Speaker: Pietro Butti
Abstract: Neural network quantum states (NNQS) represent a novel approach in quantum many-body physics using techniques inspired by artificial neural networks combined with standard variational Monte-Carlo methods. The growing computational and algorithmic capabilities make this method comparable to standard state-of-the-art methodologies while offering promising advantages. It can leverage extensive research in machine learning and easily integrate with existing libraries, simplifying code development in an unprecedented way. This accessibility and versatility make it appealing for developing new applications, even beyond quantum many-body physics itself. The purpose of this talk is to provide a thorough introduction of the topic with the help of some practical example.
Location: The DIAS Meetingroom Syd (V22-503a-2)
You can also join via Zoom (passcode: 060379).
The event is open to all.
Abstract: Neural network quantum states (NNQS) represent a novel approach in quantum many-body physics using techniques inspired by artificial neural networks combined with standard variational Monte-Carlo methods. The growing computational and algorithmic capabilities make this method comparable to standard state-of-the-art methodologies while offering promising advantages. It can leverage extensive research in machine learning and easily integrate with existing libraries, simplifying code development in an unprecedented way. This accessibility and versatility make it appealing for developing new applications, even beyond quantum many-body physics itself. The purpose of this talk is to provide a thorough introduction of the topic with the help of some practical example.
Location: The DIAS Meetingroom Syd (V22-503a-2)
You can also join via Zoom (passcode: 060379).
The event is open to all.
- Organizer: Quantum Field Theory Center
- Address: Fioniavej 34, 5230 Odense M
- Contact Email: damiapaciarinim@qtc.sdu.dk
- Link to additional material for this talk: https://github.com/netket/netket
- Add to your calendar: https://eom.sdu.dk:443/events/ical/bc6ca5f1-e615-47bc-877f-a77274270d28