Building the next generation of computational tools for quantum many-body problems

DIPC Seminars

Speaker

James LeBlanc
Memorial University of Newfoundland

When

2025/12/16
12:00

Place

DIPC Josebe Olarra Seminar Room

Host

Adolfo Grushin

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The development of computational methods that can deliver both high precision and high accuracy results is a challenging task that has garnered the attention of researchers across physics, chemistry, and materials science. Traditional approaches often face trade-offs between analytic control and numerical efficiency, making it difficult to simultaneously achieve reliability, scalability, and broad applicability. Overcoming these barriers is essential if we are to develop predictive tools that can guide the design of new quantum materials and molecular systems. Feynman diagrammatics provides a particularly appealing approach due, in part, to the general nature of diagrammatic expansions, which can be applied to a wide range of strongly correlated problems. By combining analytic structure with advanced computational techniques, our group has developed methods that can generate solutions for virtually any diagrammatic expansion with no numerical uncertainty. This framework systematically avoids the numerical sign problem that plagues Monte Carlo methods, while providing a transparent connection between many-body theory and practical computation. As a result, the methodology has the potential to transform quantum chemistry, condensed matter physics, and related fields by enabling controlled, accurate predictions in previously inaccessible regimes.

In this general talk, I will introduce some of the key challenges that numerical approaches face when applied to model Hamiltonian systems, including issues of scaling, convergence, and stability. I will then outline a sequence of recent advancements—ranging from analytic interpolation techniques to tensor-network-based contraction strategies —that are paving the way for the next generation of computational tools in materials physics and chemistry. Finally, I will highlight concrete examples of applications, illustrating how these methods can provide new insights into correlated electron models and molecular systems, and pointing toward future directions where such tools may enable meaningful discovery.