Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Quantum Quasinormal Mode Theory for Dissipative Nano-Optics and Magnetodielectric Cavity Quantum Electrodynamics
Lars Meschede, Daniel D. A. Clarke, Ortwin Hess·Jul 7, 2025
The unprecedented pace of evolution in nanoscale architectures for cavity quantum electrodynamics (cQED) has posed crucial challenges for theory, where the quantum dynamics arising from the non-perturbative dressing of matter by cavity electric and m...
Improving GANs by leveraging the quantum noise from real hardware
Hongni Jin, Kenneth M. Merz·Jul 2, 2025
We propose a novel approach to generative adversarial networks (GANs) in which the standard i.i.d. Gaussian latent prior is replaced or hybridized with a quantum-correlated prior derived from measurements of a 16-qubit entangling circuit. Each latent...
Generative flow-based warm start of the variational quantum eigensolver
Hang Zou, Martin Rahm, A. F. Kockum +1 more·Jul 2, 2025
Hybrid quantum-classical algorithms like the variational quantum eigensolver (VQE) show promise for quantum simulations on near-term quantum devices, but are often limited by complex objective functions and expensive optimization procedures. Here, we...
Efficient Gate Reordering for Distributed Quantum Compiling in Data Centers
Riccardo Mengoni, Walter Nadalin, Mathys Rennela +5 more·Jul 1, 2025
Just as classical computing relies on distributed systems, the quantum computing era requires new kinds of infrastructure and software tools. Quantum networks will become the backbone of hybrid, quantum-augmented data centers, in which quantum algori...
Quantum Computing in Discrete- and Continuous-Variable Architectures
Shraddha Singh·Jul 1, 2025
This thesis develops a theoretical framework for hybrid continuous-variable (CV) and discrete-variable (DV) quantum systems, with emphasis on quantum control, state preparation, and error correction. A central contribution is non-abelian quantum sign...
Coupled Cluster Downfolding Theory in Simulations of Chemical Systems on Quantum Hardware
Nicholas P. Bauman, Muqing Zheng, Chenxu Liu +5 more·Jul 1, 2025
The practical application of quantum technologies to chemical problems faces significant challenges, particularly in the treatment of realistic basis sets and the accurate inclusion of electron correlation effects. A direct approach to these problems...
Multi-Target Density Matrix Renormalization Group X algorithm and its application to circuit quantum electrodynamics
Sof'ia Gonz'alez-Garc'ia, A. Szasz, Alice Pagano +3 more·Jun 30, 2025
Obtaining accurate representations of the eigenstates of an array of coupled superconducting qubits is a crucial step in the design of circuit quantum electrodynamics (QED)-based quantum processors. However, exact diagonalization of the device Hamilt...
Quanvolutional Neural Networks for Pneumonia Detection: An Efficient Quantum-Assisted Feature Extraction Paradigm
Gazi Tanbhir, Md. Farhan Shahriyar, Abdullah Md Raihan Chy·Jun 27, 2025
Pneumonia poses a significant global health challenge, demanding accurate and timely diagnosis. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in medical image analysis for pneumonia detection, CNNs often su...
Quantum Assisted Ghost Gutzwiller Ansatz
P. V. Sriluckshmy, Franccois Jamet, IV FedorvSimkovic·Jun 26, 2025
The ghost Gutzwiller ansatz (gGut) embedding technique was shown to achieve comparable accuracy to the gold standard dynamical mean-field theory method in simulating real material properties, yet at a much lower computational cost. Despite that, gGut...
HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification
M. A. Haddou, Mohamed Bennai·Jun 26, 2025
This study investigates the efficacy of a hybrid quantum-classical model, denoted as HQCM-EBTC, for the automated classification of brain tumors, comparing its performance against a classical counterpart. A comprehensive dataset comprising 7,576 magn...
Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jiechen Chen, Bipin Rajendran, Osvaldo Simeone·Jun 26, 2025
Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficient...
Sculpting Quantum Landscapes: Fubini-Study Metric Conditioning for Geometry Aware Learning in Parameterized Quantum Circuits
M. A. Haddou, Mohamed Bennai·Jun 26, 2025
We present a novel meta learning framework called Sculpture that explicitly conditions the Fubini Study metric tensor of parameterized quantum circuits to mitigate barren plateaus in variational quantum algorithms. Our theoretical analysis identifies...
Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
Jesús Lozano-Cruz, Albert Nieto-Morales, Oriol Balló-Gimbernat +3 more·Jun 25, 2025
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to the task of satellite image classification using the EuroSAT dataset....
Sequential Quantum Computing
Sebastián V. Romero, Alejandro Gomez Cadavid, E. Solano +1 more·Jun 25, 2025
We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple homogeneous or heterogeneous quantum processors in hybrid classical-quantum workflows. In this manner, we are able to overcome the limitat...
Quantum-Centric Alchemical Free Energy Calculations
Milana Bazayeva, Zhen Li, Danil S. Kaliakin +4 more·Jun 25, 2025
In the present work, we present a hybrid quantum-classical workflow aimed at improving the accuracy of alchemical free energy (AFE) predictions by incorporating configuration interaction (CI) simulations using the book-ending correction method. This ...
Iterative Quantum Feature Maps
Nasa Matsumoto, Quoc Hoan Tran, Koki Chinzei +2 more·Jun 24, 2025
Quantum machine learning models that leverage quantum circuits as quantum feature maps (QFMs) are recognized for their enhanced expressive power in learning tasks. Such models have demonstrated rigorous end-to-end quantum speedups for specific famili...
Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos +2 more·Jun 24, 2025
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), ...
Quantum-Classical Computing for Time-Dependent Ion-Atom Collision Dynamics: Applications to Charge Transfer Cross Section Simulations
Minchen Qiao, Yu-Xi Liu·Jun 24, 2025
The simulation of ion-atom collisions remains a formidable challenge due to the complex interplay between electronic and nuclear degrees of freedom. We present a hybrid quantum-classical computing framework for simulating time-dependent ion-atom coll...
Quantum-Classical Hybrid Quantized Neural Network
Wenxin Li, Chuan Wang, Hongdong Zhu +4 more·Jun 23, 2025
In this work, we introduce a novel Quadratic Binary Optimization (QBO) framework for training a quantized neural network. The framework enables the use of arbitrary activation and loss functions through spline interpolation, while Forward Interval Pr...
A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations
Mujahidul Islam, Serkan Turkeli, Fatih Ozaydin·Jun 22, 2025
Quantum Generative Adversarial Networks (QGANs) have emerged as a promising direction in quantum machine learning, combining the strengths of quantum computing and adversarial training to enable efficient and expressive generative modeling. This surv...