  We present a framework for differentiable quantum transforms. Such transforms
are metaprograms capable of manipulating quantum programs in a way that
preserves their differentiability. We highlight their potential with a set of
relevant examples across quantum computing (gradient computation, circuit
compilation, and error mitigation), and implement them using the transform
framework of PennyLane, a software library for differentiable quantum
programming. In this framework, the transforms themselves are differentiable
and can be parametrized and optimized, which opens up the possibility of
improved quantum resource requirements across a spectrum of tasks.
