Quantum programming languages are a relatively recent innovation which allows arbitrarily accurate modeling of the physical world. However, these languages are largely still in early development: any lack of true abstraction and are simply proxies to circuit languages and are hardly intuitive. There is reason for this, primarily because the desired semantics of a quantum programming language are not yet completely crystallized. This paper focuses on the creation of “lightweight abstractions,” which allow human-level understanding without sacrificing low-level control. Additionally, this project describes a framework which is meant to catalyze the development of quantum programming languages.
This project focuses on a deceptively simple problem in machine learning: The extrapolation and learning of integer sequences by a computer program. (For example, learning and extending the fibonacci numbers). While this problem is simple, explicit modeling and pattern extrapolation are at the heart of many interesting problems (like Raven’s progressive matrices, which is an IQ test item). Pattern-extrapolation doesn’t yet have a satisfactory solution, and it is an open question if traditional machine learning approaches are an efficient approach for this problem. This project compares advanced approaches to the problem, and proposes a more promising algorithm.