36 citations · 71 across the 13 of their papers we have counts for
13 papers
Inherent interpretability provides inherent value in quantum machine learning
Kaitlin Gili, Zachary P. Bradshaw
The field of quantum machine learning (QML) evolved to value models believed to most directly rival those providing utility in classical ML, namely large-scale neural networks. Alt…
From hyperplanes to hyperellipsoids: a pedagogical bridge from linear classifiers to single-qubit mixed-state classifiers
Kaitlin Gili
Quantum machine learning ideas are typically absent from traditional machine learning curricula and are instead introduced in upper-level courses on quantum information processing…
Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning
Kaitlin Gili, Mainak Nistala, Kristen Wendell +1
STEM education researchers are often interested in identifying moments of students' mechanistic reasoning for deeper analysis, but have limited capacity to search through many team…
Combining physics education and machine learning research to measure evidence of students' mechanistic sensemaking
Kaitlin Gili, Kyle Heuton, Astha Shah +2
Advances in machine learning (ML) offer new possibilities for science education research. We report on early progress in the design of an ML-based tool to analyze students' mechani…
Stressing Out Modern Quantum Hardware: Performance Evaluation and Execution Insights
Aliza U. Siddiqui, Kaitlin Gili, Chris Ballance
Quantum hardware is progressing at a rapid pace and, alongside this progression, it is vital to challenge the capabilities of these machines using functionally complex algorithms.…
Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models
Michael T. Wojnowicz, Kaitlin Gili, Preetish Rath +7
We seek a computationally efficient model for a collection of time series arising from multiple interacting entities (a.k.a. "agents"). Recent models of temporal patterns across in…
An inductive bias from quantum mechanics: learning order effects with non-commuting measurements
Kaitlin Gili, Guillermo Alonso, Maria Schuld
There are two major approaches to building good machine learning algorithms: feeding lots of data into large models, or picking a model class with an ''inductive bias'' that suits…
A supplemental investigation of non-linearity in quantum generative models with respect to simulatability and optimization
Kaitlin Gili, Rohan S. Kumar, Mykolas Sveistrys +1
Recent work has demonstrated the utility of introducing non-linearity through repeat-until-success (RUS) sub-routines into quantum circuits for generative modeling. As a follow-up…
Do Quantum Circuit Born Machines Generalize?
Kaitlin Gili, Mohamed Hibat-Allah, Marta Mauri +2
In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distributio…
Introducing Non-Linear Activations into Quantum Generative Models
Kaitlin Gili, Mykolas Sveistrys, Chris Ballance
Due to the linearity of quantum mechanics, it remains a challenge to design quantum generative machine learning models that embed non-linear activations into the evolution of the s…
Generalization Metrics for Practical Quantum Advantage in Generative Models
Kaitlin Gili, Marta Mauri, Alejandro Perdomo-Ortiz
As the quantum computing community gravitates towards understanding the practical benefits of quantum computers, having a clear definition and evaluation scheme for assessing pract…
Long-time simulations with high fidelity on quantum hardware
Joe Gibbs, Kaitlin Gili, Zoë Holmes +5
Moderate-size quantum computers are now publicly accessible over the cloud, opening the exciting possibility of performing dynamical simulations of quantum systems. However, while…
Quantum Unsupervised and Supervised Learning on Superconducting Processors
Abhijat Sarma, Rupak Chatterjee, Kaitlin Gili +1
Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the dataset, the computa…