Variational Quantum Circuits

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A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading
Oct 13, 2026Hyojun Ahn, Joongheon KimQuantum Machine Learning Variational Quantum Circuits
Department of Electrical & Computer Engineering, Korea University, Seoul, Republic of Korea
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing
Oct 12, 2026Anton Firc, Nouhaila Innan, Vojtěch Staněk +6Audio Deepfake Detection Speaker Verification
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 · SDU Microelectronics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, Odense, Denmark
Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion
Oct 9, 2026Jingyan Xu, Yuanhong Wu, Samuel Yen-Chi Chen +3Ensemble Learning Hybrid Quantum-Classical ML
Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA · Wells Fargo, New York, NY, USA · Division of Computer Science, Mathematics and Science, St. John’s University, Queens, NY, USA
Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting
Oct 8, 2026Jack Waller, Xing Liang, Dimitrios Makris +1Quantum Neural Networks Quantum Machine Learning
School of Computer Science and Mathematics, Kingston University London, London, KT1 2EE, U.K.. · Department of Electronics and Electrical Engineering, Brunel University of London, London, UB8 3PH, U.K..
Q-Capsule: A Localized Capsule-Based Quantum Neural Architecture for Barren Plateau Mitigation
Oct 8, 2026Awal Ahmed Fime, Tasfia Zaman Samiha, Saika Zaman +4Quantum Neural Networks Quantum Circuit Trainability
Florida Atlantic University, Boca Raton, Florida, USA · Khulna University of Engineering & Technology, Khulna, Bangladesh · Southern Illinois University Carbondale, Carbondale, Illinois, USA
Mu-DisCoCat: A Variational Pipeline for Compositional Generalization on Quantum Processors
Oct 6, 2026Mina Abbaszadeh, Matilda Karabina Moore, Raem Haq +2Quantum Machine Learning Variational Quantum Circuits
Department of Computer Science University College London London, United Kingdom · Institute for Logic, Language and Computation University of Amsterdam Amsterdam, Netherlands
What Does It Cost to Simulate a Quantum Sentence Classifier? An Energy and Compute Perspective on Near-Term QNLP
Oct 5, 2026Kishlay Kashyap, Sandipan GangulyQuantum Machine Learning Quantum Circuit Simulation
Heritage Institute of Technology
Variational Quantum Attention for Molecular Graph Learning
Oct 3, 2026Yu-Cheng Lin, Yu-Chao Hsu, Tai-Yue Li +2Graph Attention Networks Quantum Machine Learning
Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu 300092, Taiwan. · Department of Internal Medicine, School of Medicine College of Medicine, Taipei Medical University, Taipei, Taiwan · School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea +2
Evolving Hybrid Quantum-Classical Architectures for Image Classification
Oct 2, 2026Devroop Kar, Daniel Krutz, Travis DesellHybrid Quantum-Classical ML Variational Quantum Circuits
Rochester Institute of Technology Rochester, NY 14623, USA
Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamics
Sep 30, 2026Tao Liu, Ge He, Dongyu Liang +1Time Series Forecasting Proper Orthogonal Decomposition
Lawrence Technological University · North Carolina State University
Comparative Study of Quantum and Classical Machine Learning Models in Binary Classification
Sep 20, 2026Anand Kumar Mishra, Ramanuj AwasthiQuantum Machine Learning Quantum Kernel Methods
School of Engineering & Technology (UIET), CSJM University, Kanpur, Uttar Pradesh, India
From IceCube to IT-Sphere: A Hybrid Quantum-Classical GNN for Banking IT Root Cause Analysis
Sep 19, 2026Antonio Greco, Riccardo Paoletti, Roberto Cappuccio +1Root Cause Analysis Hybrid Quantum-Classical ML
Department of Physical Sciences, Earth and Environment (DSFTA), University of Siena, Siena, Italy · Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy · IBM Consulting—Promontory, IBM Industry Diamond, Banking & Financial Markets, Milan, Italy
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
Learning to Program Adaptive Non-Local Observables for Machine Learning
Sep 16, 2026Yu-Ting Lee, Samuel Yen-Chi Chen, Huan-Hsin TsengVariational Quantum Circuits Quantum Neural Networks
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · Wells Fargo, New York, NY, USA · Brookhaven National Laboratory, AI & ML Department, Upton, NY, USA
Variational Quantum Transformer Architecture for Synthetic Language Generation
Sep 16, 2026Julian Hager, Michael Kölle, Gerhard Stenzel +3Quantum Machine Learning Autoregressive Language Modeling
Institute of Informatics, LMU Munich, Oettingenstraße 67, 80538 Munich, Germany
Hybrid Variational Quantum Circuits for Multivariate Regression and High-Dimensional Data Reconstruction
Sep 15, 2026Koffi Ognandon Ayena, Frédéric Holweck, Serge Iovleff +1Quantum Feature Maps Image Reconstruction
ICB · UR4662 · BP 1515 Lomé-Togo
Parameter-Efficient Quantum NLP for Paraphrase Detection: Performance, Robustness, and Entanglement
Sep 13, 2026Farha Nausheen, Khandakar Ahmed, Farina RiazQuantum Machine Learning Hybrid Quantum-Classical ML
ISILC, Victoria University, Melbourne, VIC, Australia · CSIRO, Australian International Institute of Higher Education, Sydney, NSW, Australia
Certification cost of quantum models: measurement correlation, not parameter count
Sep 13, 2026Pavel Sulimov, Claude LehmannFisher Information Variational Quantum Circuits
Zurich University of Applied Sciences (ZHAW), Winterthur, Switzerland · University of Zurich, Zurich, Switzerland
Hierarchical Fourier Approximation for Variational Quantum Distribution Learning
Sep 5, 2026Taha Hoseinpour Asli, Sajjad Hashemian, Ebrahim Ardeshir-LarijaniQuantum Machine Learning Quantum Generative Modeling
Sharif University of Technology · University of Tehran · Iran University of Science and Technology
A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability
Sep 4, 2026Rushat Rai, Yun-Yuan Wang, Autsada Kakaen +10Quantum Machine Learning OOD Generalization
National Yang Ming Chiao Tung University, Taiwan · NVIDIA AI Technology Center, Taiwan · Chiang Mai University, Thailand +2
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
Aug 30, 2026Dilli Hang RaiHybrid Quantum-Classical ML Quantum Neural Networks
Institute of Science and Technology Tribhuvan University, Kritipur, Nepal
Quantum-Inspired Hybrid Neural Networks for Neural Decoding: A Controlled Ablation Study of Learnable Quantum Sidecar Integration
Aug 23, 2026Diana Legziel Levy, Menachem Finkelstein, Peter Chin +2Hybrid Quantum-Classical ML Quantum Neural Networks
Reichman University, Herzliya, Israel · Dartmouth College, USA · The Edmond and Lily Safra Center for Brain Sciences at The Hebrew University of Jerusalem, Israel
VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet
Aug 19, 2026Mubassir Serneabat Sudipto, Shakil Ahmed, Ashfaq KhokharCybersecurity Zero-Trust Security
Iowa State University · Electrical and Computer Engineering, Iowa State University, Ames, Iowa, USA · Grand Valley State University +3
Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction
Aug 17, 2026Aditya Kumar, Sumit ChongderFederated Unlearning Quantum Machine Learning
IDRP - Quantum Information and Computation, Department of Physics & Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan 342030, India
How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection
Aug 13, 2026Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah +2Quantum Machine Learning Hybrid Quantum-Classical ML
Hamdard University, Karachi, Pakistan · SZABIST University, Pakistan · Fandaqah, Al Khobar, Saudi Arabia
CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation
Aug 12, 2026Xue Yang, Rigui Zhou, ShiZheng Jia +7Implicit Neural Representations Quantum Generative Modeling
School of Information Engineering, Shanghai Maritime University, Shanghai 201306, China · Research Center of Intelligent Information Processing and Quantum Intelligent Computing, Shanghai 201306, China · Quantum Innovation Centre (Q.InC), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis, #08-03, Singapore 138634, Republic of Singapore +5
SPSA Hyperparameter Tuning for Variational Quantum Natural Language Inference
Aug 11, 2026Nayan D'Souza, Christopher J. AgostinoHyperparameter Optimization Variational Quantum Circuits
NPC Worldwide, Bloomington, IN, USA · Department of Linguistics, Indiana University, Bloomington, IN, USA
Classical $\mathrm{SU}(2)$ Models Match or Exceed Shallow Variational Quantum Circuits on Vision Benchmarks
Aug 7, 2026Christopher Fulton, Irene Tsapara, Lawrence FultonVariational Quantum Circuits Image Classification
United States Air Force Test Pilot School, Edwards Air Force Base, California, 93524, USA · Department of Engineering, Data, and Computer Science; National University, San Diego, California, 92123, USA · Applied Analytics, Boston College, 140 Commonwealth Ave, Chestnut Hill, MA, 02467, USA
Readout-Rank Laws for Isotropic Quantum Tangents
Aug 7, 2026Marwan Ait HaddouInformation Geometry Variational Quantum Circuits
Independent Researcher, Morocco
Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification
Aug 7, 2026Hao-Yuan ChenHybrid Quantum-Classical ML Variational Quantum Circuits
Computer Science, University of London

