Kapit Group


Over the course of the past six years, my research has explored a broad range of fascinating topics that have led to an increasing focus on a pair of core questions: how quickly can quantum computers surpass the best classical machines on practical, real-world problems, and are there simpler alternatives to universal error correction to achieve such an advantage? The power of quantum computers to solve hard problems promises to be transformative; quantum computers exploit the exotic properties of quantum mechanics (chiefly entanglement and superposition) to solve a wide range of difficult problems potentially exponentially faster than any known algorithm in their classical counterparts. However, real quantum bits, or “qubits,” are noisy and plagued by random errors (uncontrolled operations that alter the state of the qubit, generally without the operator’s knowledge), and for many algorithms a single error is enough to totally invalidate the result. Such noise and other error sources have stymied attempts to fully realize quantum advantage, rapidly scrambling quantum states before anything but the smallest quantum algorithms can complete. In this vein, topological codes such as the surface code promise error-free quantum computing given per-operation error thresholds that are challenging but achievable in the near future, but the overhead is formidable, with a typical figure of merit of around a thousand physical qubits per logical bit, rising to ten thousand when other considerations such as the need for magic state distillation are taken into account. Consequently, the most optimistic estimates (e.g. the timelines laid out by Google and IBM) promise fault tolerant quantum computers at the end of this decade, and a variety of known and unknown obstacles might push their realization well into the 2030s. One of the most important questions in the field, then, is whether we can achieve quantum advantage before such machines are available, and if so, for what problems and how?

My group attacks this question through innovative research along three simultaneous tracks. The first, autonomous error correction and novel hardware design, is focused on highly efficient self-correcting devices, where quantum errors are corrected automatically by dissipative elements without the need for complex control circuitry. These small logical qubits could be used as qubit primitives in a larger code, reducing hardware overhead, or as the logical qubits in a noisy device (given appropriately noise-tolerant algorithms to run on it). The second track, noise tolerant quantum optimization, seeks to radically improve quantum annealing to overcome the problems that have prevented it from achieving real-world quantum advantage on a host of problems in optimization and artificial intelligence. This work has also inspired novel algorithms for gate model quantum computers, both noisy and fault-tolerant (not yet published). Finally, the third track focuses on quantum simulation, one of the core applications of quantum computing. My group’s research in this area probes novel quantum dynamics and the simulation of topological matter.

I am also the current director of Mines’ new interdisciplinary program in Quantum Engineering, that aims to provide students with a comprehensive introduction to quantum information science as well as critical skills (such as quantum programming and cryogenic experimental techniques) for careers in quantum technology. For more information, please visit quantum.mines.edu, and don’t hesitate to contact me with questions.

Research Group
Current Graduate Students

Nick Materise (PhD expected 2023)
Carla Quispe Flores (PhD expected 2024)
Hakan Ayaz (MS expected 2022)
Caleb Rotello (MS expected 2022)

Undergraduate Research Assistants

Paul Varosy
Jacob Millar

Group Alumni

Eric Jones (PhD 2020— postdoctoral researcher at NREL)
David Rodriguez Perez (PhD 2021— research scientist at Rigetti Computing)
Zhijie Tang (PhD 2021— postdoctoral researcher at Zhejiang University)

Current Experimental Collaborations

Prof. David Schuster (schusterlab.uchicago.edu) — self-correcting devices

MIT Lincoln Lab (https://www.ll.mit.edu/about/facilities/quantum-computing-laboratory) and Northrop Grumman Corporation (https://www.northropgrumman.com/what-we-do/disruptive-concepts-and-technologies-quantum-technology/), with Prof. Vadim Oganesyan (https://www.csi.cuny.edu/campus-directory/vadim-oganesyan), Prof. David Schwab (https://www.gc.cuny.edu/people/david-schwab) and the NASA Quantum Artificial Intelligence Laboratory (https://ti.arc.nasa.gov/tech/dash/groups/quail/), through the DARPA Aqua Deep and RQMLS programs — noise tolerant quantum optimization and quantum machine learning

Fermilab SQMS (sqms.fnal.gov), a collaboration that includes Rigetti Computing (rigetti.com), Prof. Jens Koch (https://sites.northwestern.edu/koch/) and Prof. Srivatsan Chakram (https://sites.rutgers.edu/chakram-lab/) — novel qubit architectures and error correction schemes

Dr. Corey Rae McRae (https://www.nist.gov/pml/quantum-electromagnetics/quantum-processing), Prof. Javad Shabani (https://wp.nyu.edu/shabanilab/) and Dr. David Pappas — materials characterization for quantum computing and novel qubit coupling architectures

Google Quantum AI (https://quantumai.google) — quantum simulation

Prof. Daniel Lidar (http://qserver.usc.edu/blog/2016/02/daniel-lidar/) and Prof. Nicolę Yunger Halpern (https://jqi.umd.edu/people/nicole-yunger-halpern) — quantum simulation and quantum thermodynamics

Funding Sources and Open Positions

My research is currently funded by the National Science Foundation (CAREER grant PHY-1653820), the Department of Energy through the SQMS NQI Center (sqms.fnal.gov), and DARPA through the RQMLS program (https://www.darpa.mil/program/reversible-quantum-machine-learning-and-simulation).

I do not currently plan to take on a new PhD student in fall 2022. However, I am looking to recruit a postdoc as part of the SQMS collaboration; please reach out if interested.

Selected Publications
Selected Papers on Autonomous Error Correction

Error-divisible quantum gates, D. Rodriguez Perez et al, arXiv:2110.11537 (2021)

Improved autonomous error correction using variable dissipation in small logical qubit architectures, D. Rodriguez Perez and E. Kapit, QST 6, 015006 (2020)

Error-transparent quantum gates for small logical qubit architectures, E. Kapit, PRL 120, 050503 (2018)

Hardware-Efficient and Fully Autonomous Quantum Error Correction in Superconducting Circuits, E. Kapit, PRL 116, 150501 (2016)

Selected Papers on Noise Tolerant Quantum Optimization

Noise-tolerant quantum speedups in quantum annealing without fine tuning, E. Kapit and V. Oganesyan, Quant. Sci. Tech. 6, 025013 (2021).

Survey of unconventional quantum annealing methods applied to a difficult trial problem, Z. Tang and E. Kapit, Phys. Rev. A. 103, 032612 (2021).

Selected Papers on Quantum Simulation

Small-world complex network generation on a digital quantum processor, E.B. Jones et al, arxiv:2111.00167 (2021)

Entanglement and complexity of interacting qubits subject to asymmetric noise, E. Kapit et al, Phys. Rev. Research 2, 043042 (2020)

Chiral groundstate currents of interacting photons in a synthetic magnetic field, P. Roushan et al, Nature Physics 13, 146 (2017)

Induced Self Stabilization in Fractional Quantum Hall States of Light, E. Kapit et al, PRX 4, 031039 (2014)

Exact Parent Hamiltonian for the Quantum Hall States in a Lattice, E. Kapit and E. Mueller, PRL 105, 215303 (2010)