Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Effective temperature in approximate quantum many-body states
Yu-Qin Chen, Shi-Xin Zhang·Nov 28, 2024
In the pursuit of numerically identifying ground states of quantum many-body systems, approximate quantum wave function ansatzes are commonly employed. This study focuses on the spectral decomposition of these approximate quantum many-body states int...
Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement
Julien Dudas, Baptiste Carles, Elie Gouzien +2 more·Nov 28, 2024
Quantum neural networks promise to extend the power of machine learning into the quantum domain, with potential applications ranging from automatic recognition of quantum states to the control of quantum devices. However, their physical implementatio...
High-Level Surface Code Decoding via Parallel FFNNs on CIM Platforms
Hao Wang, Erjia Xiao, Wenbo Mu +9 more·Nov 26, 2024
Due to the high sensitivity of qubits to environmental noise, which leads to decoherence and information loss, active quantum error correction(QEC) is essential. Surface codes represent one of the most promising fault-tolerant QEC schemes, but they r...
Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks.
Hao Ai, Yu-xi Liu·Nov 25, 2024
To demonstrate supremacy of quantum computing, increasingly large-scale superconducting quantum computing chips are being designed and fabricated. However, the complexity of simulating quantum systems poses a significant challenge to computer-aided d...
Divergence-free algorithms for solving nonlinear differential equations on quantum computers
Katsuhiro Endo, Kazuaki Z. Takahashi·Nov 25, 2024
From weather to neural networks, modeling is not only useful for understanding various phenomena, but also has a wide range of potential applications. Although nonlinear differential equations are extremely useful tools in modeling, their solutions a...
A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow
Yuji Cao, Yue Chen, Yan Xu·Nov 25, 2024
The stochastic nature of renewable energy and load demand requires efficient and accurate solutions for probabilistic optimal power flow (OPF). Quantum neural networks (QNNs), which combine quantum computing and machine learning, offer computational ...
NN-AE-VQE: Neural network parameter prediction on autoencoded variational quantum eigensolvers
Koen J. Mesman, Yinglu Tang, Matthias Moller +2 more·Nov 23, 2024
A longstanding computational challenge is the accurate simulation of many-body particle systems. Especially for deriving key characteristics of high-impact but complex systems such as battery materials and high entropy alloys (HEA). While simple mode...
EQNN: Enhanced Quantum Neural Network — A Case Study of Mobile Data Usage Prediction
Abel C. H. Chen·Nov 21, 2024
With the maturation of quantum computing technology, research has gradually shifted towards exploring its applications. Alongside the rise of artificial intelligence, various machine learning methods have been developed into quantum circuits and algo...
Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks
Jun Yong Khoo, Chee Kwan Gan, W.-Q. Ding +3 more·Nov 20, 2024
Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. This paper investigates the performance of QCNNs in comparison to the hardware-efficient...
Exact quantum algorithm for unit commitment optimization based on partially connected quantum neural networks
Jian Liu, Xu Zhou, Zhuojun Zhou +1 more·Nov 18, 2024
The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum (NISQ) era. The unit commitment (UC) problem is a fundamental problem in the fi...
Mera: Memory Reduction and Acceleration for Quantum Circuit Simulation via Redundancy Exploration
Yuhong Song, E. Sha, Longshan Xu +2 more·Nov 18, 2024
With the development of quantum computing, quantum processor demonstrates the potential supremacy in specific applications, such as Grover's database search and popular quantum neural networks (QNNs). For better calibrating the quantum algorithms and...
Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance
Ishita Agarwal, T. Patti, Rodrigo Araiza Bravo +2 more·Nov 13, 2024
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the core of QNC is the quantum perceptron (QP), which leverages the analog d...
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Jun Qi, Chao-Han Yang, S. Y. Chen +3 more·Nov 13, 2024
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This techn...
Efficient Classical Computation of Single-Qubit Marginal Measurement Probabilities to Simulate Certain Classes of Quantum Algorithms
S. Y. Pradata, ’Anin N. ’Azhiim, Hendry M. Lim +1 more·Nov 11, 2024
Classical simulations of quantum circuits are essential for verifying and benchmarking quantum algorithms, particularly for large circuits, where computational demands increase exponentially with the number of qubits. Among available methods, the cla...
Quantum Neural Network Classifier for Cancer Registry System Testing: A Feasibility Study
Xinyi Wang, Shaukat Ali, Paolo Arcaini +2 more·Nov 7, 2024
With the rapid advancement of quantum computing, research on quantum machine learning (QML) algorithms has grown significantly. Among these, the Quantum Neural Network (QNN) stands out as one of the promising algorithms that integrates the principles...
Expressivity of deterministic quantum computation with one qubit
Yujin Kim, Daniel K. Park·Nov 5, 2024
Deterministic quantum computation with one qubit (DQC1) is of significant theoretical and practical interest due to its computational advantages in certain problems, despite its subuniversality with limited quantum resources. In this work, we introdu...
Information plane and compression-gnostic feedback in quantum machine learning
Nathan Haboury, Mohammad Kordzanganeh, Alexey A. Melnikov +1 more·Nov 4, 2024
The information plane (Tishby et al. arXiv:physics/0004057, Shwartz-Ziv et al. arXiv:1703.00810) has been proposed as an analytical tool for studying the learning dynamics of neural networks. It provides quantitative insight on how the model approach...
Assessing Superposition-Targeted Coverage Criteria for Quantum Neural Networks
Minqi Shao, Jianjun Zhao·Nov 3, 2024
Quantum Neural Networks (QNNs) have achieved initial success in various tasks by integrating quantum computing and neural networks. However, growing concerns about their reliability and robustness highlight the need for systematic testing. Unfortunat...
Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
Md Abrar Jahin, Md Akmol Masud, Md Wahiduzzaman Suva +2 more·Nov 3, 2024
The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particle physics. Quantum machine learning, with its capability to leverage t...
Neural-Network-Based Design of Approximate Gottesman-Kitaev-Preskill Code.
Yexiong Zeng, Wei Qin, Ye‐Hong Chen +2 more·Nov 2, 2024
Gottesman-Kitaev-Preskill (GKP) encoding holds promise for continuous-variable fault-tolerant quantum computing. While an ideal GKP encoding is abstract and impractical due to its nonphysical nature, approximate versions provide viable alternatives. ...