Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Quantum Entanglement Path Selection and Qubit Allocation via Adversarial Group Neural Bandits
Yingyu Huang, Lei Wang, Jie Xu·Nov 1, 2024
Quantum Data Networks (QDNs) have emerged as a promising framework in the field of information processing and transmission, harnessing the principles of quantum mechanics. QDNs utilize a quantum teleportation technique through long-distance entanglem...
Quantum Deep Equilibrium Models
P. Schleich, Marta Skreta, L. B. Kristensen +2 more·Oct 31, 2024
The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth incr...
Method for noise-induced regularization in quantum neural networks
Viacheslav Kuzmin, Wilfrid Somogyi, Ekaterina Pankovets +1 more·Oct 25, 2024
In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the general objective is to reduce the amount of noise qubits are subject to, a...
Adiabatic training for Variational Quantum Algorithms
Ernesto Acosta, C. C. Gutierrez, Guillermo Botella Juan +1 more·Oct 24, 2024
This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA)...
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning
Nguyen Van Huynh, Bolun Zhang, Dinh-Hieu Tran +5 more·Oct 23, 2024
Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. ...
Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification
S. Yousuf, Iqbal Tomal, Abdullah Al Shafin +2 more·Oct 21, 2024
This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using an...
Quantum Neural Network for Accelerated Magnetic Resonance Imaging
Shuo Zhou, Yihang Zhou, Congcong Liu +4 more·Oct 12, 2024
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum ...
Non-binary artificial neuron with phase variation implemented on a quantum computer
Jhordan Silveira de Borba, Jonas Maziero·Oct 10, 2024
The first artificial quantum neuron models followed a similar path to classic models and they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test...
Quantum optical neural networks with programmable nonlinearities
E. Chernykh, M. Saygin, G. Struchalin +2 more·Oct 10, 2024
Parametrized quantum circuits are essential components of variational quantum algorithms. Until now, optical implementations of these circuits have relied solely on adjustable linear optical units. In this study, we demonstrate that using programmabl...
Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
Daniele Lizzio Bosco, Beatrice Portelli, Giuseppe Serra·Oct 8, 2024
Image processing is one of the most promising applications for quantum machine learning. Quanvolutional neural networks with nontrainable parameters are the preferred solution to run on current and near future quantum devices. The typical input prepr...
Quantum Error Propagation
E. Sultanow, Fation Selimllari, Siddhant Dutta +3 more·Oct 7, 2024
Data poisoning attacks on machine learning models aim to manipulate the data used for model training such that the trained model behaves in the attacker's favour. In classical models such as deep neural networks, large chains of dot products do indee...
Resource-efficient equivariant quantum convolutional neural networks
Koki Chinzei, Quoc Hoan Tran, Yasuhiro Endo +1 more·Oct 2, 2024
Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities. Despite theoretical developments in equivariant QNNs, their implementation on near-term quantum devices rema...
Universal logical quantum photonic neural network processor via cavity-assisted interactions
J. Basani, M. Niu, E. Waks·Oct 2, 2024
Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over bosonic encodings using native photonic h...
Quantum-data-driven dynamical transition in quantum learning
Bingzhi Zhang, Junyu Liu, Liang Jiang +1 more·Oct 2, 2024
Quantum neural networks, parameterized quantum circuits optimized under a specific cost function, provide a paradigm for achieving near-term quantum advantage in quantum information processing. Understanding QNN training dynamics is crucial for optim...
Polarization and Orbital Angular Momentum Encoded Quantum Toffoli Gate Enabled by Diffractive Neural Networks.
Qianke Wang, Dawei Lyu, Jun Liu +1 more·Sep 30, 2024
Controlled quantum gates play a crucial role in enabling quantum universal operations by facilitating interactions between qubits. Direct implementation of three-qubit gates simplifies the design of quantum circuits, thereby being conducive to perfor...
Edge Intelligence in Satellite-Terrestrial Networks With Hybrid Quantum Computing
Siyue Huang, Lifeng Wang, Xin Wang +3 more·Sep 30, 2024
This letter exploits the potential of edge intelligence empowered satellite-terrestrial networks, where users’ computation tasks are offloaded to the satellites or terrestrial base stations. The computation task offloading in such networks involves t...
Experimental Online Quantum Dots Charge Autotuning Using Neural Networks
Victor Yon, Bastien Galaup, C. Rohrbacher +11 more·Sep 30, 2024
Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional n...
ANN-enhanced detection of multipartite entanglement in a three-qubit NMR quantum processor
Vaishali Gulati, Shivanshu Siyanwal, Arvind +1 more·Sep 29, 2024
We use an artificial neural network (ANN) model to identify the entanglement class of an experimentally generated three-qubit pure state drawn from one of the six inequivalent classes under stochastic local operations and classical communication (SLO...
Subspace preserving quantum convolutional neural network architectures
Léo Monbroussou, Jonas Landman, Letao Wang +2 more·Sep 27, 2024
Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Those algorithms have gained extensive interest as they can offer polynom...
Quantum DeepONet: Neural operators accelerated by quantum computing
Peng Xiao, Muqing Zheng, Anran Jiao +2 more·Sep 24, 2024
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep opera...