Madeline Mitchell | Searcharxiv

arXiv2025

Making Quantum Accessible: A Seven-Category Framework for K-12 Quantum Education

We conducted a literature review and expert interviews to determine the most common methods being used to teach quantum physics and quantum computing concepts to primary and secondary students. Based on the findings of this review, we provide a framework of seven categories of teaching approaches for teaching mathematically accessible quantum concepts; they are Defamiliarization, Quantum Picturalism, Spin-First Approach, Einstein-First Approach, Many Paths Approach, Historical Development Approach and Game-based Quantum Learning. We summarise each of these teaching methods and overview their advantages and disadvantages of each method. Our framework makes it easy for physics educators to embrace the diverse methods of teaching quantum physics and quantum computing at the primary and secondary level.

physics.ed-ph↗

arXiv2023

Learning Sparsity-Promoting Regularizers using Bilevel Optimization

We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images. Sparsity-promoting regularization is a key ingredient in solving modern signal reconstruction problems; however, the operators underlying these regularizers are usually either designed by hand or learned from data in an unsupervised way. The recent success of supervised learning (mainly convolutional neural networks) in solving image reconstruction problems suggests that it could be a fruitful approach to designing regularizers. Towards this end, we propose to denoise signals using a variational formulation with a parametric, sparsity-promoting regularizer, where the parameters of the regularizer are learned to minimize the mean squared error of reconstructions on a training set of ground truth image and measurement pairs. Training involves solving a challenging bilievel optimization problem; we derive an expression for the gradient of the training loss using the closed-form solution of the denoising problem and provide an accompanying gradient descent algorithm to minimize it. Our experiments with structured 1D signals and natural images show that the proposed method can learn an operator that outperforms well-known regularizers (total variation, DCT-sparsity, and unsupervised dictionary learning) and collaborative filtering for denoising. While the approach we present is specific to denoising, we believe that it could be adapted to the larger class of inverse problems with linear measurement models, giving it applicability in a wide range of signal reconstruction settings.

cs.LG↗