mlsanityData to 14 Oct 2026

Adaptive Non-local Observable on Quantum Neural Networks

arXiv 2504.13414 ↗First posted 18 Apr 2025quant-ph cs.AI cs.LGcited by 7

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Abstract (arXiv)

Conventional Variational Quantum Circuits (VQCs) for Quantum Machine Learning typically rely on a fixed Hermitian observable, often built from Pauli operators. Inspired by the Heisenberg picture, we propose an adaptive non-local measurement framework that substantially increases the model complexity of the quantum circuits. Our introduction of dynamical Hermitian observables with evolving parameters shows that optimizing VQC rotations corresponds to tracing a trajectory in the observable space. This viewpoint reveals that standard VQCs are merely a special case of the Heisenberg representation. Furthermore, we show that properly incorporating variational rotations with non-local observables enhances qubit interaction and information mixture, admitting flexible circuit designs. Two non-local measurement schemes are introduced, and numerical simulations on classification tasks confirm that our approach outperforms conventional VQCs, yielding a more powerful and resource-efficient approach as a Quantum Neural Network.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 18 of its 45 references that are in the map. First posted on arXiv 18 Apr 2025.

Builds on

18 of 45 references.

References · 18

  1. 2018A Convergence Theory for Deep Learning via Over-Parameterizationcited by 787
  2. 2020Variational Quantum Algorithmscited by 692
  3. 2019Parameterized quantum circuits as machine learning modelscited by 332
  4. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  5. 2021Quantum machine learning beyond kernel methodscited by 92
  6. 2020Quantum Convolutional Neural Networks for High Energy Physics Data Analysiscited by 64
  7. 2021The Dawn of Quantum Natural Language Processingcited by 61
  8. 2021An end-to-end trainable hybrid classical-quantum classifiercited by 51
  9. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  10. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  11. 2021Quantum machine learning of large datasets using randomized measurementscited by 30
  12. 2021QTN-VQC: An End-to-End Learning framework for Quantum Neural Networkscited by 27
6 more
  1. 2022PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protectioncited by 26
  2. 2024Learning to Program Variational Quantum Circuits with Fast Weightscited by 24
  3. 2023Applying QNLP to sentiment analysis in financecited by 23
  4. 2023Federated Quantum Long Short-term Memory (FedQLSTM)cited by 15
  5. 2025Learning to Measure Quantum Neural Networkscited by 15
  6. 2024Quantum Gradient Class Activation Map for Model Interpretabilitycited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

  1. 2025Quantum Reinforcement Learning by Adaptive Non-local Observablescited by 5
  2. 2026Quantum Super-resolution by Adaptive Non-local Observablescited by 4
  3. 2026Kernel-based optimization of measurement operators for quantum reservoir computerscited by 2
  4. 2026Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuitscited by 2
  5. 2026Multivariate Time Series Forecasting with Adaptive Non-Local Observablescited by 1
  6. 2026Diagonal Adaptive Non-local Observables on Quantum Neural Networkscited by 0
  7. 2026Learning to Program Adaptive Non-Local Observables for Machine Learningcited by 0