activity
20162022
most citedQuantum Crosstalk Robust Quantum Control

66 citations · 190 across the 16 of their papers we have counts for

collaborators

17 papers

quant-ph2022★ 66 cited

Quantum Crosstalk Robust Quantum Control

Zeyuan Zhou, Ryan Sitler, Yasuo Oda +2

The prevalence of quantum crosstalk in current quantum devices poses challenges for achieving high-fidelity quantum logic operations and reliable quantum processing. Through quantu…

quant-ph2022

Optimally Band-Limited Noise Filtering for Single Qubit Gates

Yasuo Oda, Dennis Lucarelli, Kevin Schultz +2

We introduce a quantum control protocol that produces smooth, experimentally implementable control sequences optimized to combat temporally correlated noise for single qubit system…

quant-ph2022★ 2 cited

Provably Optimal Control for Multiplicative Amplitude Control Noise

Colin J. Trout, Kevin Schultz, Paraj Titum +3

We provide a technique to obtain provably optimal control sequences for quantum systems under the influence of time-correlated multiplicative control noise. Utilizing the circuit-l…

quant-ph2022★ 27 cited

Analyzing the impact of time-correlated noise on zero-noise extrapolation

Kevin Schultz, Ryan LaRose, Andrea Mari +4

Zero-noise extrapolation is a quantum error mitigation technique that has typically been studied under the ideal approximation that the noise acting on a quantum device is not time…

cs.MA2021

Bayesian optimization of distributed neurodynamical controller models for spatial navigation

Armin Hadzic, Grace M. Hwang, Kechen Zhang +2

Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms. We previously introduced the NeuroSwar…

quant-ph2021★ 2 cited

Quantifying the Impact of Precision Errors on Quantum Approximate Optimization Algorithms

Gregory Quiroz, Paraj Titum, Phillip Lotshaw +4

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively al…

eess.SP2021

Detecting Anomalous Swarming Agents with Graph Signal Processing

Kevin Schultz, Anshu Saksena, Elizabeth P. Reilly +2

Collective motion among biological organisms such as insects, fish, and birds has motivated considerable interest not only in biology but also in distributed robotic systems. In a…

cond-mat.soft2021

Analyzing Collective Motion Using Graph Fourier Analysis

Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly +2

Collective motion in animal groups, such as swarms of insects, flocks of birds, and schools of fish, are some of the most visually striking examples of emergent behavior. Empirical…

quant-ph2021

Universal Dephasing Noise Injection via Schrodinger Wave Autoregressive Moving Average Models

Andrew Murphy, Jacob Epstein, Gregory Quiroz +10

We present and validate a novel method for noise injection of arbitrary spectra in quantum circuits that can be applied to any system capable of executing arbitrary single qubit ro…

quant-ph2021★ 19 cited

Impact of correlations and heavy-tails on quantum error correction

B. D. Clader, Colin J. Trout, Jeff P. Barnes +3

We show that space- and time-correlated single-qubit rotation errors can lead to high-weight errors in a quantum circuit when the rotation angles are drawn from heavy-tailed distri…

quant-ph2020★ 12 cited

SchWARMA: A model-based approach for time-correlated noise in quantum circuits

Kevin Schultz, Gregory Quiroz, Paraj Titum +1

Temporal noise correlations are ubiquitous in quantum systems, yet often neglected in the analysis of quantum circuits due to the complexity required to accurately characterize and…

eess.SP2020★ 1 cited

Graph Signal Processing for Infrastructure Resilience: Suitability and Future Directions

Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly +2

Graph signal processing (GSP) is an emerging field developed for analyzing signals defined on irregular spatial structures modeled as graphs. Given the considerable literature rega…

quant-ph2020★ 16 cited

Optimal control for quantum detectors

Paraj Titum, Kevin M. Schultz, Alireza Seif +2

Quantum systems are promising candidates for sensing of weak signals as they can provide unrivaled performance when estimating parameters of external fields. However, when trying t…

eess.SP2020★ 1 cited

Graph Signal Processing of Indefinite and Complex Graphs using Directed Variation

Kevin Schultz, Marisel Villafane-Delgado

In the field of graph signal processing (GSP), directed graphs present a particular challenge for the "standard approaches" of GSP to due to their asymmetric nature. The presence o…

cs.MA2019★ 34 cited

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Joseph D. Monaco, Grace M. Hwang, Kevin M. Schultz +1

Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals' natural habitats. I…

quant-ph2017★ 10 cited

Exponential Families for Bayesian Quantum Process Tomography

Kevin Schultz

A Bayesian approach to quantum process tomography has yet to be fully developed due to the lack of appropriate probability distributions on the space of quantum channels. Here, by…

quant-ph2016

Orientation Statistics and Quantum Information

Kevin Schultz

Motivated by the engineering applications of uncertainty quantification, in this work we draw connections between the notions of random quantum states and operations in quantum inf…