Oct 13, 2026J/K move · Enter open · S save

A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading

Hyojun Ahn, Joongheon Kim

Department of Electrical & Computer Engineering, Korea University, Seoul, Republic of Korea

Quantum Machine Learning Variational Quantum Circuits Quantum Feature Maps

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Amplitude embedding lets a variational quantum model read many classical features with few qubits, but the model output is then only a quadratic form in the normalized input. Data re-uploading, which encodes the input repeatedly between trainable blocks, can be used to enrich such a model. Because every additional upload of an amplitude embedding repeats a costly state preparation, the gain that an upload buys has to be known before the gates are spent. This tutorial thus addresses the problem of explaining what data re-uploading adds to an amplitude-embedded model and how large the gain is. We express sequential re-uploading and parallel re-uploading with two copies in one notation and show that each upload raises the reachable polynomial order by two. Under one trainable circuit family and one budget of trainable parameters, simulation results show that re-uploading raises the test coefficient of determination from 0.689 to between 0.883 and 0.975 on targets of order four and from 0.337 to between 0.642 and 0.783 on targets of order six, on all ten seeds. On a quadratic target and on handwritten digits, one upload remains the best choice, and every additional upload lowers the gate error that the classifier tolerates. These results help designers match the number of uploads to the polynomial order of the task at hand.