Barren Plateaus

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Dynamical Lie Algebras Cannot Describe Shallow QAOA: Cragged Terrains, Barren Plateaus, and Empirical Hardness Models
Aug 4, 2026Harrison Copp, Charlton Li, Anžej Margeta-Cacace +1Barren Plateaus Quantum Approximate Optimization Algorithm
Yale University
LLM-Guided Initialization for Accelerated Hybrid Quantum-Classical Medical Image Classification
Jul 29, 2026Riza Alaudin Syah, Irwan Alnarus Kautsar, Haza Nuzly Bin Abdull HamedNeural Network Initialization Hybrid Quantum-Classical ML
Faculty of Computing Universiti Teknologi Malaysia Johor Bahru, Malaysia · Remote Student based in Jakarta, Indonesia · Informatics Department Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo Sidoarjo, Indonesia
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
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
Beyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning
Jun 30, 2026Kung-Ming Lan, Edward HuangQuantum Machine Learning Barren Plateaus
Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan · Department of Industrial and Systems Engineering, 3306 Shelby Center, Auburn University, Auburn, AL, USA
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
Quantum Machine Learning for Industrial Applications
Jun 12, 2026Léo MonbroussouQuantum Convolutional Neural Networks Quantum Machine Learning
Sorbonne Université, Ecole Doctorale Informatique, Télécommunications et Electronique (n° 572), Computer Science
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
Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
May 27, 2026Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo +1Latent Variable Models Quantum Generative Modeling
Quantum Laboratory, Fujitsu Research, Fujitsu Limited, Kawasaki, Kanagawa 211-8588, Japan
Barren Plateaus as Destructive Interference: A Diagnostic Framework and Implications for Structured Ansatzes
May 2, 2026Pilsung KangBarren Plateaus Variational Quantum Circuits
Department of Software Science, Dankook University, Yongin, 16890, South Korea
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

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Dynamical Lie Algebras Cannot Describe Shallow QAOA: Cragged Terrains, Barren Plateaus, and Empirical Hardness Models

Harrison Copp, Charlton Li, Anžej Margeta-Cacace, Amy Qiao

Yale University

Barren Plateaus Quantum Approximate Optimization Algorithm

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The dynamical Lie algebraic (DLA) theory of variational quantum algorithms (VQAs) predicts commonplace exponentially vanishing loss and gradient variances for sufficiently deep parametrized circuits. In this work, we show that these predictions fail dramatically in the shallow-circuit (and particularly constant-depth) regime for the Quantum Approximate Optimization Algorithm (QAOA) applied to the maximum independent set (MIS) problem. In a large-scale numerical study across

\sim

23,000 problem instances, we find that barren plateaus are rare, while landscapes whose variances polynomially increase with system size---which we term "cragged terrains"---are common across graph families. This aggregate polynomial growth persists both for generic, low-symmetry random graphs and for highly symmetric vertex-transitive graphs, indicating that DLA-based variance predictions do not describe landscape scaling in this regime. As a stopgap alternative to the theory, we train empirical hardness models to predict instance-wise hardness metrics for QAOA-MIS. While these models generalize poorly, they nonetheless recover the correct landscape scaling class (barren plateau vs. cragged terrain) with high fidelity. Taken together, our results identify shallow QAOA for MIS as a prototypical setting in which asymptotic, unitary-design-centric predictions may be fundamentally insufficient to describe shallow variational quantum algorithms more broadly, emphasizing the need for more empirically-informed models of VQA loss landscapes.

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