← Back to all news
arXiv Machine Learning By Riza Alaudin Syah, Irwan Alnarus Kautsar, Haza Nuzly Bin Abdull Hamed

LLM-Guided Initialization for Accelerated Hybrid Quantum-Classical Medical Image Classification

Read the original on arXiv Machine Learning →

arXiv:2607. 27262v1 Announce Type: cross Abstract: Variational quantum algorithms often encounter barren plateaus, where cost gradients decay rapidly with increasing circuit depth, undermining the trainability of parameterized quantum circuits.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jul 27

Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification

arXiv:2607. 21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning.

By Guillermo Rubi\~nos Rodr\'iguez, Mart\'in Ottavianelli, Mateo Alonso, Gonzalo Bl\'azquez Gil, Boris-Stephan Rauchmann, Pablo D\'iez-Valle, Sergio Altares-L\'opez
computer-visionbenchmarks
More like this →
arXiv AI
Jun 3

Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation

arXiv:2606. 03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes.

By Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada, Martin Roetteler, Iordanis Kerenidis
benchmarks
More like this →
arXiv Machine Learning
Jun 9

Adaptive directional gradients for parameterised quantum circuits

arXiv:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.

By Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi
More like this →
arXiv Machine Learning
Aug 11

Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework

arXiv:2604. 15645v2 Announce Type: replace Abstract: We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow.

By Ziv Chen, Hemanth Chandravamsi, Shimon Pisnoy, Aaron Goldgewert, Gal Shaviner, Boris Shragner, Steven H. Frankel
ragbenchmarks
More like this →