mlsanityData to 14 Oct 2026

Quantum Imitation Learning

arXiv 2304.02480 ↗First posted 4 Apr 2023quant-ph cs.LGcited by 1

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high computation burden. In this work, we propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. Concretely, we develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL). Q-BC is trained with a negative log-likelihood loss in an off-line manner that suits extensive expert data cases, whereas Q-GAIL works in an inverse reinforcement learning scheme, which is on-line and on-policy that is suitable for limited expert data cases. For both QIL algorithms, we adopt variational quantum circuits (VQCs) in place of DNNs for representing policies, which are modified with data re-uploading and scaling parameters to enhance the expressivity. We first encode classical data into quantum states as inputs, then perform VQCs, and finally measure quantum outputs to obtain control signals of agents. Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts, with the potential of quantum speed-up. To our knowledge, we are the first to propose the concept of QIL and conduct pilot studies, which paves the way for the quantum era.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 33 of its 94 references that are in the map. First posted on arXiv 4 Apr 2023.

Builds on

33 of 94 references.

Closest ancestors · 10

Same direction, newest first

  1. 2021Hyperparameter Selection for Imitation Learning25 May
  2. 2020Error Bounds of Imitating Policies and Environments22 Oct
  3. 2020On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration16 Oct
  4. 2020f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning2 Oct
  5. 2020Toward the Fundamental Limits of Imitation Learning13 Sep
  6. 2020Primal Wasserstein Imitation Learning8 Jun
  7. 2019A Divergence Minimization Perspective on Imitation Learning Methods6 Nov
  8. 2018An Algorithmic Perspective on Imitation Learning16 Nov
  9. 2018Sample-Efficient Imitation Learning via Generative Adversarial Nets6 Sep
  10. 2018Generative Adversarial Imitation from Observation17 Jul

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2016OpenAI Gymcited by 1,736
  5. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  6. 2016Generative Adversarial Imitation Learningcited by 1,597
  7. 2014How transferable are features in deep neural networks?cited by 1,225
  8. 2020Variational Quantum Algorithmscited by 692
  9. 2018Supervised learning with quantum enhanced feature spacescited by 665
  10. 2017End-to-end Driving via Conditional Imitation Learningcited by 485
  11. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  12. 2019Parameterized quantum circuits as machine learning modelscited by 332
11 more
  1. 2020Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depthcited by 245
  2. 2021Generalization in quantum machine learning from few training datacited by 216
  3. 2021Parametrized quantum policies for reinforcement learningcited by 90
  4. 2020Experimental Quantum Generative Adversarial Networks for Image Generationcited by 83
  5. 2020An empirical investigation of the challenges of real-world reinforcement learningcited by 77
  6. 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
  7. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 77
  8. 2020TensorFlow Quantum: A Software Framework for Quantum Machine Learningcited by 72
  9. 2021An end-to-end trainable hybrid classical-quantum classifiercited by 51
  10. 2019Quantum Computing: Lecture Notescited by 14
  11. 2019Quantum-Inspired Support Vector Machinecited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021IQ-Learn: Inverse soft-Q Learning for Imitation
  2. 2025Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits
  3. 2024Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization
  4. 2024Quantum Advantage Actor-Critic for Reinforcement Learning
  5. 2024VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
  6. 2020Augmented Q Imitation Learning (AQIL)
  7. 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components
  8. 2025Improved Offline Reinforcement Learning via Quantum Metric Encoding