activity
20242026
collaborators

9 papers

quant-ph2026

A quantum game of telephone

Arefur Rahman, Matthew L. Stevens, Cory M. Nunn +3

Characterizing multinode quantum networks without ubiquitous local entanglement sources presents significant experimental challenges. We introduce ``quantum telephone,'' an iterati…

quant-ph2026

Structured Factorization Approaches for Quantum State Tomography

Zhen Qin, Joseph M. Lukens, Brian T. Kirby +1

Since the complexity of quantum state tomography (QST) scales exponentially with system size, exploiting priors such as low-rankness, tensor-network structures, and neural-network…

quant-ph2026

Re-examining the Role of State Texture in Gate Identification and Fixed-Point Resource Theories

Alexander C. B. Greenwood, Joseph M. Lukens, Li Qian +1

A protocol for identifying controlled-NOT (CNOT) gates versus single-qubit-only gates in universal quantum circuits using randomized input states was recently shown to be intimatel…

quant-ph2025

In situ quantum verification of polarization-stabilized optical channels

Matthew L. Stevens, Noah I. Wasserbeck, Zachary Goisman +8

The active stabilization of polarization channels is a task of growing importance as quantum networks move to deployed demonstrations over existing fiber infrastructure. However, t…

stat.ML2025

Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles

Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4

This work introduces a new method designed for Bayesian deep learning called scalable Bayesian Monte Carlo (SBMC). The method is comprised of a model and an algorithm. The model in…

stat.ML2025

Comparison of parallel SMC and MCMC for Bayesian deep learning

Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4

This work systematically compares parallel implementations of consistent (asymptotically unbiased) Bayesian deep learning algorithms: sequential Monte Carlo sampler (SMC$_\parallel…