Talita Perciano
Lawrence Berkeley National Laboratory
Scientific Data Division
Biography
Dr. Perciano is a Research Scientist in the AI & Learning Systems group and the Computational Biosciences group, at Lawrence Berkeley National Laboratory. She conducts research in the areas of quantum computing, quantum algorithms, image analysis, machine learning, probabilistic graphical models, and high-performance computing motivated by the incredible challenges around scientific data generated by computational models, simulations, and experiments. Her research focuses on mathematical foundations for new methods, on the implementation of scalable methods, and on platform-portability. Her goal is to develop powerful, mathematically-grounded, scalable algorithms that meet the requirements needed to analyze current and future scientific datasets acquired in user data facilities. She has built a diverse collaboration network throughout the years in fields such as materials science, biosciences, chemistry, among others.
SRP Project Title
AQuA-DATA: Advanced Quantum Algorithms for Diverse Applications and Theoretical Advancements in Science
Topical Areas
Applied Mathematics; Computer Science; Other Computer and Information Sciences
Abstract
This project aims to bridge the gap between theoretical quantum advantages and practical scientific applications by developing quantum algorithms and quantum machine learning methods, focusing on efficient data encoding to achieve quantum utility across diverse scientific domains. Our proposal centers on developing a sophisticated suite of quantum algorithms tailored to harness classical data for advanced scientific applications. We emphasize efficient quantum data encoding, coupled with error mitigation approaches, as a pivotal strategy to bridge the gap between theoretical quantum capabilities and practical use across diverse scientific domains. Targeting classical data, which parallels the concept of classical channels in quantum physics, our approach involves specific data reduction techniques and sparsity to refine and simplify the data to serve as input to our quantum algorithms. This makes it more amenable to quantum processing, particularly focusing on computationally intensive components of larger data analysis pipelines. Our methods encompass both pure quantum and hybrid quantum-classical algorithms.
Desired Skills
Computer Science, software development, quantum algorithms, quantum computing, applied math
Lightning Talk Title
Machine Learning and Quantum Algorithms for Science
Keywords
Machine learning; applied math; quantum algorithms; probabilistic graphical models; high performance computing.