QSE Quantum Seminar: Computing with Physical Systems: Opportunities and Fundamental Limits

Please join us for the QSE Center Quantum Seminar with  Hakan Türeci from Princeton Materials Institute (PMI), who will give the talk "Computing with Physical Systems: Opportunities and Fundamental Limits" on Thursday May 1st.
Location: CM 1 121.

Pizzas will be available before the seminar at 12:00. All PhDs, postdocs, students, and PIs are welcome to join us.

TITLE: "Computing with Physical Systems: Opportunities and Fundamental Limits"

ABSTRACT:
Recent strides in machine learning have shown that computation can be performed by practically any controllable physical system that responds to physical stimuli encoding data [1]. This perspective opens new frontiers for computational approaches using Physical Neural Networks (PNNs) [2, 3, 4] and provides a framework to deepen our understanding of their biological counterparts—neural circuits in living organisms. To fully leverage this potential, PNNs must be trained with a nuanced awareness of the physical nature of signal and noise, where signal is defined relative to the specific computational task. This perspective aligns closely with approaches to determining fundamental limits in sensing but extends these ideas to a new level to encompass broader computational opportunities. I will share some perspectives on how we approach this new domain of inquiry and some recent results.
Based on work with Fangjun Hu, Saeed A. Khan, Gerasimos Angelatos, Marti Vives, Esin Türeci, Graham E. Rowlands, Guilhem J. Ribeill, Nicholas Bronn.

BIO:
​​​​​​​Hakan Türeci is a theoretical physicist with interests in research problems that frequently intersect quantum optics, quantum information science, condensed matter physics, photonics, and lasers. The overarching themes in his group's research revolve around non-equilibrium collective phenomena in optical and microwave platforms. Much of their work is inspired by the possibilities offered by experimentally accessible physics in near-term devices for computing, simulation, machine learning and signal processing.

[1] Aspen Center for Physics Winter Conference, Computing with Physical Systems, https://computingwithphysicalsystems.com/2024/

[2] F. Hu et al. `Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks." Phys. Rev. X 13, 041020 (2023).

[3] S. A. Khan et al., `A neural processing approach to quantum state discrimination", arxiv:2409.03748.

[4] F. Hu et al. `Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data", Nature Commun. 15, 7491 (2024).