10 citations · 21 across the 12 of their papers we have counts for
12 papers
Induced random mixed states are symmetric Dirichlet mixtures
Brian T. Kirby, Alexander C. B. Greenwood, Sanjaya Lohani +1
We establish an exact equivalence in distribution between -dimensional random mixed states induced by partial traces over -dimensional environments and the Mai-Alquier distri…
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…
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…
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…
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…
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…
Enhancing Quantum State Reconstruction with Structured Classical Shadows
Zhen Qin, Joseph M. Lukens, Brian T. Kirby +1
Quantum state tomography (QST) remains the prevailing method for benchmarking and verifying quantum devices; however, its application to large quantum systems is rendered impractic…
Deployed quantum link characterization via Bayesian ancilla-assisted process tomography
Arefur Rahman, Noah I. Wasserbeck, Zachary Goisman +5
The development of large-scale quantum networks requires reliable quantum channels, the quality of which can be quantified by the framework of quantum process tomography. In this w…
Building a controlled-NOT gate between polarization and frequency
Hsuan-Hao Lu, Joseph M. Lukens, Muneer Alshowkan +2
By harnessing multiple degrees of freedom (DoFs) within a single photon, controlled quantum unitaries, such as the two-qubit controlled-NOT (CNOT) gate, play a pivotal role in adva…
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…
On the connection between least squares, regularization, and classical shadows
Zhihui Zhu, Joseph M. Lukens, Brian T. Kirby
Classical shadows (CS) offer a resource-efficient means to estimate quantum observables, circumventing the need for exhaustive state tomography. Here, we clarify and explore the co…
Origin of Quantum Noise and Decoherence in Distributed Amplifiers
J. D. Franson, B. T. Kirby
The use of distributed amplifiers may have some potential advantages for the transmission of quantum information through optical fibers. In addition to the quantum noise introduced…