This thesis focuses on optical quantum information processing in two parts. The first part focuses on photonic hardware. Large-scale photonic experiments require the generation of many indistinguishable single photons simultaneously. Traditionally, such photons are generated by spontaneous parametric down-conversion (SPDC). However, the suitability of such sources to generate many photons. We have identified the maximum limits of how many photons SPDC sources can be made. A second requirement of large-scale quantum photonics is having a network with many optical modes while maintaining low optical loss. Such networks are currently lacking. Therefore we introduce the possibility to use a rough surface scatterer for a quantum advantage experiment. Although such a network is far from universal, it has many modes and low loss. Another approach is to use integrated quantum photonics, which is fully reconfigurable and is a promising way of scaling up to large systems in an industrial setting. We have characterized a programmable low-loss 12-mode silicon nitride (SiN). In the second part of the thesis, various proof-of-principle quantum experiments were performed on a 12 mode SiN integrated photonic chip. Firstly, it was shown that these networks can be used to simulate wave propagation through disordered networks. This unconventional approach allows for full experimental access and control to the physical process, which is essential for such experiments. Secondly, we introduced a semi-device independent witness protocol that can in-situ verify the indistinguishability of photons on the chip, which is an essential tool to have when the system size increases and can no longer be efficiently simulated classically. The last experiment is a quantum simulation where we show how a pure quantum state locally thermalizes in a closed system.
This thesis focuses on optical quantum information processing in two parts. The first part focuses on photonic hardware. Large-scale photonic experiments require the generation of many indistinguishable single photons simultaneously. Traditionally, such photons are generated by spontaneous parametric down-conversion (SPDC). However, the suitability of such sources to generate many photons. We have identified the maximum limits of how many photons SPDC sources can be made. A second requirement of large-scale quantum photonics is having a network with many optical modes while maintaining low optical loss. Such networks are currently lacking. Therefore we introduce the possibility to use a rough surface scatterer for a quantum advantage experiment. Although such a network is far from universal, it has many modes and low loss. Another approach is to use integrated quantum photonics, which is fully reconfigurable and is a promising way of scaling up to large systems in an industrial setting. We have characterized a programmable low-loss 12-mode silicon nitride (SiN). In the second part of the thesis, various proof-of-principle quantum experiments were performed on a 12 mode SiN integrated photonic chip. Firstly, it was shown that these networks can be used to simulate wave propagation through disordered networks. This unconventional approach allows for full experimental access and control to the physical process, which is essential for such experiments. Secondly, we introduced a semi-device independent witness protocol that can in-situ verify the indistinguishability of photons on the chip, which is an essential tool to have when the system size increases and can no longer be efficiently simulated classically. The last experiment is a quantum simulation where we show how a pure quantum state locally thermalizes in a closed system.
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.
Photonics has consistently played an important role in the investigation of quantum-enhanced technologies and the corresponding study of fundamental quantum phenomena. The majority of these experiments have relied on the free space propagation of light between bulk optical components. This relatively simple and flexible approach often provides the fastest route to small proof-of-principle demonstrations. Unfortunately, such experiments occupy significant space, are not inherently phase stable, and can exhibit significant scattering loss which severely limits their use. Integrated photonics offers a scalable route to building larger quantum states of light by surmounting these barriers. In the first half of this thesis, we describe the operation of on-chip heralded sources of single photons. Loss plays a critical role in determining whether many quantum technologies have any hope of outperforming their classical analogues. Minimizing loss leads us to choose Spontaneous Four-Wave Mixing (SFWM) in a silica waveguide for our source design; silica exhibits extremely low scattering loss and emission can be efficiently coupled to the silica chips and fibers that are widely used in quantum optics experiments. We show there is a straightforward route to maximizing heralded photon purity by minimizing the spectral correlations between emitted photon pairs. Fabrication of identical sources on a large scale is demonstrated by a series of high-visibility interference experiments. This architecture offers a promising route to the construction of nonclassical states of higher photon number by operating many on-chip SFWM sources in parallel. In the second half, we detail one of the first proof-of-principle demonstrations of a new intermediate model of quantum computation called boson sampling. While likely less powerful than a universal quantum computer, boson sampling machines appear significantly easier to build and may allow the first convincing demonstration of a quantum-enhanced computation in the not-distant future. Boson sampling requires a large interferometric network which are challenging to build with bulk optics, we therefore perform our experiment on-chip. We model the effect of loss on our postselected experiment and implement a circuit characterization technique that accounts for this loss. Experimental imperfections, including higher-order emission from our photon pair sources and photon distinguishability, are modeled and found to explain the sampling error observed in our experiment.
Photonics integration is a key technology for realizing large-scale photonic quantum information processing. We demonstrate state-of-art reconfigurable photonic processors based on low-loss silicon nitride waveguide networks. We present the science behind such a processor, which consists of a large mesh of integrated reconfigurable Mach Zehnder interferometers. In this talk, we will present the newest results of the current generation of our programmable quantum photonic processors obtained by classical as well as quantum optical characterization. Furthermore, we show the challenges of scaling up quantum photonic processors and the range of potential applications of large-scale quantum information processing those will enable.
