Papers
Live trends in quantum computing research, updated daily from arXiv.
Total Papers
27,086
This Month
659
Today
0
Research Volume
12,600 papers in 12 months (-14% vs prior quarter)
Research Focus Areas
Papers by research theme (12 months). Hover for details.
Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Output Prediction of Quantum Circuits based on Graph Neural Networks
Yuxiang Liu, Fanxu Meng, Lu Wang +3 more·Apr 1, 2025
The output prediction of quantum circuits is a formidably challenging task imperative in developing quantum devices. Motivated by the natural graph representation of quantum circuits, this paper proposes a Graph Neural Networks (GNNs)-based framework...
Inductive Graph Representation Learning with Quantum Graph Neural Networks
Arthur M. Faria, Ignacio F. Graña, Savvas Varsamopoulos·Mar 31, 2025
Quantum Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are scalable and robust, existing QGNNs often lack flexibility due to ...
Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization
Yusen Wu, Yukun Zhang, Chuan Wang +1 more·Mar 31, 2025
Quantum process characterization is a fundamental task in quantum information processing, yet conventional methods, such as quantum process tomography, require prohibitive resources and lack scalability. Here, we introduce an efficient quantum proces...
Applications of Hybrid Machine Learning Methods to Large Datasets: A Case Study
Georgios Maragkopoulos, Nikolaos Stefanakos, Aikaterini Mandilara +1 more·Mar 31, 2025
We combine classical and quantum Machine Learning (ML) techniques to effectively analyze long time-series data acquired during experiments. Specifically, we demonstrate that replacing a deep classical neural network with a thoughtfully designed Varia...
Enhanced Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono·Mar 28, 2025
The Kolmogorov-Arnold Network (KAN) is a novel multi-layer network model recognized for its efficiency in neuromorphic computing, where synapses between neurons are trained linearly. Computations in KAN are performed by generating a polynomial vector...
Variational quantum-neural hybrid imaginary time evolution
H. Kuji, T. Nikuni, Yuta Shingu·Mar 28, 2025
Numerous methodologies have been proposed to implement imaginary time evolution (ITE) on quantum computers. Among these, variational ITE (VITE) methods for noisy intermediate-scale quantum (NISQ) computers have attracted much attention, which uses pa...
Generative Decoding for Quantum Error-correcting Codes
Han-Yu Cao, Feng Pan, Dongyang Feng +2 more·Mar 27, 2025
Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, the complex code topology, and the large space for logical operators in...
Adaptive Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, A. B. Suksmono·Mar 27, 2025
Kolmogorov-Arnold Network (KAN) is a novel multi-layer neuromorphic network. Many groups worldwide have studied this network, including image processing, time series analysis, solving physical problems, and practical applications such as medical use....
Quantum advantage for learning shallow neural networks with natural data distributions
Laura Lewis, Dar Gilboa, Jarrod R. McClean·Mar 26, 2025
Without large quantum computers to empirically evaluate performance, theoretical frameworks such as the quantum statistical query (QSQ) are a primary tool to study quantum algorithms for learning classical functions and search for quantum advantage i...
Dataset Distillation for Quantum Neural Networks
Koustubh Phalak, Junde Li, Swaroop Ghosh·Mar 23, 2025
Training Quantum Neural Networks (QNNs) on large amount of classical data can be both time consuming as well as expensive. Higher amount of training data would require higher number of gradient descent steps to reach convergence. This, in turn would ...
HiQ-Lip: A Hierarchical Quantum-Classical Method for Global Lipschitz Constant Estimation of ReLU Networks
Haoqi He, Yan Xiao, Wenzhi Xu +3 more·Mar 20, 2025
Estimating the global Lipschitz constant of neural networks is crucial for understanding and improving their robustness and generalization capabilities. However, precise calculations are NP-hard, and current semidefinite programming (SDP) methods fac...
Degenerate mirrorless lasing in thermal vapors
Aneesh Ramaswamy, Dmitry Budker, Simon Rochester +5 more·Mar 18, 2025
Theoretical predictions were made for the steady-state gain of an orthogonally polarized probe field in a degenerate two-level alkali atom system driven by a linearly polarized continuous-wave pump field in [Opt. Mem. Neural Networks 32 (Suppl 3), S4...
Data Encoding for VQC in Qiskit, A Comparison With Novel Hybrid Encoding
Hillol Biswas·Mar 18, 2025
If quantum machine learning emulates the ways of classical machine learning, data encoding in a quantum neural network is imperative for many reasons. One of the key ones is the complexity attributed to the data size depending upon the features and t...
Quantum physics informed neural networks for multi-variable partial differential equations
Giorgio Panichi, Sebastiano Corli, Enrico Prati·Mar 15, 2025
Quantum Physics-Informed Neural Networks (QPINNs) integrate quantum computing and machine learning to impose physical biases on the output of a quantum neural network, aiming to either solve or discover differential equations. The approach has recent...
Quantum ensemble learning with a programmable superconducting processor
Jiachen Chen, Yao-Juan Wu, Zhen Yang +30 more·Mar 14, 2025
Quantum machine learning is among the most exciting potential applications of quantum computing. However, the vulnerability of quantum information to environmental noises and the consequent high cost for realizing fault tolerance has impeded the quan...
Single-qudit quantum neural networks for multiclass classification
Leandro C. Souza, Renato Portugal·Mar 12, 2025
This paper proposes a single-qudit quantum neural network for multiclass classification by using the enhanced representational capacity of high-dimensional qudit states. Our design employs a d-dimensional unitary operator, where d corresponds to the ...
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution
Siddhant Dutta, Nouhaila Innan, Khadijeh Najafi +2 more·Mar 11, 2025
Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the large number of parameters in ...
RBM-Based Simulated Quantum Annealing for Graph Isomorphism Problems
Yukun Wang, Yingtong Shen, Zhichao Zhang +1 more·Mar 10, 2025
The graph isomorphism problem remains a fundamental challenge in computer science, driving the search for efficient decision algorithms. Due to its ambiguous computational complexity, heuristic approaches such as simulated annealing are frequently us...
Seismic inversion using hybrid quantum neural networks
Divakar Vashisth, Rohan Sharma, T. Mukerji +1 more·Mar 6, 2025
Seismic inversion-including post-stack, pre-stack, and full waveform inversion is compute and memory-intensive. Recently, several approaches, including physics-informed machine learning, have been developed to address some of these limitations. Motiv...
KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA
Xiaorang Guo, Tigran Bunarjyan, Dailin Liu +2 more·Mar 5, 2025
Superconducting qubits are among the most promising candidates for building quantum information processors. Yet, they are often limited by slow and error-prone qubit readout-a critical factor in achieving high-fidelity operations. While current metho...