Transpilation, the process of converting quantum circuits into a form suitable for specific quantum hardware, presents a growing bottleneck as quantum computers scale up in complexity. Aleksander Kaczmarek from SoftServe Inc and Dikshant Dulal from ISAAQ Pte Ltd, Haiqu, and their colleagues address this challenge by introducing a method to accelerate transpilation times dramatically. Their work focuses on reusing previously transpiled circuits, a technique that avoids redundant calculations and significantly reduces computational cost, particularly when dealing with iterative processes such as layerwise learning in quantum machine learning. The team demonstrates that this approach, implemented within the Rivet transpiler, achieves up to a sixfold improvement in speed compared to conventional transpilation methods, paving the way for more efficient and scalable quantum algorithms.
Rivet Transpiler Accelerates Quantum Machine Learning
Quantum machine learning demands increasingly complex circuits, but preparing these circuits for execution on real quantum hardware presents a significant challenge. The process, known as transpilation, converts abstract quantum algorithms into a series of operations specific to the hardware, often requiring substantial computational resources. This research introduces the Rivet transpiler, a new approach designed to accelerate this process and improve the efficiency of quantum machine learning applications. By reusing previously compiled circuit segments, Rivet significantly reduces computational demands and accelerates the overall process. This innovation is particularly valuable for algorithms like layerwise learning, where circuits are built incrementally, allowing for efficient transpilation of added layers.
Experiments demonstrate significant reductions in transpilation time, with layerwise learning algorithms experiencing up to a 600% improvement. The research team conducted extensive experiments with various data encoding strategies, demonstrating consistent reductions in transpilation time across all methods. These strategies, including angle encoding, ZZFeatureMap, and amplitude encoding, each offer unique trade-offs between circuit complexity and expressibility. Rivet adapts to these differences, consistently delivering performance gains, and the ZZFeatureMap, which captures feature dependencies through entangling gates, benefited particularly from Rivet’s optimizations. The results demonstrate that Rivet not only reduces transpilation time but also maintains comparable accuracy and loss to traditional training methods, making it a valuable tool for accelerating quantum machine learning research and development.