Tangible programming languages (TPL) involve physical objects, often interlocking blocks, that represent computer programming elements. Users connect TPL blocks in logical chains to construct code that typically controls the behavior of another device. Designed for young children, they offer a playful and embodied approach to computer science education. While these systems can effectively teach basic programming concepts, TPL code lacks transferability and expressivity, which limits the types of problems that learners can engage with. Our work explores how designing for the multimodality of TPLs can support greater complexity in programming concepts, a smoother transition to advanced programming settings, and enhanced learner expression. We present touchBase, a TPL that leverages concepts in physical computing and principles of Gestalt psychology to support a culturally-sustaining approach to CS learning design.

We present PyQUBO, an open-source Python library for constructing quadratic unconstrained binary optimizations (QUBOs) from the objective functions and the constraints of optimization problems. PyQUBO enables users to prepare QUBOs or Ising models for various combinatorial optimization problems with ease thanks to the abstraction of expressions and the extensibility of the program. QUBOs and Ising models formulated using PyQUBO are solvable by Ising machines, including quantum annealing machines. We introduce the features of PyQUBO with applications in the number partitioning problem, knapsack problem, graph coloring problem, and integer factorization using a binary multiplier. Moreover, we demonstrate how PyQUBO can be applied to production-scale problems through integration with quantum annealing machines. Through its flexibility and ease of use, PyQUBO has the potential to make quantum annealing a more practical tool among researchers.