Reasoning about faulty quantum programs

Nengkun Yu

Juliana Kaizer Vizzotto, Giovani Rubert Librelotto, Amr Sabry

Mingsheng Ying

Felix Gemeinhardt, Stefan Klikovits, Manuel Wimmer

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.

Christine Klymko, Blair D. Sullivan, Travis S. Humble

Adiabatic quantum programming defines the time-dependent mapping of a quantum algorithm into an underlying hardware or logical fabric. An essential step is embedding problem-specific information into the quantum logical fabric. We present algorithms for embedding arbitrary instances of the adiabatic quantum optimization algorithm into a square lattice of specialized unit cells. These methods extend with fabric growth while scaling linearly in time and quadratically in footprint. We also provide methods for handling hard faults in the logical fabric without invoking approximations to the original problem, and illustrate their versatility through numerical studies of embeddabilty versus fault rates in square lattices of complete bipartite unit cells. The studies show these algorithms are more resilient to faulty fabrics than naive embedding approaches, a feature which should prove useful in benchmarking the adiabatic quantum optimization algorithm on existing faulty hardware.

Mingsheng Ying