Many applications of quantum optics demand delicate quantum properties of light carefully tailored to accomplish a specific task. To this end, numerical simulations of quantum light sources are vital for designing, characterizing, and optimizing quantum photonic technology. Here, I show that exploiting information hidden in zero-photon measurement outcomes provides an exponential speedup for time-integrated photon-counting simulations, realizing eight orders of magnitude reduction in the time to compute six-photon detection probabilities while achieving 10 orders of magnitude higher precision compared to the state of the art. This enables simulations of large photonic experiments with an unprecedented level of physical detail. It can accelerate the design of sources to generate photonic resource states for quantum sensing and measurement-based quantum computing while capturing realistic imperfections. It also establishes a general theoretical framework to study dynamic interactions between stationary qubits mediated by measurements of flying qubits, which can be used to model distributed quantum computing and quantum communication.
Many applications of quantum optics demand delicate quantum properties of light carefully tailored to accomplish a specific task. To this end, numerical simulations of quantum light sources are vital for designing, characterizing, and optimizing quantum photonic technology. Here, I show that exploiting information hidden in zero-photon measurement outcomes provides an exponential speedup for time-integrated photon-counting simulations, realizing eight orders of magnitude reduction in the time to compute six-photon detection probabilities while achieving 10 orders of magnitude higher precision compared to the state of the art. This enables simulations of large photonic experiments with an unprecedented level of physical detail. It can accelerate the design of sources to generate photonic resource states for quantum sensing and measurement-based quantum computing while capturing realistic imperfections. It also establishes a general theoretical framework to study dynamic interactions between stationary qubits mediated by measurements of flying qubits, which can be used to model distributed quantum computing and quantum communication.
Quantum mechanics endows light with properties that have no classical analogue - superposition, entanglement, and non-classical photon statistics, that can be harnessed as resources for computing, communication, sensing, and simulation. After decades of foundational research, quantum states of light can now be generated, manipulated, and measured with sufficient precision that their non-classical properties are no longer merely curiosities, but engineerable resources. Integrated photonics offers a compelling path forward. By confining light in lithographically defined waveguides on a chip, it replaces fragile bulk optical assemblies with compact, phase-stable, and reproducible circuits. Semiconductor quantum dots embedded in photonic nanostructures provide bright, on-demand single-photon emission compatible with waveguide integration, while superconducting nanowire single-photon detectors provide the efficiency and timing precision that quantum optical measurements demand. A particularly powerful frontier is reconfigurability: programmable photonic circuits whose functionality is defined in software can serve as versatile quantum photonic processors without any physical modification. This dissertation presents work on engineering quantum light across the full chain of generation, control, and detection using integrated photonic platforms. It covers coherent control of nanowire quantum dot emitters, generation of multipartite entangled states on a silicon nitride chip, and the use of programmable silicon-on-insulator interferometers as reconfigurable quantum optical processors for simulating quantum systems.
This thesis presents the development of resource-efficient photon-distillation schemes designed to produce highly indistinguishable photons, a key requirement for large-scale photonic quantum computing. Although photons do not naturally interact, their indistinguishability enables effective interactions through quantum interference, allowing entanglement generation using only linear optical elements and measurements. In practice, however, photons are never perfectly identical. Small differences introduce decoherence, limiting the scalability and reliability of photonic quantum technologies. Addressing indistinguishability errors is therefore essential for fault-tolerant quantum computing. The thesis first reviews the importance of photon indistinguishability and the need to mitigate related errors. It then examines experimental methods for characterizing imperfections and introduces a general model of imperfect photon states. This model captures indistinguishability errors alongside additional experimental noise from photon loss and multiphoton events. A key insight is that loss and multiphoton noise together can act as an effective source of indistinguishability error. To quantify this effect, the work defines an effective indistinguishability metric, enabling practical evaluation of photon-distillation schemes under realistic conditions. The core theoretical contribution is the development of photon-distillation protocols based on multiphoton Fourier interference. Unlike earlier approaches, these schemes can asymptotically suppress indistinguishability errors to arbitrary levels while requiring only linear scaling in photon resources relative to the desired error reduction. The analysis further suggests that photon distillation, when combined with quantum error correction, can significantly reduce the overall resource overhead for fault-tolerant photonic quantum computing. Experimentally, a three-photon Fourier-based distillation protocol was demonstrated on a high-fidelity photonic processor using a resonantly driven quantum-dot single-photon source. After subtracting excess noise contributions, a 2.2-fold reduction in indistinguishability error was achieved; when excess noise was retained, a 1.2-fold net reduction was observed. These results confirm a clear below-threshold improvement in photon quality. Additional analysis suggests that optimized distillation schemes could reduce the photon cost of a logical qubit by up to a factor of four. The thesis concludes by addressing insertion loss and outlining design strategies and future directions for integrating photon-distillation components into scalable quantum architectures.
