Papers
Live trends in quantum computing research, updated daily from arXiv.
Total Papers
27,548
This Month
1,041
Today
0
Research Volume
12,932 papers in 12 months (-5% vs prior quarter)
Research Focus Areas
Papers by research theme (12 months). Hover for details.
Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits
Yuqing Li, Jinglei Cheng, Xulong Tang +3 more·Dec 16, 2024
Quantum machine learning is considered one of the flagship applications of quantum computers, where variational quantum circuits could be the leading paradigm both in the near-term quantum devices and the early fault-tolerant quantum computers. Howev...
Regression and Classification with Single-Qubit Quantum Neural Networks
Leandro C. Souza, B. C. Guingo, Gilson A. Giraldi +1 more·Dec 12, 2024
Since classical machine learning has become a powerful tool for developing data-driven algorithms, quantum machine learning is expected to similarly impact the development of quantum algorithms. The literature reflects a mutually beneficial relations...
Data efficient prediction of excited-state properties using quantum neural networks
Manuel Hagelueken, Marco F Huber, Marco Roth·Dec 12, 2024
Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their ground state counterparts. We ...
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning
Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu +1 more·Dec 12, 2024
In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrating quantum computing principl...
Measurement-based quantum convolutional neural network for deep learning
Yifan Sun, Xiangdong Zhang·Dec 11, 2024
Recently, quantum convolutional neural networks (QCNNs) are proposed, harnessing the power of quantum computing for faster training compared to the classical counterparts. However, this framework for deep learning also relies on multiple processing l...
Predicting Chaotic Systems with Quantum Echo-state Networks
Erik L. Connerty, Ethan N. Evans, Gerasimos Angelatos +1 more·Dec 10, 2024
Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum ...
Development of neural network-based optimal control pulse generator for quantum logic gates using the GRAPE algorithm in NMR quantum computer
Ebrahim Khaleghian, Arash Fath Lipaei, A. Bahrampour +2 more·Dec 8, 2024
In this paper, we introduce a neural network to generate optimal control pulses for general single-qubit quantum logic gates, within a Nuclear Magnetic Resonance (NMR) quantum computer. By utilizing a neural network, we can efficiently implement any ...
Quantum network tomography of small Rydberg arrays by machine learning
Kaustav Mukherjee, Johannes Schachenmayer, S. Whitlock +1 more·Dec 7, 2024
Configurable arrays of optically trapped Rydberg atoms are a versatile platform for quantum computation and quantum simulation, also allowing controllable decoherence. We demonstrate theoretically, that they also enable proof-of-principle demonstrati...
Universal 2-Local Symmetry-Preserving Quantum Neural Networks for Fermionic Systems
Ge Yan, Kaisen Pan, Ruocheng Wang +3 more·Dec 6, 2024
Simulating quantum many-body systems represents a fundamental challenge where classical machine learning methods are severely bottlenecked by the exponential curse of dimensionality. Variational Quantum Algorithms (VQAs) offer a native paradigm to ta...
Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices
Alberto Marchisio, Emman Sychiuco, Muhammad Kashif +1 more·Dec 6, 2024
The rapid advancement in Quantum Computing, particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML) applications. Despite their potential, fully-quantum algorithms remai...
Computational Advantage in Hybrid Quantum Neural Networks: Myth or Reality?
Muhammad Kashif, Alberto Marchisio, Muhammad Shafique·Dec 6, 2024
Hybrid Quantum Neural Networks (HQNNs), under the umbrella of Quantum Machine Learning (QML), have garnered significant attention due to their potential to enhance computational performance by integrating quantum layers within traditional neural netw...
A Novel Single-Layer Quantum Neural Network for Approximate SRBB-Based Unitary Synthesis
Giacomo Belli, Marco Mordacci, Michele Amoretti·Dec 4, 2024
In this work, a novel quantum neural network is introduced as a means to approximate any unitary evolution through the Standard Recursive Block Basis (SRBB) and is subsequently redesigned with the number of CNOTs asymptotically reduced by an exponent...
Lean Classical‐Quantum Hybrid Neural Network Model for Image Classification
A. Liu, Cuihong Wen, Jieci Wang·Dec 3, 2024
The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machine learning algorithms for image classification predominantly relies o...
GQWformer: A Quantum-based Transformer for Graph Representation Learning
Lei Yu, Hongyang Chen, Jingsong Lv +1 more·Dec 3, 2024
Graph Transformers (GTs) have demonstrated significant advantages in graph representation learning through their global attention mechanisms. However, the self-attention mechanism in GTs tends to neglect the inductive biases inherent in graph structu...
Quantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement
An Ning, Tai-Yue Li, Nan-Yow Chen·Dec 2, 2024
In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages the strengths of pointwise convolution to efficiently integr...
Quantum Convolutional Neural Network with Flexible Stride
Kai-huan Yu, Song Lin, Bin-Bin Cai·Dec 1, 2024
Convolutional neural network is a crucial tool for machine learning, especially in the field of computer vision. Its unique structure and characteristics provide significant advantages in feature extraction. However, with the exponential growth of da...
Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation
D. Puente, Matteo Rizzi·Nov 29, 2024
This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns st...
Optimizing Quantum Embedding using Genetic Algorithm for QML Applications
Koustubh Phalak, Archisman Ghosh, Swaroop Ghosh·Nov 29, 2024
Quantum Embeddings (QE) is an important component of Quantum Machine Learning (QML) algorithms to load classical data present in Euclidean space onto quantum Hilbert space, which are then later forwarded to the Parametric Quantum Circuit (PQC) for tr...
Parametrized multiqubit gate design for neutral-atom based quantum platforms
M. Mohan, Julius de Hond, S. Kokkelmans·Nov 29, 2024
A clever choice and design of gate sets can reduce the depth of a quantum circuit, and can improve the quality of the solution one obtains from a quantum algorithm. This is especially important for near-term quantum computers that suffer from various...
Quantum feedback control with a transformer neural network architecture
Pranav Vaidhyanathan, Florian Marquardt, Mark T. Mitchison +1 more·Nov 28, 2024
Attention-based neural networks such as transformers have revolutionized various fields such as natural language processing, genomics, and vision. Here, we demonstrate the use of transformers for quantum feedback control through both a supervised and...