About us
This is a group for anyone interested in Quantum Computing and Quantum Information in the Washington DC area. I started this group to find and meet the people around who will do events and participate in the discussion of the topics related to Quantum Computing.
Upcoming events
3
Mesh-Free Numerical Method for Dirichlet Eigenpairs of the Laplacian
Location not specified yetTitle: Mesh-Free Numerical Method for Dirichlet Eigenpairs of the Laplacian with Potential
Date: September 7 2026 (Monday Holiday) 10:00 am - Noon EDT
Summary: This paper is concerned with the numerical approximation of the L^2 Dirichlet eigenpairs of the operator -delta + V on a simply connected C^2 bounded domain containing the origin, where is a radial potential. We propose a mesh-free method inspired by the Method of Particular Solutions for the Laplacian (i.e. ). Extending this approach to general radial potentials is challenging due to the lack of explicit basis functions analogous to Bessel functions. To overcome this difficulty, we consider the equation on a ball containing , without imposing boundary conditions, for a collection of values forming a fine discretisation of the interval in which eigenvalues are sought. By rewriting the problem in polar coordinates and applying a Fourier expansion with respect to the angular variable, we obtain a decoupled system of ordinary differential equations. These equations are solved numerically using a one-dimensional Finite Element Method, yielding a family of basis functions that are solutions of the equation on the ball and are independent of the domain . Dirichlet eigenvalues of are then approximated by minimising the boundary values on among linear combinations of the basis functions and identifying those values of for which the computed minimum is sufficiently small. The proposed method is highly memory-efficient compared to the standard Finite Element approach.
Speaker: Dr. Dragoș Manea is a mathematician specialising in mathematical analysis and applied mathematics. He holds a Master of Science degree from the University of Oxford and completed his PhD in Mathematics in 2025. He is currently a Research Assistant at the “Simion Stoilow” Institute of Mathematics of the Romanian Academy in Bucharest, Romania. His doctoral research focused on the theoretical and numerical analysis of evolutionary partial differential equations, with particular emphasis on deriving asymptotic results. In addition, he is interested in the numerical treatment of elliptic inverse and eigenvalue problems using non-standard approaches that avoid explicit meshing of the computational domain. Beyond PDE theory, his work also explores optimisation methods and their applications across a wide spectrum of problems, ranging from theoretical questions — such as the asymptotic analysis of solutions to the Schrödinger equation — to practical engineering applications, including the optimisation of urban traffic and aircraft trajectories.
Moderators: Dr. Pawel Gora, CEO of Quantum AI Foundation Quantum AI Foundation Dr. Sebastian Zajac, member of QPoland QPoland - QWorld16 attendees
Quantum Deep Learning: A Comprehensive Review
Location not specified yetDate: Dec 5 2026 10:00 am - Noon EST
Title: Quantum Deep Learning: A Comprehensive Review
Abstract:
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced based on specific resource constraints. Distinct from broader quantum machine learning, QDL emphasizes compositional depth at the pipeline level and the integration of quantum or quantum-inspired components within end-to-end workflows. This review provides an operational definition of QDL and introduces a taxonomy comprising four primary paradigms: hybrid quantum-classical models, quantum deep neural networks, quantum algorithms for deep learning primitives, and quantum-inspired classical algorithms. Theoretical principles are connected to advanced architectures, software toolchains, and experimental demonstrations across superconducting, trapped-ion, photonic, semiconductor spin, and neutral-atom systems, as well as quantum annealers. Claims of quantum advantage are critically assessed by distinguishing provable complexity-theoretic separations from empirical observations. The analysis characterizes trade-offs between model expressivity, trainability, and classical simulability, while systematically detailing the bottlenecks imposed by optimization landscapes, input-output access models, and hardware constraints. Applications are surveyed in domains encompassing image classification, natural language processing, scientific discovery, quantum data processing, and quantum optimal control, underscoring fair benchmarking against optimized classical counterparts and a comprehensive assessment of resource requirements. This review serves as a tutorial entry point for graduate students while guiding readers to specialized literature. It concludes with a verification-aware roadmap to transition QDL from near-term demonstrations to scalable and fault-tolerant implementations.
Link to the paper: https://arxiv.org/abs/2603.06644Speaker: Dr. Yanjun Ji has an interdisciplinary research background in Physics and Computer Science. In 2020, she completed her Master's degree in Theoretical Physics at Saarland University. In 2024, she earned her Ph.D. in Computer Science at the University of Stuttgart. She is currently a postdoctoral researcher at Forschungszentrum Jülich. She is committed to interdisciplinary research at the cutting-edge convergence of quantum computing and artificial intelligence. Her research encompasses both foundational theoretical investigations and the development of practical applications, aiming to serve society and benefit humanity.
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