BEGIN:VCALENDAR
VERSION:2.0
PRODID:Icfo
X-PUBLISHED-TTL:P1W
BEGIN:VEVENT
UID:6a2d75bdce21c
DTSTART:20240423T130000Z
SEQUENCE:0
TRANSP:OPAQUE
DTEND:20240423T140000Z
LOCATION:Seminar Room
SUMMARY:ICFO | GORKA MUÑOZ-GIL
CLASS:PUBLIC
DESCRIPTION:Quantum computing has recently emerged as a transformative tech
 nology. Yet\, its promised&nbsp\;advantages rely on efficiently translatin
 g quantum operations into viable physical realizations.&nbsp\;In this talk
 \, I will show how to use generative machine learning models\, specificall
 y denoising diffusion models (DMs)\, to facilitate this transformation. Le
 veraging text-conditioning\, we steer the&nbsp\;model to produce desired q
 uantum operations within gate-based quantum circuits. Notably\,&nbsp\;DMs 
 allow to sidestep during training the exponential overhead inherent in the
  classical&nbsp\;simulation of quantum dynamics&mdash\;a consistent bottle
 neck in preceding ML techniques. We demonstrate the model's capabilities a
 cross two tasks: entanglement generation and unitary&nbsp\;compilation. Th
 e model excels at generating new circuits and supports typical DM extensio
 ns&nbsp\;such as masking and editing to\, for instance\, align the circuit
  generation to the constraints of&nbsp\;the targeted quantum device. Given
  their flexibility and generalization abilities\, we envision&nbsp\;DMs as
  pivotal in quantum circuit synthesis\, enhancing both practical applicati
 ons but also insights into theoretical quantum computation.
DTSTAMP:20260613T152237Z
END:VEVENT
END:VCALENDAR