Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Pediatric TSC-Related Epilepsy Classification from Clinical Mr Images Using Quantum Neural Network
Ling Lin, Yihang Zhou, Zhanqi Hu +8 more·May 27, 2024
Tuberous sclerosis complex (TSC) presents as a multisystem disorder with profound neurological impacts. Our study introduces Quantum-Residual Neural Network (QR-Net), a novel quantum neural network model that innovatively combines conventional residu...
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection
Yifeng Peng, Xinyi Li, Zhiding Liang +1 more·May 25, 2024
Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in q...
Unsupervised Deep Neural Network Approach To Solve Bosonic Systems
Avishek Singh, NIRMAL K. Ganguli·May 24, 2024
The simulation of quantum many-body systems poses a significant challenge in physics due to the exponential scaling of Hilbert space with the number of particles. Traditional methods often struggle with large system sizes and frustrated lattices. In ...
Machine learning of reduced quantum channels on noisy intermediate-scale quantum devices
Giovanni Cemin, Marcel Cech, Erik Weiss +4 more·May 21, 2024
World-wide efforts aim at the realization of advanced quantum simulators and processors. However, despite the development of intricate hardware and pulse control systems, it may still not be generally known which effective quantum dynamics, or channe...
Quantum resonant dimensionality reduction
Fan Yang, Furong Wang, Xusheng Xu +4 more·May 21, 2024
Quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage. Here, we propose...
Quantum Positional Encodings for Graph Neural Networks
Slimane Thabet, Mehdi Djellabi, Igor Sokolov +3 more·May 21, 2024
In this work, we propose novel families of positional encodings tailored to graph neural networks obtained with quantum computers. These encodings leverage the long-range correlations inherent in quantum systems that arise from mapping the topology o...
An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data
Navneet Singh, Shiva Raj Pokhrel·May 16, 2024
In this paper, we explore the power of Quantum Machine Learning as we extend, implement and evaluate algorithms like Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) in Qisk...
Measurement-based quantum machine learning
Luis Mantilla Calder'on, R. Raussendorf, P. Feldmann +1 more·May 14, 2024
Quantum machine learning (QML) leverages quantum computing for classical inference, furnishes the processing of quantum data with machine-learning methods, and provides quantum algorithms adapted to noisy devices. Typically, QML proposals are framed ...
Efficient and Scalable Architectures for Multi-level Superconducting Qubit Readout
Chaithanya Naik Mude, Satvik Maurya, Benjamin Lienhard +1 more·May 14, 2024
Realizing the full potential of quantum computing requires large-scale quantum computers capable of running quantum error correction (QEC) to mitigate hardware errors and maintain quantum data coherence. While quantum computers operate within a two-l...
Hamiltonian-Based Quantum Reinforcement Learning for Neural Combinatorial Optimization
G. Kruse, Rodrigo Coehlo, A. Rosskopf +2 more·May 13, 2024
Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range ...
Barren plateaus are amplified by the dimension of qudits
Lucas Friedrich, Tiago de Souza Farias, Jonas Maziero·May 13, 2024
Variational quantum algorithms (VQAs) have emerged as pivotal strategies for attaining quantum advantage in diverse scientific and technological domains, notably within quantum neural networks. However, despite their potential, VQAs encounter signifi...
Continuous-variable quantum Boltzmann machine
Shikha Bangar, Leanto Sunny, Kubra Yeter-Aydeniz +1 more·May 10, 2024
We propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. It can be realized experimentally on a continuous-variable (CV) photonic quantum computer. We used a CV quantum imaginary time evolution ...
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Shamminuj Aktar, Andreas Bartschi, Diane Oyen +2 more·May 9, 2024
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full pot...
Hybrid Quantum Graph Neural Network for Molecular Property Prediction
Michael Vitz, Hamed Mohammadbagherpoor, S. Sandeep +3 more·May 8, 2024
To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have demonstrated abi...
Cross-Platform Autonomous Control of Minimal Kitaev Chains
D. V. Driel, Rouven Koch, Vincent P. M. Sietses +21 more·May 7, 2024
Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here,...
Beyond the Buzz: Strategic Paths for Enabling Useful NISQ Applications
P. Hegde, O. Kyriienko, H. Heimonen +6 more·May 7, 2024
There is much debate on whether quantum computing on current NISQ devices, consisting of noisy hundred qubits and requiring a non-negligible usage of classical computing as part of the algorithms, has utility and will ever offer advantages for scient...
Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control
Jacob R. Taylor, S. Das Sarma·May 7, 2024
Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is potential to learn...
Transformer models for quantum gate set tomography
King Yiu Yu, Aritra Sarkar, M. Rimbach-Russ +2 more·May 3, 2024
Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challen...
Quantum Machine Learning: Quantum Kernel Methods
S. Naguleswaran·May 2, 2024
Quantum algorithms based on quantum kernel methods have been investigated previously [1]. A quantum advantage is derived from the fact that it is possible to construct a family of datasets for which, only quantum processing can recognise the intrinsi...
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...