mlsanityData to 14 Oct 2026

ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers

arXiv 2506.21537 ↗First posted 26 Jun 2025quant-ph cs.CV cs.ETcited by 1

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Abstract (arXiv)

Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary differential equation (neural ODE) based residual neural networks (ResNets), which aim to improve the effectiveness of neural networks using the principles of ordinary differential equations. In this work, we present our insights about why analog Rydberg atom quantum computers are especially well-suited for ResNets. We also introduce ResQ, a novel framework to optimize the dynamics of Rydberg atom quantum computers to solve classification problems in machine learning using analog quantum neural ODEs.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 39 references that are in the map. First posted on arXiv 26 Jun 2025.

Builds on

17 of 39 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2018Neural Ordinary Differential Equationscited by 3,779
  4. 2019Augmented Neural ODEscited by 397
  5. 2020Power of data in quantum machine learningcited by 338
  6. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  7. 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
  8. 2018Continuous-variable quantum neural networkscited by 74
  9. 2022Learning to predict arbitrary quantum processescited by 35
  10. 2021QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalizationcited by 27
  11. 2023MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computerscited by 16
  12. 2020A hybrid quantum-classical neural network with deep residual learningcited by 13
5 more
  1. 2024Digital-analog quantum learning on Rydberg atom arrayscited by 6
  2. 2022Differentiable Analog Quantum Computing for Optimization and Controlcited by 4
  3. 2023SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computerscited by 3
  4. 2024Non-native Quantum Generative Optimization with Adversarial Autoencoderscited by 3
  5. 2024ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networkscited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Layerwise Federated Learning for Heterogeneous Quantum Clients using Quoruscited by 5