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.

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.

- A. Khodadad Kashi, L. Caspani, and M. Kues, “Spectral Hong-Ou-Mandel Effect between a Heralded Single-Photon State and a Thermal Field: Multiphoton Contamination and the Nonclassicality Threshold,” Phys. Rev. Lett. 131, 233601 (2023) doi.org/10.1103/PhysRevLett.131.233601
- A. M. Angulo, J. Heine, J. S. D. Gomez, H. Mahmudlu, R. Haldar, C. Klitis, M. Sorel, M. Kues, „Shaping the spectral correlation of bi-photon quantum frequency combs by multi-frequency excitation of an SOI integrated nonlinear resonator,” Optics Letters 48, 5583 (2023). doi.org/10.1364/OL.503909
- L. Sader, S. Bose, A. Khodadad Kashi, Y. Boussafa, R. Haldar, R. Dauliat, P. Roy, M. Fabert, A. Tonello, V. Couderc, M. Kues, B. Wetzel, “Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics,” ACS Photonics 10, 3915-3928 (2023). doi.org/10.1021/acsphotonics.3c00711
- H. Mahmudlu, R. Johanning, A. Khodadad Kashi, A. van Rees, J. P. Epping, R. Haldar, K.-J. Boller, M. Kues “Fully on-chip photonic turnkey quantum source for entangled qubit/qudit state generation”, Nature Photonics 17, 518 (2023). doi.org/10.1038/s41566-023-01193-1
- T. Godin, L. Sader, A. Khodadad Kashi, P. Hanzard, A. Hideur, D. J Moss, R. Morandotti, G. Genty, J. M. Dudley, A. Pasquazi, M. Kues, B. Wetzel, “Recent advances on time-stretch dispersive Fourier transform and its applications,” Advances in Physics: X 7, 2067487 (2022).
- A. Khodadad Kashi, L. Sader, R. Haldar, B. Wetzel, M. Kues, “Frequency-to-time mapping technique for direct spectral characterization of biphoton states from pulsed spontaneous parametric processes,” Frontiers in Photonics 3, 834065 (2022).