Papers
Live trends in quantum computing research, updated daily from arXiv.
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12,523 papers in 12 months (-16% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Stability Analysis of Three Coupled Kerr Oscillators: Implications for Quantum Computing
K. Chmielewski, K. Grygiel, K. Bartkiewicz·Jun 23, 2025
We investigate the classical dynamics of optical nonlinear Kerr couplers, focusing on their potential relevance to quantum computing applications. The system consists of three Kerr-type nonlinear oscillators arranged in two configurations: a triangul...
Enhancing Expressivity of Quantum Neural Networks Based on the SWAP test
Sebastian Nagies, Emiliano Tolotti, Davide Pastorello +1 more·Jun 20, 2025
Quantum neural networks (QNNs) based on parametrized quantum circuits are promising candidates for machine learning applications, yet many architectures lack clear connections to classical models, potentially limiting their ability to leverage establ...
Revealing Quantum Information Encoded in Classical Images
Otmane Ainelkitane, Brian Recktenwall-Calvet, Aasma Iqbal +1 more·Jun 20, 2025
In this study, we investigate a simple quantum pre-processing filter kernel designed with only two CNOT gates for image feature extraction. We examine the impact of these filters when combined with a classical neural network for image classification ...
Neutrino Telescope Event Classification on Quantum Computers
Pablo Rodriguez-Grasa, Pavel Zhelnin, Carlos A. Argüelles +1 more·Jun 19, 2025
Quantum computers represent a new computational paradigm with steadily improving hardware capabilities. In this article, we present the first study exploring how current quantum computers can be used to classify different neutrino event types observe...
Feedback-driven recurrent quantum neural network universality
Lukas Gonon, Rodrigo Martínez-Peña, Juan-Pablo Ortega·Jun 19, 2025
Quantum reservoir computing uses the dynamics of quantum systems to process temporal data, making it particularly well-suited for machine learning with noisy intermediate-scale quantum devices. Recent developments have introduced feedback-based quant...
A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads Prediction
J. Kumar, D. Saxena, Kishu Gupta +2 more·Jun 19, 2025
Accurate workload prediction and advanced resource reservation are indispensably crucial for managing dynamic cloud services. Traditional neural networks and deep learning models frequently encounter challenges with diverse, high-dimensional workload...
Full-stack Analog Optical Quantum Computer with A Hundred Inputs
S. Yokoyama, Atsushi Sakaguchi, W. Asavanant +11 more·Jun 19, 2025
Optical technology emerges as a highly promising platform for quantum computing, driven by its enormous potential for large-scale ultrafast computation and its integration with telecom technology. There have been intensive investigations ongoing into...
Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal
Hemanth Kannamarlapudi, Sowmya Chintalapudi·Jun 19, 2025
Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In this paper,...
Fully convolutional 3D neural network decoders for surface codes with syndrome circuit noise
S. Gicev, L. Hollenberg, Muhammad Usman·Jun 19, 2025
Artificial Neural Networks (ANNs) are a promising approach to the decoding problem of Quantum Error Correction (QEC), but have observed consistent difficulty when generalising performance to larger QEC codes. Recent scalability-focused approaches hav...
Superconducting Qubit Readout Using Next-Generation Reservoir Computing
Robert M. Kent, Benjamin Lienhard, Gregory Lafyatis +1 more·Jun 18, 2025
Quantum processors require rapid and high-fidelity simultaneous measurements of many qubits. While superconducting qubits are among the leading modalities toward a useful quantum processor, their readout remains a bottleneck. Traditional approaches t...
High-expressibility Quantum Neural Networks using only classical resources
Marco Maronese, Francesco Ferrari, Matteo Vandelli +1 more·Jun 16, 2025
Quantum neural networks (QNNs), as currently formulated, are near-term quantum machine learning architectures that leverage parameterized quantum circuits with the aim of improving upon the performance of their classical counterparts. In this work, w...
The effect of Quantum Time Crystal Computing to Quantum Machine Learning methods
Hikaru Wakaura, A. B. Suksmono·Jun 15, 2025
Many body localization shows the robustness for external perturbations and time reversal symmetry on Time Crystal. This Time Crystal prolongs the coherence time, hence, it is used for quantum computers as qubits. Therefore, we established the method ...
Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training
Limengnan Zhou, Hanzhou Wu·Jun 15, 2025
Quantum neural networks (QNNs) leverage quantum computing to create powerful and efficient artificial intelligence models capable of solving complex problems significantly faster than traditional computers. With the fast development of quantum hardwa...
Physics-inspired neural networks as quasi inverse of quantum channels
Sameen Aziz, Muhammad Faryad·Jun 13, 2025
Quantum channels are not invertible in general. A quasi-inverse allows for a partial recovery of the input state, but its analytical results are found only in a restricted space of its parameters. This work explores the potential of neural networks t...
Quantum Learning and Estimation for Coordinated Operation between Distribution Networks and Energy Communities
Yingrui Zhuang, Lin Cheng, Yuji Cao +4 more·Jun 13, 2025
Price signals from distribution networks (DNs) guide energy communities (ECs) in adjusting their energy usage, enabling effective coordination for reliable power system operation. However, this coordinated operation faces significant challenges due t...
A purely Quantum Generative Modeling through Unitary Scrambling and Collapse
Yihua Li, Jiayi Chen, Tamanna S. Kumavat +1 more·Jun 12, 2025
Quantum computing offers fundamentally more expressive mechanisms for generative modeling, yet current approaches remain constrained by classical neural components that bottleneck quantum capability and hardware efficiency. We propose the Quantum Scr...
Guided graph compression for quantum graph neural networks
Mikel Casals, Vasilis Belis, E. Combarro +3 more·Jun 11, 2025
Graph neural networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix operations on GPUs. Quantum computing offers a promising avenue to add...
Learning-Optimized Qubit Mapping and Reuse to Minimize Inter-Core Communication in Modular Quantum Architectures
Sokea Sang, Leanghok Hour, Youngsun Han·Jun 11, 2025
Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple Quantum Processing Units (QPUs). However, this approach introduces significant challenges due to costly inter-core operati...
Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design
Haiyan Wang·Jun 10, 2025
We introduce Genetic Transformer Assisted Quantum Neural Networks (GTQNNs), a hybrid learning framework that combines a transformer encoder with a shallow variational quantum circuit and automatically fine tunes the circuit via the NSGA-II multi obje...
Solving excited states for long-range interacting trapped ions with neural networks
Yixuan Ma, Chang Liu, Weikang Li +4 more·Jun 10, 2025
The computation of excited states in strongly interacting quantum many-body systems is of fundamental importance. Yet, it is notoriously challenging due to the exponential scaling of the Hilbert space dimension with the system size. Here, we introduc...