3.3k citations · 14.8k across the 91 of their papers we have counts for
92 papers
Analyzing the quantum approximate optimization algorithm: ansätze, symmetries, and Lie algebras
Sujay Kazi, Martín Larocca, Marco Farinati +3
The Quantum Approximate Optimization Algorithm (QAOA) has been proposed as a method to obtain approximate solutions for combinatorial optimization tasks. In this work, we study the…
Thermodynamic Linear Algebra
Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon +5
Linear algebraic primitives are at the core of many modern algorithms in engineering, science, and machine learning. Hence, accelerating these primitives with novel computing hardw…
The power and limitations of learning quantum dynamics incoherently
Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph +4
Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share qu…
Challenges and Opportunities in Quantum Machine Learning
M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang +2
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with app…
Thermodynamic AI and the fluctuation frontier
Patrick J. Coles, Collin Szczepanski, Denis Melanson +3
Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic fluctuations. We connect these physics-inspired AI algorithms by unifying them under a si…
Resource frugal optimizer for quantum machine learning
Charles Moussa, Max Hunter Gordon, Michal Baczyk +3
Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms hav…