mlsanityData to 7 Oct 2026

Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs

arXiv 2109.03806 ↗First posted 8 Sep 2021quant-ph cs.AIcited by 11

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Abstract (arXiv)

With the constant increase of the number of quantum bits (qubits) in the actual quantum computers, implementing and accelerating the prevalent deep learning on quantum computers are becoming possible. Along with this trend, there emerge quantum neural architectures based on different designs of quantum neurons. A fundamental question in quantum deep learning arises: what is the best quantum neural architecture? Inspired by the design of neural architectures for classical computing which typically employs multiple types of neurons, this paper makes the very first attempt to mix quantum neuron designs to build quantum neural architectures. We observe that the existing quantum neuron designs may be quite different but complementary, such as neurons from variational quantum circuits (VQC) and Quantumflow. More specifically, VQC can apply real-valued weights but suffer from being extended to multiple layers, while QuantumFlow can build a multi-layer network efficiently, but is limited to use binary weights. To take their respective advantages, we propose to mix them together and figure out a way to connect them seamlessly without additional costly measurement. We further investigate the design principles to mix quantum neurons, which can provide guidance for quantum neural architecture exploration in the future. Experimental results demonstrate that the identified quantum neural architectures with mixed quantum neurons can achieve 90.62% of accuracy on the MNIST dataset, compared with 52.77% and 69.92% on the VQC and QuantumFlow, respectively.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 36 of its 48 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

36 of 48 references.

References · 36

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24 more
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 5; 2024: 1.

11 papers.

  1. 2021QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalizationcited by 27
  2. 2022QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruningcited by 23
  3. 2022Quantum Neural Network Compressioncited by 12
  4. 2022Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?cited by 12
  5. 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Modelcited by 5
  6. 2023A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devicescited by 5
  7. 2023Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defensescited by 5
  8. 2024PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloudcited by 5
  9. 2022Review of Ansatz Designing Techniques for Variational Quantum Algorithmscited by 3
  10. 2023Neural Crystalscited by 0
  11. 2023U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Acceleratorscited by 0