The UConn Quantum Computing Club invites you to a special discussion exploring an exciting and less traditional direction in quantum computing and machine learning.
This session will be virtually led by Professor Victor Batista.
Quantum machine learning is often framed around qubit-based systems, but an emerging and powerful alternative uses bosonic quantum computers built from qumodes rather than simple two-level systems.
In this talk, we’ll explore how these unconventional platforms open new possibilities for solving problems in chemistry, physics, and beyond.
What You’ll Learn:
• The difference between qubit-based and bosonic (qumode) quantum computers
• Why bosonic systems are naturally suited for chemistry and physics problems
• How these platforms can simulate molecular spectra, chemical dynamics, and graph-based problems
• The potential advantages, including reduced circuit depth compared to standard approaches
What to Expect:
An accessible, concept-driven discussion designed to give undergraduate students an intuitive understanding of how new quantum hardware expands the scope of quantum machine learning.
Recommended Background:
No prior experience required. Familiarity with basic quantum computing or machine learning concepts may be helpful but is not necessary.
This session is open to all undergraduate students, with graduate students encouraged to join the discussion. If you can't make it in person, you can use this link: https://teams.microsoft.com/meet/26958671969776?p=EXIzWkeBVNrQRv0CKy
Date & Time Tuesday, April 7 | 6:00–7:00 PM
Location Gant South 117 (University of Connecticut, Storrs)
For more information, contact: President of QCC at mrb24003@uconn.edu