2026
Correcting coherent quantum errors by going with the flow
Wayne M. Witzel, Anand Ganti, Tzvetan S. Metodi
arXiv:2602.21076
2025
A small and interesting architecture for early fault-tolerant quantum computers
Jacob S. Nelson, Andrew J. Landahl, and Andrew D. Baczewski
arXiv:2507.20387
Application scale quantum circuit compilation with controlled error
Justin Kalloor, Lucas Kovalsky, Mathias Weiden, John Kubiatowicz, Ed Younis, Costin Iancu, Mohan Sarovar
arXiv:2510.18000
Real-time adaptation of quantum noise channel estimates
Lucas Daguerre, Mohan Sarovar.
arXiv:2501.18685 | Phys. Rev. A, 111, 062609 (2025)
2024
Quantum computation of stopping power for inertial fusion target design
Nicholas C. Rubin, Dominic W. Berry, Alina Kononov, Fionn D. Malone, Tanuj Khattar, Alec White, Joonho Lee, Harmut Neven, Ryan Babbush, and Andrew D. Baczewski
arXiv:2308.12352 | PNAS 121, 23 (2024)
Exponential improvements in the simulation of lattice gauge theories using near-optimal techniques
Mason Rhodes, Michael Kreshchuk, and Shivesh Pathak
arXiv:2405.10416
Requirements for building effective Hamiltonians using quantum-enhanced density matrix downfolding
Shivesh Pathak, Antonio E. Russo, Stefan Seritan, Alicia B. Magann, Eric Bobrow, Andrew J. Landahl, and Andrew D. Baczewski
arXiv:2403.01043
An assessment of quantum phase estimation protocols for early fault-tolerant quantum computers
Jacob S. Nelson and Andrew D. Baczewski
arXiv:2403.00077
2023
Verifying quantum phase estimation using an expressive theorem-proving assistant
Wayne M. Witzel, Warren D. Craft, Robert Carr, Deepak Kapur
arXiv:2304.02183 | Phys. Rev. A.108, 052609 (2023)
Self-Healing of Trotter Error in Digital Adiabatic State Preparation
Lucas K. Kovalsky, Fernando A. Calderon-Vargas, Matthew D. Grace, Alicia B. Magann, James B. Larsen, Andrew D. Baczewski, Mohan Sarovar
arXiv:2209.06242 | Phys. Rev. Lett. 131, 060602 (2023)
A fast quantum route to random numbers
Mohan Sarovar
Nature (News & Views), 619, 256 (2023)
Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods
Ryan Babbush, William J. Huggins, Dominic W. Berry, Shu Fay Ung, Andrew Zhao, David R. Reichman, Hartmut Neven, Andrew D. Baczewski, and Joonho Lee
arXiv:2301.01203 | Nature Communications 14, 4058 (2023)
Quantum-inspired tempering for ground state approximation using artificial neural networks
Tameem Albash, Conor Smith, Quinn Campbell, and Andrew D. Baczewski
arXiv:2210.11405 | SciPost Phys. 14, 121 (2023)
Quantifying T-gate-count improvements for ground-state-energy estimation with near-optimal state preparation
Shivesh Pathak, Antonio E. Russo, Stefan K. Seritan, and Andrew D. Baczewski
arXiv:2210.10872 | Phys. Rev. A 107, L040601 (2023)
Nanoscale Architecture for Frequency-Resolving Single-Photon Detectors
Steve M. Young, Mohan Sarovar, François Léonard.
arXiv:2205.05817 | Commun. Phys. 6, 71 (2023)
2022
Shining light on data
Akshat Kumar, Mohan Sarovar
Machine Learning and the Physical Sciences workshop, NeurIPS 2022.
Shining light on data: geometric data analysis through quantum dynamics
Akshat Kumar, Mohan Sarovar
arXiv:2212.00682
Establishing trust in quantum computations
Timothy Proctor, Stefan Seritan, Erik Nielsen, Kenneth Rudinger, Kevin Young, Robin Blume-Kohout, Mohan Sarovar
arXiv:2204.07568
Quantum circuit debugging and sensitivity analysis via local inversions
Fernando A. Calderon-Vargas, Timothy Proctor, Kenneth Rudinger, Mohan Sarovar
arXiv:2204.06056 | Quantum 7, 921 (2023)
Surrogate-based optimization for variational quantum algorithms
Ryan Shaffer, Lucas Kocia, Mohan Sarovar
arXiv:2204.05451
