Quantum Architecture Search

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15 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization
Oct 7, 2026Lukas Theißinger, Thore Gerlach, Christian BauckhageQuantum Architecture Search Quantum Machine Learning
University of Bonn Germany · European Space Agency Netherlands · Lamarr Institute Germany
Evolving Hybrid Quantum-Classical Architectures for Image Classification
Oct 2, 2026Devroop Kar, Daniel Krutz, Travis DesellHybrid Quantum-Classical ML Quantum Architecture Search
Rochester Institute of Technology Rochester, NY 14623, USA
Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Sep 10, 2026Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1Quantum Architecture Search Generative Replay
Delft University of Technology, Delft, The Netherlands. · Quantum Computing Division, QuTech, Delft, The Netherlands. · Indian Institute of Technology (BHU), Varanasi, India. +1
DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
Jul 31, 2026Jiayang Niu, Yan Wang, Jie Li +4Quantum Architecture Search World Models
School of Computing Technologies, RMIT University · Quantum Systems, Data61, CSIRO · RMIT University
Approximate Quantum State Preparation Through Proximal Policy Optimization
Jul 23, 2026Marco Mordacci, Michele AmorettiQuantum Architecture Search Quantum State Preparation
Quantum Software Laboratory, University of Parma, Parco Area delle Scienze, 181/A, Parma, 43124, Italy.
Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design
Jul 21, 2026Krishna Bhatia, Gautami Sanjay NaikLarge Language Model-Guided Optimization Quantum Machine Learning
QuantumAI Lab, Fractal Analytics India · Indian Institute of Information Technology Dharwad India
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 +1Neural Decoding Selective Prediction
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
HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search
Jul 6, 2026Jiayang Niu, Akib Karim, Yan Wang +5Quantum Architecture Search Benchmark Construction
School of Computing Technologies RMIT University · Quantum Systems School of Computing Technologies RMIT University · Data61, CSIRO
Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
May 21, 2026Farah Elnakhal, Alberto Marchisio, Nouhaila Innan +2Hybrid Quantum-Classical ML Hardware-Aware NAS
Science Division, New York University Abu Dhabi, UAE · eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE · Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi, UAE +1
Hybrid Quantum-Classical Neural Architecture Search
May 18, 2026Alberto Marchisio, Muhammad Kashif, Nouhaila Innan +1Quantum Machine Learning Hybrid Quantum-Classical ML
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, United Arab Emirates · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, United Arab Emirates
Zero-shot Quantum Neural Architecture Search
May 12, 2026Tung Dao, Son N. Tran, Huynh Thi Thanh BinhQuantum Machine Learning Neural Architecture Search
Hanoi University of Science and Technology, Hanoi, Vietnam · Deakin University, Melbourne, Australia
Magic-Informed Quantum Architecture Search
May 5, 2026Vincenzo Lipardi, Domenica Dibenedetto, Georgios Stamoulis +1Quantum Architecture Search
Department of Advanced Computing Sciences Maastricht University Maastricht, The Netherlands
Replay-buffer engineering for noise-aware quantum circuit optimization
Apr 23, 2026Akash Kundu, Sebastian FeldQuantum Architecture Search Reinforcement Learning
Delft University of Technology, Delft, The Netherlands · Quantum Computing Division, QuTech, The Netherlands
QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
Apr 8, 2026Kooshan Maleki, Alberto Marchisio, Muhammad ShafiqueQuantum Architecture Search Quantum Neural Networks
Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran · 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
Layered Quantum Architecture Search for 3D Point Cloud Classification
Mar 20, 2026Natacha Kuete Meli, Jovita Lukasik, Vladislav Golyanik +1Quantum Architecture Search Quantum Neural Networks
University of Siegen · MPI for Informatics, SIC

Oct 7, 2026J/K move · Enter open · S save

Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization

Lukas Theißinger, Thore Gerlach, Christian Bauckhage

University of Bonn Germany · European Space Agency Netherlands · Lamarr Institute Germany

Quantum Architecture Search Quantum Machine Learning Variational Quantum Algorithms

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Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational quantum optimization algorithms. However, its practical deployment on quantum hardware is limited by the large number of circuit measurements required during optimization, making hardware execution costly. In this work, we show that for a broad class of combinatorial optimization problems and commonly used rotational gate parameterizations, the measurement cost of DQAS can be significantly reduced without changing the optimization objective. We derive the proposed measurement reduction scheme theoretically and validate it experimentally on 3-SAT and MaxCut benchmark problems. Our approach reduces the requested gradient measurement cost by about 39 to 41% while introducing only negligible classical post-processing overhead, lowering the practical cost of executing DQAS on quantum hardware.