Is Productivity in Quantum Programming Equivalent to Expressiveness?

Abstract

The expressiveness of quantum programming languages plays a crucial role in the efficient and comprehensible representation of quantum algorithms. Unlike classical programming languages, which offer mature and well-defined abstraction mechanisms, quantum languages must integrate cognitively challenging concepts such as superposition, interference and entanglement while maintaining clarity and usability.

However, identifying and characterizing differences in expressiveness between quantum programming paradigms remains an open area of study. Our work investigates the landscape of expressiveness through a comparative analysis of hosted quantum programming languages such as Qiskit, Cirq, Qrisp, and quAPL, and standalone languages including Q# and Qmod.

We focused on evaluating how different quantum programming languages support the implementation of core quantum algorithms—Deutsch-Jozsa, Simon, Bernstein-Vazirani, and Grover—using expressiveness metrics: Lines of Code (LOC), Cyclomatic Complexity (CC), and Halstead Complexity (HC) metrics as proxies for developer productivity.

Our findings suggest that different quantum programming paradigms offer distinct trade-offs between expressiveness and productivity, highlighting the importance of language design in quantum software development.