Most contemporary quantum programming languages describe computation as circuits,
using a host classical counterpart to drive the execution of quantum programs. However, the …

Most implementations of machine learning algorithms are based on special-purpose
frameworks such as TensorFlow or PyTorch. While these frameworks are convenient to use …

Multi-dimensional array manipulation constitutes a core component of numerous numerical
methods, eg finite difference solvers of Partial Differential Equations (PDEs). The efficiency …

The Evolution of APL, the HOPL I paper by Falkoff and Iverson on APL, recounted the
fundamental design principles which shaped the implementation of the APL language in …

The APL notation would appear to be a clear match for convolutional neural networks, but
traditional implementations of APL have lagged behind the performance of highly tuned …

With society becoming more and more reliant on software, the importance of software quality
is becoming ever more clear. Poor quality software is estimated to have cost $2.41 trillion to …

Dependently-typed host languages empower users to verify a wide range of properties of
embedded languages and programs written in them. Designers of such embedded …

We present a design pattern for composing deep learning networks in a typed, higher-order
fashion. The exposed library functions are generically typed and the composition structure …

Logic programming allows a high level of abstraction in both code and problem solving
concepts. Language representatives of this paradigm of which Prolog is the most well …