We develop an analytic perturbative framework that enables the analysis of small Markovian errors in probabilistic, photon-heralded quantum operations between non-interacting emitters. Building on and extending the Zero-Photon-Generation (ZPG) framework, we derive closed-form perturbative solutions that capture both ideal (zero-order) and noisy (low-order) gate dynamics conditioned on time-integrated photon counting. Our framework provides analytic solutions to process matrices and Pauli error weights up to leading order, bridging the gap between detailed physical imperfections of a system and its corresponding abstract Pauli noise models. Moreover, our framework captures imperfections across the full physical system stack, from photon generation to optical manipulation. We benchmark the resulting perturbative predictions on a repeat-until-success CZ gate against numerical simulations, demonstrating accurate modeling of source-induced noise, and then apply the same framework to analyze coherent phase-shifter miscalibrations as a representative example of optical-manipulation errors. The methods developed in this work enable physics-informed parameter tuning to optimize gate designs and develop tailored quantum error correction protocols toward fault-tolerant quantum computing using hybrid light--matter quantum systems.
This work models metropolitan-scale photonic quantum networks that use time bin encoding for quantum key distribution and quantum state teleportation. We develop and validate theoretical models by comparing them with prior experimental results. We use our newly developed simulator of quantum network communication, called SeQUeNCe, to perform simulations at the individual photon level with picosecond resolution. The simulator integrates accurate models of optical components including light sources, interferometers, detectors, beam splitters, and telecommunication fiber, allowing studies of their complex interactions. Optical quantum networks have been generating significant interest because of their ability to provide secure communication, enable new functionality such as clock synchronization with unprecedented accuracy, and reduce the communication complexity of certain distributed computing problems. In the past few years experimental demonstrations moved from table-top experiments to metropolitan-scale deployments and long-distance repeater network prototypes. As the number of optical components in these experiments increases, simulation tools such as SeQUeNCe will simplify experiment planning and accelerate designs of new network protocols. The modular design of our tool will also allow modeling future technologies such as network nodes with quantum memories and quantum transducers as they become available.
The integration of quantum photonic architectures into industrial computation represents a transformative approach to addressing the growing complexity, precision demands, and energy inefficiencies of modern manufacturing systems. This research explores the development and application of integrated quantum photonic platforms for high-throughput simulation and optimization, enabling large-scale, parallelized modeling of manufacturing processes. Unlike traditional electronic-based computation, photonic architectures exploit the quantum properties of light—such as superposition, entanglement, and interference—to achieve massive computational concurrency with low latency and high energy efficiency. The study focuses on applying quantum photonics to enhance industrial process optimization, including workflow scheduling, supply chain coordination, defect detection, and material design optimization. Through simulations and conceptual modeling, the research demonstrates how photon-based qubits and integrated optical circuits can execute optimization algorithms more efficiently than classical architectures when managing large, interdependent datasets. The findings underscore the potential of quantum-enhanced photonic computing to achieve scalable, real-time decision-making in sectors such as semiconductor fabrication, automotive manufacturing, and energy systems engineering. Conducted in collaboration with academic scholars, the project provided hands-on exposure to algorithmic modeling, quantum heuristic design, and analytical evaluation, deepening the researcher’s understanding of both theoretical and applied quantum systems. Additionally, the experience cultivated transferable skills in critical thinking, interdisciplinary collaboration, and scientific communication—core competencies essential for the next generation of engineers and scientists navigating the quantum industrial era. This study ultimately highlights quantum photonics as a foundational enabler of intelligent, high-performance, and sustainable manufacturing ecosystems.
Quantum information processing provides remarkable advantages over its classical counterpart. Quantum optical systems have been proved to be sufficient for realizing general quantum tasks, which, however, often rely on single-photon sources. In practice, imperfect single-photon sources, such as a weak-coherent-state source, are used instead, which will inevitably limit the power in demonstrating quantum effects. For instance, with imperfect photon sources, the key rate of the Bennett-Brassard 1984 (BB84) quantum key distribution protocol will be very low, which fortunately can be resolved by utilizing the decoy-state method. As a generalization, we investigate an efficient way to simulate single photons with imperfect ones to an arbitrary desired accuracy when the number of photonic inputs is small. Based on this simulator, we can thus replace the tasks that involve only a few single-photon inputs with the ones that make use of only imperfect photon sources. In addition, our method also provides a quantum simulator to quantum computation based on quantum optics. In the main context, we take a phase-randomized coherent state as an example for analysis. A general photon source applies similarly and may provide some further advantages for certain tasks.