Extending XACC for Quantum Optimal Control

TL;DR

This paper extends the XACC framework to incorporate quantum optimal control techniques, enabling the translation of digital quantum circuits into pulse sequences optimized for specific hardware dynamics, thus advancing quantum compilation.

Contribution

It introduces a modular extension to XACC that supports user-defined quantum optimal control methods for pulse-level compilation in both C++ and Python.

Findings

Demonstrates integration of GRAPE, GOAT, and Krotov's methods.

Enables direct compilation from quantum circuits to optimized pulse sequences.

Provides a foundation for future quantum-classical compiler designs.

Abstract

Quantum computing vendors are beginning to open up application programming interfaces for direct pulse-level quantum control. With this, programmers can begin to describe quantum kernels of execution via sequences of arbitrary pulse shapes. This opens new avenues of research and development with regards to smart quantum compilation routines that enable direct translation of higher-level digital assembly representations to these native pulse instructions. In this work, we present an extension to the XACC system-level quantum-classical software framework that directly enables this compilation lowering phase via user-specified quantum optimal control techniques. This extension enables the translation of digital quantum circuit representations to equivalent pulse sequences that are optimal with respect to the backend system dynamics. Our work is modular and extensible, enabling third party optimal control techniques and strategies in both C++ and Python. We demonstrate this extension with familiar gradient-based methods like gradient ascent pulse engineering (GRAPE), gradient optimization of analytic controls (GOAT), and Krotov's method. Our work serves as a foundational component of future quantum-classical compiler designs that lower high-level programmatic representations to low-level machine instructions.