Smart protonic quantum frequency circuits

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, restraining their further advancement.

In QFreC, I target the realization of photonic frequency-based quantum co-processors, specifically tailor-made to solve machine learning problems with capabilities commensurate with todays high-power, yet energy-efficient processing needs.

In particular, I will use 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.

For implementing quantum-accelerated machine learning tasks such as the classification of classical or quantum data, I will follow i) the exploration of quantum photonic frequency-domain processing with the adaptation of qubit learning concepts (vector-based and neural network-based approaches) to high-dimensional quantum representations, i.e., quDits, ii) the realization of efficiency-enhanced and novel integrated quantum frequency comb systems with quantum resources that allow real-world applications using highly nonlinear on-chip platforms, and iii) the development of reconfigurable, fast, and broadband experimental control schemes using, e.g., quadrature amplitude modulation formats and nonlinear optical processes.

To enable stable, compact, cost- and energy-efficient quantum processing devices, the QFreC project will build on the advances of the well-developed telecommunications infrastructure and the photonic chip fabrication industry.

QFreC merges photonic quantum frequency-domain circuits with quantum machine learning, enabling large-scale controllable quantum resources for the exploration of quantum-enhanced machine learning