Variational Quantum Circuits

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14 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

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LC-Implicit-QAOA: Active-Workspace-Capped Exact Objective-and-Gradient Evaluation for Training over Bounded QUBO Light Cones
Aug 6, 2026Chih-Chung HsuQuadratic Unconstrained Binary Optimization Variational Quantum Circuits
Institute of Smart Industry and Green Energy, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Statistical Mechanics of Learning on Product Wasserstein Manifolds
Aug 2, 2026Srinivasa Rao P Vangmayi P ReddyStatistical Physics of Learning Wasserstein Gradient Flows
Curlvee Technolabs, India; Former Scientist, C-DAC.
Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
Aug 2, 2026Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin +4Hybrid Quantum-Classical ML Variational Quantum Circuits
Terra Quantum AG, Kornhausstrasse 25, St. Gallen, 9000, Switzerland · Département de Physique Appliquée, Université de Genève, 24 Quai Ernest-Ansermet, Geneva, 1211, Switzerland
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
Cautious optimism for deep parameterized quantum circuits
Jul 23, 2026Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5Double Descent Neural Network Generalization
Leiden University, Niels Bohrweg 1, 2333 CA Leiden, Netherlands · Volkswagen Group Innovation, Berliner Ring 2, 38440 Wolfsburg, Germany · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany +4
An Analytically Trained Variational Surrogate for Quantum Phase Estimation on NISQ Hardware
Jul 23, 2026Mousumi Kundu, Ashish Kumar Patra, Anurag K. S. V. +4Quantum Chemistry Variational Quantum Circuits
Qclairvoyance Quantum Labs, Secunderabad, TG 500094, India. · Department of CSE(AI & ML), School of Engineering, Dayananda Sagar University, Bengaluru, KA 562112, India. · The University of Arizona, Tucson, AZ 85721, USA. +1
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
Rethinking Quantum Continual Learning with Quantum Fisher Information
Jul 17, 2026Yu-Chao Hsu, Yu-Cheng Lin, Tai-Yue Li +2Quantum Machine Learning Variational Quantum Circuits
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea · Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu 300092, Taiwan. · National Center for High-Performance Computing , National Institutes of Applied Research (NIAR), Hsinchu 300092, Taiwan
A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations
Jul 17, 2026Sanjay Chakraborty, Fredrik HeintzMultivariate Time Series Forecasting Time Series Forecasting
Department of Computer Science & Engineering, Techno International New Town, Kolkata, India
Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction
Jul 15, 2026James T. Pegg, Hubert Okadome Valencia, Ronin WuGraph Neural Networks Variational Quantum Circuits
QunaSys Europe Copenhagen, Denmark · QunaSys Inc. Tokyo, Japan
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Jul 13, 2026Anton Firc, Martin Perešíni, Vojtěch Mrázek +7GPU Acceleration Hybrid Quantum-Classical ML
EvoAI Hardware Group, Brno University of Technology, Brno, Czech Republic · eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE
When cheap gradients fail: the measurement cost of attacking quantum classifiers
Jul 13, 2026Bacui Li, Chandra Thapa, Tansu Alpcan +1Quantum Machine Learning Variational Quantum Circuits
Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria 3010, Australia · CSIRO Data61, Marsfield, NSW 2122, Australia · School of Computing and Information Systems, University of Melbourne, Parkville, Victoria 3010, Australia
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
MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery
Jul 12, 2026Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique +1Selective Prediction Meta-Learning
Department of Physics, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India · eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE
An End-to-End Hybrid Quantum--Classical Sampling Workflow for Discrete Markov Random Fields: A Reproducible Case Study
Jul 10, 2026Arul Rhik MazumderMarkov Chain Monte Carlo Markov Random Fields
School of Computer Science
Quantum Circuits in Diffusion Models: A Fair-Comparison Study and a Mechanistic Analysis of Angle-Embedding Failures
Jul 10, 2026Jaeuk Kim, Sanghoon YooQuantum Generative Modeling Variational Quantum Circuits
NextITS Co., Ltd.
Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
Jul 7, 2026Jian Xu, Delu Zeng, John Paisley +1PAC-Bayesian Generalization Bounds Quantum Machine Learning
1RIKEN iTHEMS · 2RIKEN AIP · 3South China University of Technology +1
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
Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Jul 2, 2026Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +8Time Series Forecasting Variational Quantum Circuits
National Taiwan University, Taiwan · Stevens Institute of Technology, NJ, USA · Seoul National University, Korea +3
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
Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation
Jun 29, 2026Javier Lazaro, Juan-Ignacio Vazquez, Pablo Garcia-BringasHybrid Quantum-Classical ML Variational Quantum Circuits
University of Deusto, Bilbao, Spain · FSAS International Quantum Center, Fujitsu
Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding
Jun 27, 2026Aspen Erlandsson Brisebois, Luis Pablo Gonzalez Dominguez, Shivansi Prajapati +8Quantum Feature Maps Quantum Machine Learning
Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, Saskatoon, SK, Canada · Engineering Science, Faculty of Applied Science, University of Toronto, Toronto, ON, Canada · Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, SK, Canada +5
Quantum Dynamic Time Warping for Multivariate Time Series Classification
Jun 26, 2026Diego Alvarez-Estevez, Alejandro Mayorga-Redondo, Eduardo Mosqueira-ReyDynamic Time Warping Multivariate Time Series Classification
CITIC Research Center · Universidade da Coruña · A Coruña, 15071, Spain
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
Jun 22, 2026Samuel Yen-Chi Chen, Yifeng Peng, Jiun-Cheng Jiang +8Quantum Machine Learning Recurrent Neural Networks
Wells Fargo · Stevens Institute of Technology · National Taiwan University +3
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
An LLM System for Autonomous Variational Quantum Circuit Design
Jun 11, 2026Kenya Sakka, Wataru Mizukami, Kosuke MitaraiQuantum Chemistry Quantum Kernel Methods
Center for Quantum Information and Quantum Biology, The University of Osaka, 1-2 Machikaneyama, Toyonaka 560-0043, Japan · Graduate School of Engineering Science, The University of Osaka 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
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
Who Earns the Safety? Intervention-Aware Quantum Predictive Control with Safety Attribution
Jun 8, 2026Yifan WangSafety-Critical Control Control Barrier Functions
Department of Mechanical Engineering, McGill University Montréal, QC H3A 2T7, Canada

