Photonic Quantum Computing

Photonics is one of the leading platforms for quantum computing, combining high-fidelity manipulation of quantum states with the potential for large-scale integration. Advances in integrated photonics enable compact, stable and scalable quantum processors, while new single-photon sources and detectors are paving the way towards the demanding requirements of fault-tolerant quantum computing.

Our group investigates both the hardware and the computational building blocks required for scalable photonic quantum technologies. We work with state-of-the-art single-photon sources, time-bin architectures and programmable photonic circuits, which we use to implement adaptive quantum protocols, including quantum machine learning, variational algorithms and quantum operations.

A central question underlying our research is whether and where quantum resources can offer benefits for information processing. We therefore explore how phenomena such as interference and entanglement can be harnessed to enhance computational capabilities and tackle tasks that may be challenging for classical approaches.

In parallel, we investigate the generation and characterization of large multipartite entangled states, such as GHZ and cluster states, which are key resources for measurement-based quantum computing.

Together, these efforts aim to bridge the gap between quantum photonic hardware and useful quantum computation, while identifying where quantum effects can translate into computational benefits.

Artificial intelligence is transforming how we process information, yet ever-larger datasets and models demand faster and more energy-efficient computing hardware. Our research addresses this challenge at the intersection of quantum computing, machine learning, and photonics.

We develop hybrid quantum-classical approaches for near-term photonic hardware, investigating whether quantum resources such as interference, superposition, and entanglement can unlock new computational capabilities. A central challenge is implementing nonlinear and memory-dependent dynamics, which are key to the versatility of AI systems but do not arise naturally in quantum evolution. One example is the quantum memristor, a photonic device with memory-dependent behaviour that paves the way towards neuron-like dynamics and neuromorphic quantum computing.

These ideas extend beyond the laboratory: in 2025, we launched a programmable photonic quantum processor into space to explore resource-efficient quantum information processing in extreme environments

We are investigating the fundamental light-matter interaction of solid-state quantum emitters and in particular the dynamics of the excitation and emission processes. In recent experiments, we probe semiconductor quantum dots with light pulses of varying photon statistics and investigate the nonlinear response. Furthermore, we study how the quantum coherence of the excitation field can be transferred to the few-level system and is preserved in the photonic output state upon recombination.Results of our research impacts both the field of photonic quantum computing and quantum cryptography. Already today, quantum dots serve as source of highly pure and indistinguishable single photons, entangled photon pairs or photonic cluster states generated by spin-photon coupling. This makes them a crucial resource for our linear-optical quantum computing schemes based on time-loops, as well as for the fusion-based quantum computing scheme. Notably, we have demonstrated a key building block of this approach: the generation of a three-photon heralded GHZ state. Moreover, quantum dots can act as a highly nonlinear medium (and thus an effective mediator of photon-photon interaction), a key functionality to implement deterministic gates in photonic quantum computing. Besides, quantum dots are excellent sources for communication tasks. For instance, our recent work included both experimental and theoretical investigations into optimizing pumping parameters to achieve optimal performance in various cryptographic applications, ranging from quantum key distribution to quantum bit commitment and quantum strong coin flipping. Above all, we enjoy exploring the rich physics and deepen our understanding of this unique platform sitting at the interface of quantum information and cavity quantum electrodynamics.

Experimental Neuromorphic Computing Based on Quantum Memristor
M. Selimovic et al, PRX Quantum 7, 033067 (2026).  

Experimental data reuploading with provable enhanced learning capabilities
M. X. Mauser et al., Science Advances 12 (2026).

Experimental quantum-enhanced kernel-based machine learning on a photonic processor
Z. Yin et al., Nature Photonics 19, 1020-1027 (2025).

Photonic Sources of Heralded Greenberger-Horne-Zeilinger States
H. Cao et al., Physical Review Letter 132 (2024).

Experimental photonic quantum memristor
M. Spagnolo et al., Nature Photonics 16, 318–323 (2022).

Experimental quantum speed-up in reinforcement learning agents
V. Saggio et al.,Nature 591, 229–233 (2021).

Experimental Boson Sampling
M. Tillmann, B. Dakic, R. Heilmann, S. Nolte, A. Szameit, P. Walther,
Nature Photonics 7, 540-544 (2013).

Experimental verification of quantum computation
S. Barz, J.F. Fitzsimons, E. Kashefi, P. Walther, Nature Physics 9, 727-731 (2013).