Objective 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 today’s 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 Fields of science engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehiclesnatural sciencescomputer and information sciencesartificial intelligencemachine learningnatural sciencesphysical sciencesopticsnonlinear opticsengineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksoptical networksfiber-optic networknatural sciencesphysical sciencestheoretical physicsparticle physicsphotons Keywords integrated quantum optics on-chip frequency combs machine learning photonic quantum signal processing Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2020-STG - ERC STARTING GRANTS Call for proposal ERC-2020-STG See other projects for this call Funding Scheme ERC-STG - Starting Grant Coordinator GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER Net EU contribution € 1 497 658,00 Address Welfengarten 1 30167 Hannover Germany See on map Region Niedersachsen Hannover Region Hannover Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER Germany Net EU contribution € 1 497 658,00 Address Welfengarten 1 30167 Hannover See on map Region Niedersachsen Hannover Region Hannover Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
