RESEARCH

New preprint: Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
In this preprint, we present the key pieces needed for interpretable ML to extract meaningful information from quantum data, and apply it to extract phase diagrams of simulated and experimental data.

New preprint: Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
In this preprint, we extend previous work of the group on quantum circuit generation, enabling now the synthesis of parametrized gates with a novel multimodal diffusion model approach. 

New publication: Universal Quantum Computer from Relativistic Motion
In this paper, we show how to use relativistic quantum fields acting on qubits to build a relativistic quantum machine learning ansatz.  The paper was published in Physical Review Letters, and was covered by New Scientist.

New preprint: Minimally Universal Parity Quantum Computing
In this work, we show a sufficient condition for universal quantum computing using the parity quantum computing framework. These results also have implications for universal measurement-based quantum computing as well as for circuit compilation on IBM devices.

New preprint: The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops
In this work, we present a framework to investigate the thermodynamics of agents, showing that an agent must forget aspects of its past experience in order to be thermodynamically efficient.

Gorka Muñoz-Gil receives the Best Poster award at the AI4Quantum conference
He received this award for his poster "Quantum circuit synthesis with diffusion models", at the AI4Quantum confenrence organized by the Novo Nordisk Foundation in Copenhagen.