Physics 838 Graduate Student Seminar

In 1990, a seminar was initiated for QMC (formerly CNAM/CSR) graduate students in order to present their research to the other students, postdocs, and faculty in the Center. In addition to fostering a rich, collaborative environment in which students learn about the breadth and scope of research being done in QMC, the idea of this series is to teach several crucial skills to our students:

1) How to present their research in a clear and time-efficient way to an audience that was not expert in their area of research;

2) How to best answer questions during their presentations;

3) How to ask good questions when in an audience (or interview), in particular about research beyond their own narrow PhD topic.

In this seminar, students submit formalized feedback to each weekly presenter, providing informative information about presentation style, research content and tips for improvement.

Best Speaker Awards

At the end of each term, a cash prize award is given for the best student and postdoc presentations based on class feedback scores. Previous winners are listed here:

2025 (fall) Jared Dans (student)

2025 (spring)  Jarryd Horn (student)

2024 (fall) Jared Erb (student)

2023 (fall) Jared Erb (student), Peter Czajka (postdoc)

2022 (fall) Sungha Baek (student), Keenan Avers (postdoc)

2020 (fall) Shukai Ma 

2019 (spring) Rui Zhang (student), Tarapada Sarkar (postdoc)

2018 (fall) Chris Eckberg (student), Jen-Hao Yeh (postdoc)

2015 Paul Syers, Jasper Drisko

2014 Sean Fackler, Paul Syers,

2013 Kevin Kirshenbaum, Kirsten Burson

2012 Baladitya Suri, Kristen Burson

2011 (fall) Sergii Pershoguba, Ted Thorbeck

2011 (spring) Anirban Gangopadhyay, Baladitya Suri

2010 (fall) Christian J. Long, Tomasz M. Kott

2010 (spring) Tomasz M. Kott, Kevin Kirshenbaum

2009 (fall) Arun Luykx, Jen-Hao Yeh

PHYS838C Seminar: Han Cai

Calendar
Physics 838 Seminar
Date
03.09.2026 4:00 pm - 5:00 pm
Location
John S. Toll Room 1201

Description

Reversible Superconducting Digital Logic for Energy-Efficient Computing


The rapid growth of computation has raised concerns about energy consumption, motivating the
development of reversible computing, which operates differently from conventional irreversible
logic. Reversible digital logic offers significant energy-efficiency advantages and has potential
applications in astronomy detector readout and quantum information science. Reversible Fluxon
Logic (RFL) is one of the promising approaches, using unpowered, ballistic flux solitons
(fluxons) in long Josephson junctions (LJJs) to encode information. Logical ‘1’ and ‘0’ are
represented by the polarity of fluxons pulses, corresponding to clockwise and counterclockwise
circulating currents, respectively.

We first demonstrate a low-energy transmission line composed of discrete LJJs, consisting of 80
Josephson junctions with 7.5 μA critical currents and connecting inductors. Measurements
confirm ballistic fluxon propagation, with the transmission line operating in the continuous
regime and a fluxon rest energy of approximately 47 zJ. To characterize logic gate operations,
we then implement a two-polarity detector (TPD) that distinguishes fluxons by polarity,
corresponding to the two-bit states in our logic. We observe that one polarity requires a lower
bias current due to a ground loop in the otherwise floating LJJ, which can trap an extra fluxon
along one path. Energy analysis of screening currents qualitatively agrees with experimental data
and provides insight into flux behavior in ballistic reversible circuits. Building on these results,
we design a ballistic flip-flop (BFF) gate with a floating fluxons launcher that avoids added
ground loop. Simulations demonstrate correct internal phases for gate initialization, accurate
digital outputs for both fluxon polarities, and no asymmetry in the output bias. These studies
establish our ballistic gate as a robust, energy-efficient logic element, compatible with placement
near solid-state qubits at mK temperatures, and demonstrate that ballistic logic is practical,
providing a stable foundation for scalable, high-speed, ultra-low-energy computing for next-
generation computing systems.


Advisor: Kevin Osborn