This PDF file contains the front matter associated with SPIE Proceedings Volume 13106, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurements on the RPs then used to modulate a real local oscillator (RLO) on the ground - a solution that also removes some known attacks on CV-QKD. However, this is a cumbersome task in practice - requiring substantial complexity in equipment requirements and deployment. As an alternative to this traditional practice, here we introduce a new method for estimating phase corrections for an RLO by using only intensity measurements from RPs as input to a convolutional neural network, mitigating completely the necessity to measure phase wavefronts directly. We show that the phase correction accuracy needed to provide for non-zero secure key rates through satellite-to-ground channels is achieved by our intensity-only measurements. Our work shows, for the first time, how artificial intelligence algorithms can replace phase-measuring equipment in the context of CV-QKD delivered from space, thereby delivering an alternate deployment paradigm for this global quantum-communication application.

Entangled photon pair sources relying on spontaneous parametric down-conversion (SPDC) are an essential ingredient for optical quantum technologies. Currently, the vast majority of SPDC sources is based on solid-state crystals. However, their rigid nature implies a careful design of the source as well as it limits the two-photon state that can be generated, without much possibility of tuning it. Here, we demonstrate a new generation of efficient, highly tunable sources of entangled photons based on recently discovered liquid crystal materials called ferroelectric nematic liquid crystals (FNLC). In addition to other highly desirable properties that liquid crystals possess, such as the ability to self-assemble into complex structures, strong response to the electric field and integrability into complex optical systems, FNLCs also possess considerable optical nonlinearity. This, as we demonstrate, enables also efficient SPDC generation and opens the door for promising new functionalities in quantum technologies. This work demonstrates the first-ever realization of SPDC in soft, organic matter, with the possibility of controlling the molecular order and thus tuning the two-photon polarization state. We show that almost any polarization state can be generated by simply adjusting the twist of the molecular orientation along the sample. Additionally, by applying only a few volts, we can drastically alter both the emission rate and the generated state of photon pairs, which enables real-time tunability. Developed concepts could lead to complex multi-pixel devices generating quantum light with real time tunability and therefore hold the potential to have a huge impact in the field of quantum technologies.

Recent progress in the study of the non-classical macroscopic state of light sheds light on the characterization of high-gain spontaneous parametric down-conversion (SPDC). However, the theory of high-dimensional entangled photon pairs with SPDC in this high-gain regime is far from being complete. Here, we present a numerical study of the effect of the phase-matching function on the OAM entanglement in high-gain SPDC. We use a recently developed classical model of high-gain SPDC to compute the OAM spectrum of the entangled photon pair and examine how the OAM entanglement depends on various physical parameters determining the phase-matching function. Our study offers guidelines for quantum state engineering with bright sources of photon pairs entangled in high-dimensional OAM spaces.

We study Polarization Mode Dispersion (PMD) in fiber-based Quantum Key Distribution (QKD) using a broadspectrum polarization-entangled photon source. We analyze wavelength-dependent polarization transformations over 10 km deployed fiber channels, with measurements spanning one year of evolution. By examining the rotation of polarization states on the Poincare sphere and projecting them along a PMD trajectory onto a predefined state within a measurement basis, we derived a simple new formula that accurately links the measurement error probability to finite signal bandwidth and the observed differential group delay (DGD). Finally, we propose strategies to mitigate the PMD effect for entangled-based QKD by optimizing the orientation of the measurement bases and by performing the optimal spectral filtering of the source.

We demonstrate the feasibility of time-bin encoding in the third telecommunication window within the frame of Quantum Key Distribution (QKD) protocol in Free Space Optical (FSO) horizontal links. Operating at a 0.6 GHz repetition rate, the QKD transmitter delivers time-bin qubits at 1558.98nm over a turbulent channel linking two nearby buildings in the city of Florence. To mitigate those effects, the receiver mounts a tip/tilt adaptive system which proves a better Free Space (FS) to fiber coupling ratio stability with respect to no active control. Furthermore, we show that the Photonic Integrated Circuit (PIC) unbalanced Mach-Zender Interferometer (uMZI) in the quantum detection scheme, combined with Superconducting Nano-Wire Single Photon Detectors (SNSPD), guarantees long stability and an high Secret Key Rate (SKR) in the order of the thousands of kilobits per second (kbps).

The data reuploading trick was originally proposed for quantum computing to achieve the universal approximation property. In this paper, we introduce data reuploading to realize universal non-quantum photonic computing with practical photonic integrated circuits (PICs). We aim to comprehensively discuss the various advantages and implementation considerations of this approach. Our framework can eliminate the need of quantum squeezed lights, photon counters, and nonlinear photonics, which have been essential for enabling photonic neural networks in conventional configurations. Additionally, we explore ways to minimize the optical components by combining multiple functionalities into a single phase shifter, showing competitive performance when compared to using the same number of phase shifters, all without employing any nonlinear photonic devices. Considering these characteristics, our investigation into the use of PICs for data reuploading presents a novel architectural approach to realize photonic neural networks. This approach embodies unique features that distinctly set it apart from traditional photonic neural networks.

The second quantum revolution, Quantum 2.0, is fueled by recent progress in generating and manipulating quantum states in both light and matter, leading to new applications such as quantum sensing, computing, and communications. These new applications, which leverage unique quantum properties, such as superposition, entanglement, and measurement sensitivity of quantum states to offer fundamental advantages over classical technologies, are in principle enabled by the Quantum 1.0 technologies such as lasers. As quantum information science and technology progresses steadily from a purely academic discipline towards technology demonstrations, the imperative to transition from laboratory-grade lasers to industry-grade lasers becomes evident in the quest for scalability, robustness, improved performance, and often, reduced SWaP (Size, Weight, and Power) for field deployability. This paper provides an overview of the current state and challenges of laser-enabled quantum applications and outlines the advancements in laser technologies from macro-optics to micro-optics to integrated photonics with their prospects towards the practical realization of quantum advantage.

Heterogeneous computing (HC) systems are essential parts of modern-day computing architectures such as cloud, cluster, grid, and edge computing. Many algorithms exist within the classical environment for mapping computational tasks to the HC system’s nodes, but this problem is not well explored in the quantum area. In this work, the practicality, accuracy, and computation time of quantum mapping algorithms are compared against eleven classical mapping algorithms. The classical algorithms used for comparison include A-star (A*), Genetic Algorithm (GA), Simulated Annealing (SA), Genetic Simulated Annealing (GSA), Opportunistic Load Balancing (OLB), Minimum Completion Time (MCT), Minimum Execution Time (MET), Tabu, Min-min, Maxmin, and Duplex. These algorithms are benchmarked using several different test cases to account for varying system parameters and task characteristics. This study reveals that a quantum mapping algorithm is feasible and can produce results similar to classical algorithms.

Quantum entanglement is an essential element for building the backbone of quantum information systems. Our particular interest lies in long-range distribution of entangled photons to facilitate secure data transfer in free space. To achieve this, we rely on photon pairs generated in such a way that their polarization characteristics represent the corresponding qubit states and have a high degree of correlation in measurements. The main focus of this paper is integrity of the quantum states in free-space channels. When transmitted in atmosphere, classical signals suffer from wave front distortions caused by the spatial and temporal fields of the refractive index. However, this mechanism does not have the same bearing on qubit values and their correlation. We study the effects of turbulence on quantum states by utilizing a laboratory testbed that includes an atmospheric chamber developed by the AFRL. It uses a system of controlled components capable of creating various turbulence conditions. When polarized signals are passed through the atmospheric chamber, we analyze the corresponding quantum states and evaluate the degree of entanglement using our mathematical models and existing metrics.