Photonics integration is a key technology for realizing large-scale photonic quantum information processing. We demonstrate state-of-art reconfigurable photonic processors based on low-loss silicon nitride waveguide networks. We present the science behind such a processor, which consists of a large mesh of integrated reconfigurable Mach Zehnder interferometers. In this talk, we will present the newest results of the current generation of our programmable quantum photonic processors obtained by classical as well as quantum optical characterization. Furthermore, we show the challenges of scaling up quantum photonic processors and the range of potential applications of large-scale quantum information processing those will enable.
In recent years, quantum information has rapidly evolved as a field with transformative potential in science and technology. It provides secure encryption for communication, accurate emulation of practical physical systems, streamlined and accelerated machine learning models, and advanced computational powers that outperform classical supercomputers in solving complex problems. These advancements are all reliant on the superposition and entanglement properties of qubits. While various candidates such as trapped ions or superconducting qubits have been proposed to realize quantum technologies, photons have demonstrated a prominent advantage due to their robustness against decoherence and noise, high clock cycle rate, fast and easy measurement, and room temperature working environment. In addition, photonic integrated chip (PIC) technology has developed rapidly since the beginning of the 21st century. The number of devices integrated on a single chip has increased rapidly, and its complexity has also increased. Quantum computing combined with integrated photonics is expected to solve bottleneck problems in practical situations. In this thesis, based on the silicon-on-insulator (SOI) platform, an optical computing chip and a large-scale quantum computing chip with various practical applications are specifically studied. In the first part of the thesis, a novel integrated diffractive optical network is studied, and a scheme is designed to realize parallel Fourier transform, convolution operations, and application-specific optical computing. Due to the utilization of on-chip compact diffractive cells (slab waveguides), the footprint and power consumption of the proposed architecture is reduced from quadratic scaling in the input data dimensions required for Mach-Zehnder interferometer (MZI)-based optical neural network (ONN) architectures to linear scaling for the improved deep neural networks. A 10-fold reduction in both footprint and energy consumption and equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets. This reduction in the resource scaling from quadratic to linear has a profound impact on the realization of large-scale silicon-photonics computing circuits with current fabrication technologies. A quantum photonic microprocessor based on Gaussian Boson sampling (GBS) that offers dynamic programmability to solve various graph-related NP-complete problems is then demonstrated. The system with optical, electrical, and thermal packaging implements a GBS with 16 modes of single-mode squeezed vacuum states, a universal programmable 16-mode interferometer, and a single photon readout on all outputs with high accuracy, generality, and controllability. The developed system is applied to demonstrate applications in solving NP-complete problems, manifesting the ability of photonic quantum computing to realize practical applications for conventionally intractable computations. The GBS-based quantum photonic microprocessor is applied to solve task assignment, Boolean satisfiability, graph clique, max cut, and vertex cover. These demonstrations suggest an excellent benchmarking platform, paving the way toward large-scale combinatorial optimization. Finally, this thesis explores the application possibility of large-scale PICs in molecular simulation. A new algorithm utilizing states with zero displacements (vacuum-squeezed states) coupling to a linear network is proposed to calculate molecular vibronic spectra. With a large-scale PIC, molecular vibronic spectra are accurately simulated for formic acid and thymine with high reconstructed fidelity (>91%) and for naphthalene, phenanthrene, and benzene under the non-Condon regime. Our experimental demonstration will open pathways toward conducting large-scale molecular quantum simulations, a task surpassing classical computers’ capabilities.
Programmable waveguide circuits are crucial building blocks for integrated spectrometric applications and quantum photonic information processing. Amongst the dielectric material platforms in integrated photonics, silicon nitride stands out with highly attractive properties such as a large bandgap energy and a moderately high index contrast. This allows low propagation losses in a wide spectral range while simultaneously allowing a dense packing of components. The aforementioned properties, together with the inherent phase stability and phase programmability achievable in silicon nitride, enable the creation of complex photonic circuits. In this thesis we describe and demonstrate densely integrated programmable photonic circuits based on silicon nitride waveguides for wavelength metrology and quantum information processing. We concentrate on reconfigurable photonic integrated circuits based on silicon nitride waveguides with low-loss propagation, to explore interference in the spectral and temporal domain for advanced applications. We investigated two types of integrated interferometric devices featuring low loss in combination with programmability for classical and quantum photonic processing. The first is simple tunable microring resonator circuits in combination with neural network data processing for the analysis of classical light in the spectral domain as wavelength meter. The second is a complex tunable network of waveguide interferometers for controlling quantum correlations (coincidences) between single photons. Exploiting the long-term interferometric stability, low propagation loss and tight optical confinement of integrated silicon nitride waveguides, we have shown complex reconfigurable optical circuits both for classical and quantum photonic processing. For future development of integrated programmable photonic processors, various challenges need to be addressed such as compact and low-power phase shifters, a further increase of the component density and lower the propagation losses.