Machine learning empowers computers to solve complex tasks such as pattern identification and strategy optimization with applications in, e.g. financial trading, fraud detection, medical diagnosis, and self-driving vehicles. The required computing power is, however, pushing existing computational resources to their limits (hardware and power requirements), restraining their further advancement. QFreC targets the realization of photonic frequency-based quantum co-processors, specifically tailor-made to solve machine learning problems with capabilities commensurate with today’s high-power, yet energy-efficient processing needs. In particular, QFreC explores a high-dimensional photonic quantum frequency comb approach, where photons have hundreds to thousands of discrete and equidistantly spaced frequency modes, giving access to large, scalable information capacity. QFreC thus strategically combines i) the investigation of photonic frequency domain processing by the adaptation of existing and exploration of new qubit learning concepts, ii) the realization of efficiency-enhanced and novel integrated quantum frequency comb sources and iii) the development of reconfigurable, fast, and broadband control schemes via electro-optical and all-optical nonlinear processes.