Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
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...
Spike-based alignment learning solves the weight transport problem
Timo Gierlich, Andreas Baumbach, Akos F. Kungl +2 more·Mar 4, 2025
In both machine learning and in computational neuroscience, plasticity in functional neural networks is frequently expressed as gradient descent on a cost. Often, this imposes symmetry constraints that are difficult to reconcile with local computatio...
CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI
Mominul Islam, Mohammad Junayed Hasan, M.R.C. Mahdy·Mar 4, 2025
The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learni...
Optimizing Low-Energy Carbon IIoT Systems With Quantum Algorithms: Performance Evaluation and Noise Robustness
Kshitij Dave, Nouhaila Innan, B. K. Behera +3 more·Mar 2, 2025
Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance. Although classical algorithms manage energy efficiency and data processing with...
Hybrid quantum neural networks with variational quantum regressor for enhancing QSPR modeling of CO2-capturing amine
Hyein Cho, Jeonghoon Kim, Kyoung Tai No +1 more·Feb 28, 2025
Accurate amine property prediction is essential for optimizing CO2 capture efficiency in post-combustion processes. Quantum machine learning (QML) can enhance predictive modeling by leveraging superposition, entanglement, and interference to capture ...
Quantum-Assisted Variational Monte Carlo
Longfei Chang, Zhendong Li, Wei-Hai Fang·Feb 28, 2025
Solving the ground state of quantum many-body systems remains a fundamental challenge in physics and chemistry. Recent advancements in quantum hardware have opened new avenues for addressing this challenge. Inspired by the quantum-enhanced Markov cha...
Exploring experimental limit of deep quantum signal processing using a trapped-ion simulator
J. Bu, Lei Zhang, Zhan Yu +12 more·Feb 27, 2025
Quantum signal processing (QSP), which enables systematic polynomial transformations on quantum data through sequences of qubit rotations, has emerged as a fundamental building block for quantum algorithms and data re-uploading quantum neural network...
An Amplitude-Encoding-Based Classical-Quantum Transfer Learning framework: Outperforming Classical Methods in Image Recognition
Shouwei Hu, Xi Li, Banyao Ruan +1 more·Feb 27, 2025
The classical-quantum transfer learning (CQTL) method is introduced to address the challenge of training large-scale, high-resolution image data on a limited number of qubits (ranging from tens to hundreds) in the current Noisy Intermediate-Scale qua...
Efficient and Universal Neural-Network Decoder for Stabilizer-Based Quantum Error Correction
Gengyuan Hu, Wanli Ouyang, Chao-Yang Lu +2 more·Feb 27, 2025
Scaling quantum computing to practical applications necessitates reliable quantum error correction. Although numerous correction codes have been proposed, the overall correction efficiency critically limited by the decode algorithms. We introduce Gra...
Quantum generative classification with mixed states
Diego H. Useche, Sergio Quiroga-Sandoval, S. Molina +3 more·Feb 27, 2025
Classification can be performed using either a discriminative or a generative learning approach. Discriminative learning consists of constructing the conditional probability of the outputs given the inputs, while generative learning consists of const...
On the Interpretability of Neural Network Decoders
Lukas Bödeker, Luc J. B. Kusters, Markus Müller·Feb 27, 2025
Neural‐network (NN) based decoders are becoming increasingly popular in the field of quantum error correction (QEC), including for decoding of state‐of‐the‐art quantum computation experiments. In this work, established interpretability methods are us...
Quantum autoencoders for image classification
Hinako Asaoka, Kazue Kudo·Feb 21, 2025
Classical machine learning often struggles with complex, high-dimensional data. Quantum machine learning offers a potential solution, promising more efficient processing. The quantum convolutional neural network (QCNN), a hybrid algorithm, fits curre...
Benchmarking MedMNIST dataset on real quantum hardware
Gurinder Singh, Hongni Jin, Kenneth M. Merz·Feb 18, 2025
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMN...
Large Language Models Can Help Mitigate Barren Plateaus in Quantum Neural Networks
Jun Zhuang, Chaowen Guan·Feb 17, 2025
In the era of noisy intermediate-scale quantum (NISQ) computing, Quantum Neural Networks (QNNs) have emerged as a promising approach for various applications, yet their training is often hindered by barren plateaus (BPs), where gradient variance vani...
Programmable photonic waveguide arrays: opportunities and challenges
Yang Yang, Akram Youssry, A. Peruzzo·Feb 17, 2025
The rising complexity of photonic applications, ranging from quantum computing to neuromorphic processing, has driven the demand for highly programmable and scalable photonic integrated circuits. While mesh-based architectures built from Mach-Zehnder...
Non-stabilizerness of Neural Quantum States
Alessandro Sinibaldi, Antonio Francesco Mello, Mario Collura +1 more·Feb 13, 2025
We introduce a methodology to estimate non-stabilizerness or "magic", a key resource for quantum complexity, with Neural Quantum States (NQS). Our framework relies on two schemes based on Monte Carlo sampling to quantify non-stabilizerness via Stabil...
Arbitrary state preparation in quantum harmonic oscillators using neural networks
Nicolas Parra-A, Vladimir Vargas-Calderón, Herbert Vinck-Posada·Feb 7, 2025
Preparing quantum states is a fundamental task in various quantum algorithms. In particular, state preparation in quantum harmonic oscillators (HOs) is crucial for the creation of qudits and the implementation of high-dimensional algorithms. In this ...
Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schrödinger Equation
Kaichen Ouyang, Mingyang Yu, Zong Ke +3 more·Feb 6, 2025
Physics-informed Neural Networks (PINNs) show that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. Similar to optimizing loss functions in mac...
Quantum Quandaries: Unraveling Encoding Vulnerabilities in Quantum Neural Networks
S. Upadhyay, Swaroop Ghosh·Feb 3, 2025
Quantum computing (QC) has the potential to revolutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. However, QML mode...
Expedited Noise Spectroscopy of Transmon Qubits
Bhavesh Gupta, V. Joshi, Udit Kandpal +3 more·Feb 2, 2025
There has been tremendous progress in the physical implementation of quantum protocols in recent times, bringing us closer than ever to realizing the promise of quantum computing. However, environmental noise continues to pose a crucial challenge to ...