Photonic platforms represent a promising technology for the realization of several quantum communication protocols and for experiments of quantum simulation. Moreover, large-scale integrated interferometers have recently gained a relevant role in quantum computing, specifically with Boson Sampling devices and the race for quantum supremacy. Indeed, various linear optical schemes have been proposed for the implementation of unitary transformations, each one suitable for a specific task. Notwithstanding, so far a comprehensive analysis of the state of the art under broader and realistic conditions is still lacking. In the present work we fill this gap, providing in a unified framework a quantitative comparison of the three main photonic architectures, namely the ones with triangular and square designs and the so-called fast transformations. All layouts have been analyzed in presence of losses and imperfect control over the internal reflectivities and phases, showing that the square design outperforms the triangular scheme in most operational conditions. Our results represent a further step ahead towards the implementation of quantum information protocols on large-scale integrated photonic devices.
Photonic platforms represent a promising technology for the realization of several quantum communication protocols and for experiments of quantum simulation. Moreover, large-scale integrated interferometers have recently gained a relevant role in quantum computing, specifically with Boson Sampling devices and the race for quantum supremacy. Indeed, various linear optical schemes have been proposed for the implementation of unitary transformations, each one suitable for a specific task. Notwithstanding, so far a comprehensive analysis of the state of the art under broader and realistic conditions is still lacking. In the present work we fill this gap, providing in a unified framework a quantitative comparison of the three main photonic architectures, namely the ones with triangular and square designs and the so-called fast transformations. All layouts have been analyzed in presence of losses and imperfect control over the internal reflectivities and phases, showing that the square design outperforms the triangular scheme in most operational conditions. Our results represent a further step ahead towards the implementation of quantum information protocols on large-scale integrated photonic devices.
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.
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.
Multimode optical interferometers represent the most viable platforms for the successful implementation of several quantum information schemes that take advantage of optical processing. Examples range from quantum communication and sensing, to computation, including optical neural networks, optical reservoir computing, or simulation of complex physical systems. The realization of such routines requires high levels of control and tunability of the parameters that define the operations carried out by the device. This requirement becomes particularly crucial in light of recent technological improvements in integrated photonic technologies, which enable the implementation of progressively larger circuits embedding a considerable amount of tunable parameters. We formulate efficient procedures for the characterization of optical circuits in the presence of imperfections that typically occur in physical experiments, such as unbalanced losses and phase instabilities in the input and output collection stages. The algorithm aims at reconstructing the transfer matrix that represents the optical interferometer without making any strong assumptions about its internal structure and encoding. We show the viability of this approach in an experimentally relevant scenario, defined by a tunable integrated photonic circuit, and we demonstrate the effectiveness and robustness of our method. Our findings can find application in a wide range of optical setups, based on both bulk and integrated configurations.
Boson Sampling is a computational paradigm representing one of the most viable and pursued approaches to demonstrate the regime of quantum advantage. Recent results have demonstrated significant technological leaps in single-photon generation and detection, leading to progressively larger experimental instances of Boson Sampling experiments in different photonic systems. However, a crucial requirement for a fully-fledged platform solving this problem is the capability of implementing large scale interferometers, that must simultaneously exhibit low losses, high degree of reconfigurability and the realization of arbitrary transformations. In this work, we move a step forward in this direction by demonstrating the adoption of a novel compact and reconfigurable 3D-integrated platform for photonic Boson Sampling. We perform 3- and 4-photon experiments by using such platform, showing the possibility of programming the circuit to implement a large number of unitary transformations. These results show that such compact and highly-reconfigurable layout can be scaled up to experiments with larger number of photon and modes, and can provide a viable direction for hybrid computing with photonic processors.
Recent progress in photonic information processing has spurred strong demand in scalable and reconfigurable photonic circuitry. Conventional spatially meshed multiport interferometers require a number of components growing quadratically with the system size, posing a fundamental scaling challenge ahead. Here, we introduce a hardware-efficient synthetic time-domain photonic processor that achieves at least an exponential reduction in hardware component count for implementing arbitrary linear transformations. The processor’s dynamic connectivity allows systematic pruning, minimizing optical loss while preserving all-to-all connectivity. We benchmark our architecture on the task of boosted Bell-state measurements—a protocol essential for linear optical quantum computation, and show that it exceeds thresholds for universal cluster-state quantum computation under realistic hardware constraints. We link the device performance to the geometry of multiphoton transport, showing that localization effects from redundant, imperfect hardware may enhance robustness to coherent errors. Our design establishes a practical pathway toward near-term, scalable, and reconfigurable photonic processors in the synthetic time dimension.
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.