of the barren pl...
Leveraging Quantum Superposition to Infer the Dynamic Behavior of a Spatial-Temporal Neural Network Signaling Model
G. A. Silva·Mar 27, 2024
The exploration of new problem classes for quantum computation is an active area of research. In this paper, we introduce and solve a novel problem class related to dynamics on large-scale networks relevant to neurobiology and machine learning. Speci...
Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.
Anthony M. Smaldone, Victor S. Batista·Mar 27, 2024
Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled...
A neural network approach for two-body systems with spin and isospin degrees of freedom
Chuanxin Wang, Tomoya Naito, Jian Li +1 more·Mar 25, 2024
We propose an enhanced machine learning method to calculate the ground state of two-body systems. By extending the original method [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)], the present method enables consideration of the sp...
Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series
Junggu Choi, Tak Hur, Daniel K. Park +4 more·Mar 16, 2024
Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for cl...
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
Raffaele Marino, L. Buffoni, Bogdan Zavalnij·Mar 13, 2024
This manuscript provides a comprehensive review of the Maximum Clique Problem, a computational problem that involves finding subsets of vertices in a graph that are all pairwise adjacent to each other. The manuscript covers in a simple way classical ...
Application of Quantum Tensor Networks for Protein Classification
Debarshi Kundu, Archisman Ghosh, Srinivasan Ekambaram +3 more·Mar 11, 2024
Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quan...
Distributed quantum architecture search
Haozhen Situ, Zhimin He, Shenggen Zheng +1 more·Mar 10, 2024
Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing. However, designing efficient parameterized quantum circuits remains a challenge. Quantum architecture search tackles this by adjusting cir...
Jet Discrimination with Quantum Complete Graph Neural Network
Yi-An Chen, Kai-Feng Chen·Mar 8, 2024
Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications. Furthermore, the extension of machine learning to quantum computers has given rise to the emer...