Quantum Circuit Expressibility

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12 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading
Oct 13, 2026Hyojun Ahn, Joongheon KimQuantum Circuit Expressibility Quantum Machine Learning
Department of Electrical & Computer Engineering, Korea University, Seoul, Republic of Korea
Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depth
Aug 5, 2026Arunava Majumder, Marius Krumm, Hendrik Poulsen Nautrup +1Quantum Generative Modeling Quantum Circuit Expressibility
University of Innsbruck, Department of Theoretical Physics, Technikerstraße 21a, A-6020 Innsbruck, Austria.
Identity-Paired Progressive Depth Training: When Trainability Persists Beyond Expressibility
Jul 18, 2026Athanasios Hadjidimoulas, Tirthak Patel, Anastasios KyrillidisQuantum Circuit Expressibility Variational Quantum Circuits
Computer Science, Rice University · QuanTAS research cluster, Ken Kennedy Institute at Rice University
Breaking the One-Dimensional Expressibility-Trainability Tradeoff
Jul 6, 2026Kyoungho Cho, Yu-Seong Jeon, Jinhyoung Lee +1Quantum Circuit Expressibility Barren Plateaus
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
Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
Jun 10, 2026Jeongho Bang, Kyoungho Cho, Jeongwoo JaeModel Selection Sample Complexity
Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul 03722, Republic of Korea · Department of Quantum Information, Yonsei University, Incheon 21983, Republic of Korea · Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea +1
Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
May 5, 2026Abhishek Sadhu, Sharu Theresa JoseQuantum Circuit Expressibility Variational Quantum Circuits
School of Computer Science, University of Birmingham · Edgbaston, Birmingham B15 2TT, United Kingdom
Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
Apr 27, 2026Chi-Sheng Chen, En-Jui KuoQuantum Circuit Expressibility Quantum Machine Learning
Beth Israel Deaconess Medical Center & Harvard Medical School Boston, MA, USA · Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu, Taiwan
An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor
Apr 26, 2026Tushar PandeyQuantum Circuit Expressibility Variational Quantum Circuits
Texas A&M University, College Station, TX 77843, USA
Adaptive H-EFT-VA: A Provably Safe Trajectory Through the Trainability-Expressibility Landscape of Variational Quantum Algorithms
Apr 12, 2026Eyad I. B. HamidQuantum Circuit Expressibility Variational Quantum Algorithms
Department of Physics, International University of Africa, Khartoum, Sudan
LieTrunc-QNN: Lie Algebra Truncation and Quantum Expressivity Phase Transition from LiePrune to Provably Stable Quantum Neural Networks
Apr 3, 2026Haijian Shao, Dalong Zhao, Xing Deng +2Quantum Circuit Expressibility Quantum Neural Networks
School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China · Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, USA
Model selection in hybrid quantum neural networks with applications to quantum transformer architectures
Mar 23, 2026Harsh Wadhwa, Rahul Bhowmick, Naipunnya Raj +3Quantum Circuit Expressibility Quantum Neural Networks
Quantum Lab, Fujitsu Research of India
How to find expressible and trainable parameterized quantum circuits?
Mar 15, 2026Peter Röseler, Dennis Willsch, Kristel MichielsenQuantum Circuit Expressibility Variational Quantum Circuits
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany · Universität zu Köln, 50923 Köln, Germany · Faculty of Medical Engineering and Technomathematics, University of Applied Sciences Aachen, 52428 Jülich, Germany +1

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 Circuit Expressibility Quantum Machine Learning Quantum Data Encoding 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.