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Photonics North (PN)

Photonic Quantum Processor for Novel Applications

Monika Monika, Carlos Sevilla-Gutiérrez, Cristina Josefina Amaya Mendez, Dmitriy Mitin, Idoia Ugarte Olcoz, Luis Javier González-Martín del Campo, M. Leyendecker, Michael Reibe, Oskar Kohout, Reinhard Geiß, Thomas Müller-Wirts, Umair Ashraf Mir, Varun Raj Kaipalath, Volodymyr Zubariev, Frank Setzpfandt, Fabian Steinlechner

Published May 20, 2025• 0 citations
Publisher

Abstract

We present a quantum photonic processor featuring 32 modes, with the potential for further scalability. The system demonstrates robustness against extrinsic phase noise, accounting for temperature and acoustic fluctuations. Our initial objective is to tailor this processor for quantum boson sampling (QBS) as a standard benchmark. Upon successful validation, the platform will be ready to provide cutting-edge solutions for strategic industrial problems.

DOI
10.1109/pn66844.2025.11097157

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Recently, quantum advantage over classical computation has been claimed using photons [1] . To control photonic quantum computations such as Boson sampling, a non-universal approach on quantum computation, large-scale quantum processors are needed [2] , [3] . To realize scalable and robust photonic quantum processors integrated photonics is a key technology. In our approach we use the integrated waveguide platform based on silicon nitride [4] , due to its low intrinsic losses and technological maturity. The low losses result in a high fidelity of the quantum photonics processor which is mandatory for reliable quantum computation while maturity of the platform allows for scaling up of the dimensions of the processor. Here, we present a universal 12-mode quantum photonics processor which is the largest of its kind to date.
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This dissertation explores the use of single photons in integrated photonic circuits as a platform for quantum computation and quantum simulation. In the noisy intermediate-scale quantum (NISQ) era, photonic implementations of boson sampling provide one of the most promising routes to demonstrating quantum advantage, while also enabling insight-driven experiments that probe many-body interference and computational complexity. The first part of the thesis is devoted to the hardware development and characterization of programmable silicon nitride photonic processors. We demonstrate large-scale multi-photon interference in a 20-mode universal processor, establishing the potential of integrated photonics for scaling up boson sampling experiments. In parallel, we introduce a validation protocol based on detector binning, implemented on a 12-mode predecessor of the device. Together, these studies advance the photonic platform itself, focusing on scalability, versatility, and benchmarking, rather than direct applications. The second part turns to quantum simulation. We first present a photonic simulation of a spin-foam amplitude from loop quantum gravity, illustrating how multi-photon interference can provide a testbed for problems in fundamental physics. We then implement a particle-number-difference Maxwell demon using conditional operations on photon subsets in a multi-mode interferometer. This experiment probes the link between thermodynamics and information theory, showing how a photonic platform with single-photon resolution and quantum interference can be used to explore information-driven control of physical systems. The final part addresses quantum computation, where we demonstrate a boson-sampling-accelerated Monte Carlo integrator. This hybrid quantum–classical algorithm represents one of the few proposals to extend boson sampling toward practical applications while plausibly retaining its computational hardness. We validate the method in a proof-of-principle experiment reproducing perturbative corrections in a model inspired by Efimov physics. Altogether, the results presented in this thesis position integrated photonics as both a scalable hardware platform for multi-photon experiments and a versatile tool for advancing quantum simulation and computation.