Medical Imaging Classification with Cold-Atom Reservoir Computing using Auto-Encoders and Surrogate-Driven Training
Authors: Nuno Batista, Ana Morgado, Oscar Ferraz et al.
Summary
The authors built a medical imaging classifier (for detecting polyps in images) that uses a quantum computing simulator as one of its processing layers. Because quantum measurements aren't differentiable (you can't use standard backpropagation through them), they train a surrogate neural network that mimics the quantum layer, allowing gradients to flow end-to-end. An autoencoder compresses the high-dimensional images into compact representations that are fed into the quantum simulator, and the whole pipeline is trained jointly for both accurate classification and faithful image reconstruction.
Main takeaways:
- Quantum computing layers can't be trained with standard backpropagation because measurements are discrete and non-differentiable
- A differentiable surrogate model (a neural net trained to emulate the quantum layer) bridges the gradient barrier and enables end-to-end training
- An autoencoder reduces image dimensionality before the quantum step; it's trained jointly for both compression and classification accuracy
- The quantum embeddings come from expectation values in a simulated Rydberg Hamiltonian (a physics model of interacting atoms)
- The hybrid pipeline outperforms traditional PCA or unguided autoencoders on polyp detection, even with current noisy quantum simulators
Relevance
No obvious connection to my work on persona installation or behavioral conditioning in language models. Included because it's a creative solution to a gradient barrier problem, though in a completely different domain (quantum computing rather than interpretability).
Abstract
arXiv:2605.06727v1 Announce Type: new Abstract: We introduce a hybrid quantum-classical pipeline, based on neutral-atom reservoir computing, for medical image classification, focusing on the binary classification task of polyp detection. To deal effectively with the high dimensionality, we integrate a guided auto-encoder. This pipeline learns compact and discriminative representations of image data that are also well-suited for quantum reservoir computing. A key challenge in such systems is the non-differentiable nature of quantum measurements, which creates a 'gradient barrier' for standard training. We overcome this barrier by incorporating a differentiable surrogate model that emulates the quantum layer, enabling end-to-end backpropagation through the entire system. This guided training process is jointly optimized for classification accuracy and for faithful image recovery from the auto-encoder. The learned latent representations are encoded as pulse detuning parameters within a Rydberg Hamiltonian, and quantum embeddings are subsequently obtained through expectation values. These embeddings are then passed to a linear classifier. Our simulations show that this method outperforms some traditional approaches that use PCA or unguided autoencoders. We also conduct ablation studies to assess the impact of various quantum and training parameters, demonstrating the robustness and flexibility of our proposed pipeline for real-world medical imaging applications, even in the current NISQ era.