Software Between Quantum and Machine Learning -- And Down to Pulses

Quantum computing platforms are typically controlled through unitary gate abstractions, which provide a uniform interface but may limit the exploitation of the underlying hardware's full capabilities. By operating directly at the control-pulse level, developers can tap into a more expressive and fine-grained control paradigm. This approach enables a more intimate understanding of the underlying physics and can potentially unlock new capabilities. The intersection of quantum computing and machine learning is a key area of research, with potential applications in fields such as cryptography and optimization. Researchers are exploring new software frameworks that can bridge the gap between quantum and classical computing, allowing for more efficient and effective utilization of quantum resources¹. This shift in programming paradigm has significant implications for the development of quantum computing applications, and practitioners should be aware of the potential benefits and challenges of operating at the control-pulse level.