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Search Publications by: Jacob Taylor (Fed)

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Displaying 1 - 25 of 154

Mechanical Sensors for Planck-scale Dark Matter Searches via Long-range Forces

February 14, 2025
Author(s)
Jacob Taylor, Juehang Qin, Dorian Amaral, sunil bhave, Erqian Cai, Daniel Carney, Raphael Lang, Shengchao Li, Claire Marvinney, Alberto Marino, Jared Newton, Christopher Tunnell
Dark matter candidates with masses around the Planck-scale are theoretically well-motivated and have been the subject of numerous studies; it has also been suggested that it might be possible to search for dark matter solely via gravitational interactions

Data needs and challenges for quantum dot devices automation

October 31, 2024
Author(s)
Justyna Zwolak, Jacob Taylor, Reed Andrews, Jared Benson, Garnett Bryant, Donovan Buterakos, Anasua Chatterjee, Sankar Das Sarma, Mark Eriksson, Eliska Greplova, Michael Gullans, Fabian Hader, Tyler Kovach, Pranav S. Mundada, Mick Ramsey, Torbjoern Rasmussen, Brandon Severin, Anthony Sigillito, Brennan Undseth, Brian Weber
Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be

Collision-resolved pressure sensing

April 11, 2024
Author(s)
Daniel Carney, Daniel Barker, Thomas W. LeBrun, David Moore, Jacob Taylor
Heat and pressure are ultimately transmitted via quantized degrees of freedom, like gas particles and phonons. While a continuous Brownian description of these noise sources is adequate to model measurements with relatively long integration times

Precision Bounds on Continuous-Variable State Tomography Using Classical Shadows

March 18, 2024
Author(s)
Srilekha Gandhari, Victor Albert, Thomas Gerrits, Jacob Taylor, Michael Gullans
Shadow tomography is a framework for constructing succinct descriptions of quantum states, called classical shadows, with powerful methods to bound the estimators used. Classical shadows are well-studied in the discrete-variable case, which consists of sta

Colloquium: Advances in automation of quantum dot devices control

February 17, 2023
Author(s)
Justyna Zwolak, Jacob Taylor
Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual

Toward Robust Autotuning of Noisy Quantum Dot Devices

February 25, 2022
Author(s)
Joshua Ziegler, Thomas McJunkin, Emily Joseph, Sandesh Kalantre, Benjamin Harpt, Donald Savage, Max Lagally, Mark Eriksson, Jacob Taylor, Justyna Zwolak
The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this

Theoretical Bounds on Data Requirements for the Ray-Based Classification

November 10, 2021
Author(s)
Brian Weber, Sandesh Kalantre, Thomas McJunkin, Jacob Taylor, Justyna Zwolak
The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed

Trapped electrons and ions as particle detectors

August 5, 2021
Author(s)
Jacob Taylor, Daniel Carney, Hartmut Haffner, David Moore
Electrons and ions trapped with electromagnetic fields have long served as important high- precision metrological instruments, and more recently have also been proposed as a platform for quantum information processing. Here we point out that these systems

Ray-based framework for state identification in quantum dot devices

June 7, 2021
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, Samuel Neyens, Evan MacQuarrie, Mark A. Eriksson, Jacob Taylor
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement

Mechanical Quantum Sensing in the Search for Dark Matter

August 13, 2020
Author(s)
Jacob Taylor, Gadi Afek, Sunil Bhave, Daniel Carney, Gordan Krnjaic, David Moore, Robinjeet Singh, Cindy Regal, Benjamin M. Brubaker, Andrew Geraci, Jonathan D. Cripe, Sohitri Ghosh, Jack Harris, Anson Hook, Jonathan Kunjummen, Rafael Lang, Li Tongcang, Tongyan Lin, Zhen Liu, Joseph Lykken, Lorenzo Magrini, Jack Manley, Nobuyuki Matsumoto, Alissa Monte, Fernando Monteiro, Thomas Purdy, C. J. Riedel, Swati Singh, Kanupriya Sinha, Juehang Qin, Dalziel Wilson, Yue Zhao
Numerous astrophysical and cosmological observations are best explained by the existence of dark matter, a mass density which interacts only very weakly with visible, baryonic matter. Searching for the extremely weak signals produced by this dark matter

Auto-tuning of double dot devices it in situ with machine learning

March 31, 2020
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, J. P. Dodson, Evan MacQuarrie, D. E. Savage, M. G. Lagally, S N. Coppersmith, Mark A. Eriksson, Jacob Taylor
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time- consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the \it in situ} implementation of a

Ray-based classification framework for high-dimensional data

February 3, 2020
Author(s)
Justyna Zwolak, Jacob Taylor, Sandesh Kalantre, Thomas McJunkin, Brian Weber
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than

Machine Learning techniques for state recognition and auto-tuning in quantum dots

January 20, 2019
Author(s)
Sandesh Kalantre, Justyna Zwolak, Stephen Ragole, Xingyao Wu, Neil M. Zimmerman, Michael Stewart, Jacob Taylor
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many cases, including in