Hybrid Quantum-Classical Architecture for Single-Cell Gene Expression Prediction

In order to generate cellular function maps, we have a large reliance on predicting how genetic modifications shift gene activity. Although high-throughput screens are available, the huge number of possibilities slows down scaling. Our present frameworks offer insight, but they still aren't the most ideal when working with complex biological intricacies.

This project is a hybrid quantum-classical architecture to predict single-cell gene expression profiles following gene perturbations. First, inside a frozen classical State Transition (STATE) transformer core, we embedded parameterized quantum circuits (PQCs), as trainable nonlinear transformations in our system. Then, we had the system encode basal gene expression and perturbation features into a shared latent representation, mapped using linear projection to our quantum circuit parameters.

The quantum part itself used layers of adjustable single-qubit rotations and entanglement gates. From there, the Pauli-Z expectation values were then compacted into usable encoding, which a final linear layer decoded into gene expression predictions. The optimization process was mainly focused on the quantum parameters and decoder, while keeping the classical backbone frozen.

The hybrid design worked for enhancing the classical encoder with the additional nonlinear processing. It allowed for organization while bringing together localized and correlated quantum states to predict expression for hidden perturbations.

The research shows that adaptable quantum circuits are able to work as nonlinear operators in biological foundation frameworks. Our hybrid strategy shows a very real route of this very application of quantum learning techniques for the dimensionality complexities inherent to genes.