Presentation
Invited: Machine Learning for Optimization of Quantum Compilation
DescriptionNoisy Intermediate-Scale Quantum (NISQ) devices face limitations in qubit quantity, operational accuracy, coherence duration, and qubit connectivity within quantum processing units (QPUs). Dynamically remapping logical qubits to physical qubits in the compiler is essential for enabling two-qubit gates in algorithms. This process adds extra operations, reducing the algorithm's fidelity. Therefore, minimizing these additional gates is critical. In this work, we propose an approach to perform feature engineering on quantum circuit representations, creating detailed embeddings. This method facilitates the intricate integration of machine learning techniques. Compared with heuristic search-based algorithms, our approach lowers the overhead of quantum resources needed for adapting a logical circuit to a physical circuit executable.
Event Type
Special Session (Research)
TimeTuesday, June 251:30pm - 2:00pm PDT
Location3006, 3rd Floor
AI