# Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures

arXiv: https://arxiv.org/abs/2311.05559 · HTML: https://mlsanity.com/paper/2311.05559/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping](https://mlsanity.com/paper/2212.14807/) (2022)
- [SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training](https://mlsanity.com/paper/2301.02601/) (2023)
- [Weight Re-Mapping for Variational Quantum Algorithms](https://mlsanity.com/paper/2306.05776/) (2023)

## Built on by (most cited first, 3 of 3)

- [The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment](https://mlsanity.com/paper/2601.04732/) (2026)
- [Quantum Generator Kernels](https://mlsanity.com/paper/2602.00361/) (2026)
- [Classical SU(2) Models Match or Exceed Shallow Variational Quantum Circuits on Vision Benchmarks](https://mlsanity.com/paper/2608.07822/) (2026)

## Abstract (arXiv)

Quantum computing offers the potential for superior computational capabilities, particularly for data-intensive tasks. However, the current state of quantum hardware puts heavy restrictions on input size. To address this, hybrid transfer learning solutions have been developed, merging pre-trained classical models, capable of handling extensive inputs, with variational quantum circuits. Yet, it remains unclear how much each component -- classical and quantum -- contributes to the model's results. We propose a novel hybrid architecture: instead of utilizing a pre-trained network for compression, we employ an autoencoder to derive a compressed version of the input data. This compressed data is then channeled through the encoder part of the autoencoder to the quantum component. We assess our model's classification capabilities against two state-of-the-art hybrid transfer learning architectures, two purely classical architectures and one quantum architecture. Their accuracy is compared across four datasets: Banknote Authentication, Breast Cancer Wisconsin, MNIST digits, and AudioMNIST. Our research suggests that classical components significantly influence classification in hybrid transfer learning, a contribution often mistakenly ascribed to the quantum element. The performance of our model aligns with that of a variational quantum circuit using amplitude embedding, positioning it as a feasible alternative.