Introduction to continuous-variable quantum neural networks

by Dr Tomasz Rybotycki (CAMK PAN)

Thursday 14 May 2026, 14:00 → 15:00 Europe/Warsaw

Online event

Description

Revised abstract of the main paper [1]:

We introduce a general method for building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field. This circuit contains a layered structure of continuously parameterized gates which is universal for CV quantum computation. Affine transformations and nonlinear activation functions, two key elements in neural networks, are enacted in the quantum network using Gaussian and non-Gaussian gates, respectively. The non-Gaussian gates provide both the nonlinearity and the universality of the model. Due to the structure of the CV model, the CV quantum neural network can encode highly nonlinear transformations while remaining completely unitary. We show how a classical network can be embedded into the quantum formalism.

We discuss simple implementation of such networks in piquasso.

References

1. https://arxiv.org/abs/1806.06871
2. https://arxiv.org/abs/2209.14754v2
3. https://piquasso.readthedocs.io/en/stable/advanced/cvqnn.html