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LC-Implicit-QAOA: Active-Workspace-Capped Exact Objective-and-Gradient Evaluation for Training over Bounded QUBO Light Cones

Chih-Chung Hsu

Institute of Smart Industry and Green Energy, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Quadratic Unconstrained Binary Optimization Variational Quantum Circuits Quantum Approximate Optimization Algorithm

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QAOA training repeatedly queries an objective and all shared gradients, making exact evaluation a feasibility bottleneck even when QUBO terms have bounded causal cones. Building on established causal-cone restriction and adjoint differentiation, LC-Implicit-QAOA profiles cone structure and induced-edge counts before local-amplitude and named-workspace allocation, then jointly selects equal-size microbatches and checkpoint schedules under a named active-evaluator workspace budget. "Implicit" means omitting both global state and global cost table, not implicit differentiation; infeasible requests are rejected before those allocations. An independently implemented complex128/float64 dense adjoint agrees with LC over 1,800 graph-angle comparisons, with a worst relative gradient error of 1.56 x 10^-13. LC completes all 104 target requests in a p=2 bounded-cone grid; under a prespecified n <= 24 validation cap, the matched state-plus-cost reference is executed for 28 requests and deliberately not run on 76. Across 80 budgeted requests, measured allocated evaluator memory stays within budget, reaching at most 0.797 of it. On 3-regular n=512, p=2, the adjoint reaches the same finite-budget endpoint in 101 objective-equivalent calls and 189 s, versus 909 calls and 1,565 s for central differences. LC targets fixed-depth one- and two-local diagonal QUBO costs with a transverse-field mixer; it provides neither global states, sampling, nor a hardware-independent fastest-backend rule.