Quantum Circuit Trainability

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Latest papers 16

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Quantum Graph Convolutional Networks: Implementation and Trainability Analysis
Sep 17, 2026Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limbäck-Stokin +2Graph-Based Semi-Supervised Learning Variational Quantum Circuits
Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate-Mondragon, Spain · University of the Basque Country/Euskal Herriko Unibertsitatea-EHU · Quantum Learning Labs, Department of Computer Science, University College London, London, United Kingdom +4
Stacking the Deck: Tunable Trainability in Stacked LCUs
Jul 27, 2026Nikhil Khatri, Stefan Zohren, Gabriel MatosBarren Plateaus Quantum Circuit Simulation
Machine Learning Research Group, University of Oxford, United Kingdom · 2Quantinuum, London, United Kingdom
Entanglement geometry separates circuit cutting, classical hardness, and trainability
Jul 20, 2026Maria Gragera Garces, Sabina Drăgoi, Lirandë PiraTensor Networks Quantum Circuit Simulation
Quantum Software Lab University of Edinburgh, UK · ETH ZÜrich, ZÜrich, Switzerland IBM Research, RÜschlikon, Switzerland · Centre for Quantum Technologies, National University of Singapore, Singapore
Identity-Paired Progressive Depth Training: When Trainability Persists Beyond Expressibility
Jul 18, 2026Athanasios Hadjidimoulas, Tirthak Patel, Anastasios KyrillidisVariational Quantum Circuits Variational Quantum Algorithms
Computer Science, Rice University · QuanTAS research cluster, Ken Kennedy Institute at Rice University
Overcoming Fourier Locking in Quantum Data Re-uploading Classifiers via Spectral Homotopy
Jul 13, 2026Spencer TopelQuantum Machine Learning Variational Quantum Circuits
Moth, Brooklyn, New York
Breaking the One-Dimensional Expressibility-Trainability Tradeoff
Jul 6, 2026Kyoungho Cho, Yu-Seong Jeon, Jinhyoung Lee +1Barren Plateaus Variational Quantum Circuits
Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul 03722, Republic of Korea · Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea · Department of Physics, Hanyang University, Seoul 04763, Republic of Korea +2
A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks
Jun 28, 2026Hassan Ugail, Newton HowardEquivariant Neural Networks Neural Network Training Dynamics
Centre for Visual Computing and Intelligent Systems University of Bradford United Kingdom · School of Individualized Study Rochester Institute of Technology United States
Exponentially many initializations to avoid barren plateaus
Jun 16, 2026Ankit Kulshrestha, Ricard Puig, Diego García-Martín +4Barren Plateaus Variational Quantum Circuits
Fujitsu Research of America, Santa Clara, CA 95054, USA · Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland · Centre for Quantum Science and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland +4
Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
Jun 8, 2026Gennaro De Luca, Vinayak Sharma, Aviral ShrivastavaQuantum Generative Modeling Quantum Machine Learning
SCAI, Arizona State University
Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
May 31, 2026Syed Farhan Ahmad, Gregory T. ByrdHybrid Quantum-Classical ML Barren Plateaus
Department of ECE, North Carolina State University, Raleigh, North Carolina, USA
Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
May 7, 2026Michael Poppel, David Bucher, Maximilian Zorn +5Variational Quantum Circuits Quantum Circuit Trainability
Department of Computer Science, LMU Munich, Germany · Aqarios GmbH, Munich, Germany
Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
May 5, 2026Abhishek Sadhu, Sharu Theresa JoseVariational Quantum Circuits Adversarial Perturbations
School of Computer Science, University of Birmingham · Edgbaston, Birmingham B15 2TT, United Kingdom
An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor
Apr 26, 2026Tushar PandeyVariational Quantum Circuits Physics-Informed ML
Texas A&M University, College Station, TX 77843, USA
A four-player potential game for barren-plateau-aware quantum ansatz design
Apr 23, 2026Rubén Darío GuerreroQuantum Chemistry Barren Plateaus
Parametrized-QC-Graphs Project
Trainability Beyond Linearity in Variational Quantum Objectives
Apr 20, 2026Gordon Ma, Xiufan LiBarren Plateaus Variational Quantum Circuits
Centre for Quantum Technologies, National University of Singapore, Singapore
Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
Date pendingQingyu Meng, Yangshuai WangQuantum Machine Learning Hybrid Quantum-Classical ML
Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China. · Department of Mathematics, National University of Singapore, 10 Lower Kent Ridge Road, Singapore.

Sep 17, 2026, quant-phJ/K move · Enter open · S save

Quantum Graph Convolutional Networks: Implementation and Trainability Analysis

Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limbäck-Stokin, Kin Ian Lo+1

Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate-Mondragon, Spain · University of the Basque Country/Euskal Herriko Unibertsitatea-EHU · Quantum Learning Labs, Department of Computer Science, University College London, London, United Kingdom+4

Graph-Based Semi-Supervised Learning Variational Quantum Circuits Quantum Circuit Trainability Quantum Neural Networks

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Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data, but training and inference on large graphs are often bottlenecked by memory constraints and sparse linear-algebra workloads. Quantum computing offers an alternative set of primitives that may improve scalability for graph learning. Building on the quantum graph neural network (QGNN) framework of Liao \textit{et al.}, this work implements two representative architectures --- the Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC) models --- and evaluates them on open benchmark graph datasets and semi-supervised learning tasks using quantum simulation. We compare predictive performance and optimization behavior against classical baselines, showing that the quantum models achieve competitive performance with fewer parameters. Finally, we present a cost gradient analysis that identifies the tasks for which the models showcased are trainable. This is followed by a classical simulability study to find regimes in which the proposed circuits remain robust during training.

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