Processing quantum information poses novel challenges regarding the debugging of faulty quantum programs. Notably, the lack of accessible information on intermediate states during quantum processing, renders traditional debugging techniques infeasible. Moreover, even correct quantum programs might not be processable, as current quantum computers are limited in computation capacity. Thus, quantum program developers have to consider trade-offs between accuracy (i.e., probabilistically correct functionality) and computational cost of the proposed solutions. Manually finding sufficiently accurate and efficient solutions is a challenging task, even for quantum computing experts. To tackle these challenges, we propose a quantum program improvement framework for an automated generation of accurate and efficient solutions, coined Genetic Quantum Program Improver ( GeQuPI ). In particular, we focus on the tasks of debugging and optimization of quantum programs. Our framework uses techniques from quantum information theory and applies multi-objective genetic programming, which can be further hybridized with quantum-aware optimizers. To demonstrate the benefits of GeQuPI , it is applied to 47 quantum programs reused from literature and openly published libraries. The results show that our approach is capable of correcting faulty programs and optimize inefficient ones for the majority of the studied cases, showing average optimizations of 35% with respect to computational cost. • A framework for genetic improvement of quantum programs is proposed. • The framework provides debugging and optimization facilities. • It applies methods from quantum information theory and multi-objective optimization. • Its possible configurations include, a.o., varying levels of hardware closeness. • The evaluation on 47 quantum programs shows promising results.

Quantum programming languages support the design of quantum applications. However, to create such programs, one needs to understand the fundamental characteristics of quantum computing and quantum information theory. Furthermore, quantum algorithms frequently make use of abstract operations with a hidden low-level realization (e.g., Quantum Fourier Transform). Thus, turning from elementary quantum operations to a higher-level view of quantum circuit design not only reduces the development effort but also lowers the entry barriers for non-quantum computing experts. To this end, this article proposes a modeling language and design framework for quantum circuits. This allows the definition of composite operators to advocate a higher-level quantum algorithm design, together with automated code generation for the circuit execution. To demonstrate the benefits of the proposed approach, coined Composition-based Quantum Circuit Designer , we applied it for realizing the Quantum Counting algorithm and the Quantum Approximate Optimization Algorithm. Our evaluation results show that, compared to an existing state-of-the-art editor, the proposed approach allows for the realization of both quantum algorithms on a high level with a substantially reduced development effort. In particular, the proposed approach shows constant scaling when increasing the size of the investigated quantum circuits and a lower change criticality when evolving existing quantum circuits.