Accelerating Transpilation in Quantum Machine Learning with Haiqu’s Rivet-transpiler
Publications
Quantum programs need to be translated into hardware-compatible instructions before they can run, a process known as transpilation. In applications such as quantum machine learning, circuits may change repeatedly during training, normally requiring them to be transpiled again after each modification; Haiqu’s Rivet transpiler avoids much of this repeated work by reusing already-translated parts of the circuit. In our experiments, this reduced transpilation time by up to 6x, enabling faster iteration and making larger, more complex quantum models more practical to train.
Date: 29/08/2025
Journal/conference link and reference: N/A
