Research

my research but more descriptive.

now

I’m finishing up my work at the Johns Hopkins University Applied Physics Laboratory, across quantum-inspired algorithms (tensor networks) and machine learning for scaling holographic quantum circuits. Specifically, I’m developing techniques to address scalability in quantum computing.

At UIUC, I have also begun mentoring students in the Bezryadin research group on projects that extend my previous papers.

previously

I have completed a lot of other really different projects at UIUC and JHU APL. Below is a detailed recollection of the progression of my research ability, starting from my freshman college year until now.

In my freshman year, I joined Professor Alexey Bezryadin’s lab, where I began investigating generalized Josephson Junction superconducting quantum inteference devices, aka JJ-SQUIDs. I first developed a critical current model of this device. Then, I published a python package to investigate the effects of fabrication disorder in JJ-SQUIDs and also found several rotational symmetries. I documented my work in an informal write-up as my first technical write-up. Later, I built a parallelized inverse JJ-SQUID tool that approximates the JJ-SQUID geometry given experiment data using parallelized optimization algorithms, written in C++. This tool is now in use by other UIUC physics research groups. I was also the first person in my lab to revive a broken time-resolved circuit-qed measurement setup, allowing for new types of measurements to be done.

In my sophomore year, I investigated multiple nanowire superconducting quantum interference devices (MW-SQUID), which are metastable. In my first paper (first-author), I conducted an in-depth analysis of the metastable properties of MW-SQUIDs. I generalized the little parks effect and proved a isomorphism between the MW-SQUID’s symmetry and the Charge-Parity-Time symmetry from Particle Physics. In my second paper (first-author), I showed that breaking space and time symmetry in the MW-SQUID always produces superconducting diodes. Actually, a perfect diode can arise too!

In parallel, I also led a UIUC NASA competition team, competing in the NASA Human Lander Challenge, where we focused on making meaningful progress on problems in NASA’s hardest field: Cryogenics. Throughout my sophomore year, I led the vision of a hardware-aware, fault tolerant cryogenic propellent transfer strategy that mitigates, minimizes, and monitors propellant boil-off throughout line chilldown, tank chilldown, and single-phase propellant transfer. Our design included a new hardware-aware line chilldown technique that maintained heat transfer efficiency up to 80% while eliminating nearby hardware damage, an 90% accurate two-phase flow detection algorithm with capacitance measurements, and a hardware-efficient configuration to detect completion of tank chilldown. Our deliverables included a proposal (9 page, link not included) and technical (15 page) report, a proposal video, a technical poster, and slides. We were awarded $9k and invited to present our work to NASA, where our ideas were integrated into the NASA mission architecture.

I then began my summer research internship at the Johns Hopkins University Applied Physics Laboratory, studying holographic quantum tensor networks with Dr. Michael Wall. I developed and implemented a holographic generative quantum machine learning algorithm (based on this paper), investigated quantum annealing for high dimensional QUBO problems, and also attempted to use reinforcement learning for quantum compilation purposes. I was rehired for my junior year to work on quantum compilation problems.

In my junior year, I investigated hardware applications of nanowires. I published a third paper (first-author) laying the theoretical foundations for of the first-ever nanowire transmon qubit. This solved a 20 year problem in the nanowire community. We also filed for a patent on this concept. As well, the diode prediction I made in my 2nd paper was experimentally confirmed in a fourth paper (2nd-author). In parallel, I continued working at APL, where I was figuring out how to efficiently scale holographic quantum circuits towards classically intractable regimes.

I then entered my 2nd summer research internship at the Johns Hopkins University Applied Physics Laboratory, working on three projects. Firstly, I designed and benchmarked a hybrid machine learning architecture used to scale up holographic quantum circuits. A fifth paper (first-author) will be released soon on this. Secondly, I developed a Matrix Product State-based solver for non-linear diffusion-advection partial differential equations. I solved several important tensor network problems and showed a classical scaling advantage in time and memory with this framework. I am preparing a sixth paper (first-author) on this work. Thirdly, I wrote and validated a semi-classical cold atom model for non-inertial state estimation. An internal memo (first-author) will be distributed to APL staff.

Now, I am entering my senior year in my undergraduate career. See “now” for what I’m currently up to.

presentations

  • "Superconducting Nanowires for Digital and Quantum Logic" @ Astronaut Scholarship Technical Conference ( slides )
  • "A Machine Learning Architecture for scaling Holographic Quantum Circuits" @ Johns Hopkins Applied Physics Lab (deliverables not included)
  • "Quantum Circuits, Algorithms, and Sensors" @ Johns Hopkins Applied Physics Lab (deliverables not included)
  • "Simulating Diffusion-Advection via Matrix Product States" @ Johns Hopkins Applied Physics Lab (deliverables not included)
  • "Superconducting Nanowires for Digital and Quantum Logic" @ UIUC Undergraduate Research Symposium ( slides )
  • "Superconducting Quantum Interference in Nanowire Networks for Digital and Quantum Logic" @ APS Global Summit ( poster )
  • "Using AdS/CFT for better Quantum Computing" @ UIUC Undergraduate Physics Seminars ( slides )
  • "Multiple Nanowire Superconducting Quantum Interference Devices: Symmetries, Critical Currents, and Diode Effect" @ Chicago Quantum Exchange ( poster )
  • "Holographic Quantum Tensor Networks and Quantum Annealing" @ Johns Hopkins Applied Physics Lab (deliverables not included)
  • "Model Fitting Algorithm for disordered Josephson Junction Arrays" @ UIUC Engineering Symposium ( poster )
  • "Efficient Cryogenic Low Invasive Propellant Supply Exchange" @ NASA Marshall Space Flight Center ( slides | poster )
  • "Analyzing Disorder and Symmetries in random Josephson Junction Arrays" @ UIUC Undergraduate Research Symposium ( poster )

other stuff

  • Critical current model of many Josephson Junction Array (code)
  • Python package used to analyze fabrication disorder (link)
  • Critical current model of many nanowire SQUID (code)
  • Inverse design solver for many Josephson Junction Array (code)
  • Technical report submitted to 2025 NASA Human Lander Challenge (link)
  • Proposal rendered video submitted to 2025 NASA Human Lander Challenge (link)