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arXiv:2502.05245

Gaussian Models to Non-Gaussian Realms of Quantum Photonic Simulators

Dennis Delali Kwesi Wayo, Rodrigo Alves Dias, Masoud Darvish Ganji, Camila Martins Saporetti, Leonardo Goliatt

Published Feb 7, 2025• 0 citations
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Abstract

Quantum photonic simulators have emerged as indispensable tools for modeling and optimizing quantum photonic circuits, bridging the gap between theoretical models and experimental implementations. This review explores the landscape of photonic quantum simulation, focusing on the transition from Gaussian to non-Gaussian models and the computational challenges associated with simulating large-scale photonic systems. Gaussian states and operations, which enable efficient simulations through covariance matrices and phase-space representations, serve as the foundation for photonic quantum computing. However, non-Gaussian states crucial for universal quantum computation introduce significant computational complexity, requiring advanced numerical techniques such as tensor networks and high-performance GPU acceleration. We evaluate the leading photonic quantum simulators, including Strawberry Fields, Piquasso, QuTiP SimulaQron, Perceval, and QuantumOPtics.jl analyzing their capabilities in handling continuous-variable (CV) and discrete-variable (DV) quantum systems. Special attention is given to hardware-accelerated methods, including GPU-based tensor network approaches, machine learning integration, and hybrid quantum-classical workflows. Furthermore, we investigate noise modeling techniques, such as photon loss and dark counts, and their impact on simulation accuracy. As photonic quantum computing moves toward practical implementations, advancements in high-performance computing (HPC) architectures, such as tensor processing units (TPUs) and system-on-a-chip (SoC) solutions, are accelerating the field. This review highlights emerging trends, challenges, and future directions for developing scalable and efficient photonic quantum simulators.

DOI
10.48550/arxiv.2502.05245

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In the past few years, quantum computing has gotten more attention, and the need for efficient simulations is getting increasingly important as well. A significant branch of quantum computing is photonic quantum computing. For simulating photonic quantum circuits, Strawberry Fields is the most popular framework. In this paper, we compared it with another framework currently under development called Piquasso regarding gradient calculation time, which is an essential part of continuous-variable quantum neural networks. We present the apparent scalability of Piquasso over Strawberry Fields by storing fewer data, but leading to possible accuracy differences as a trade-off, which could motivate future work.
Recent advancements in quantum photonics have driven significant progress in photonic quantum computing (PQC), addressing challenges in scalability, efficiency, and fault tolerance. Experimental efforts have focused on integrated photonic platforms utilizing materials such as silicon photonics and lithium niobate to enhance performance. Parameters like photon loss rates, coupling efficiencies, and fidelities have been pivotal, with state-of-the-art systems achieving coupling efficiencies above 90% and photon indistinguishability exceeding 99%. Quantum error correction schemes have reduced logical error rates to below 10−310^{-3}10−3, marking a step toward fault-tolerant PQC. Photon generation has also advanced with deterministic sources, such as quantum dots, achieving brightness levels exceeding 10610^6106 photon pairs/s/mW and time-bin encoding enabling scalable entanglement. Heralded single-photon sources now exhibit purities above 99%, driven by innovations in fabrication techniques. High-efficiency photon detectors, such as superconducting nanowire single-photon detectors (SNSPDs), have demonstrated detection efficiencies exceeding 98%, dark count rates below 1 Hz, and timing jitters as low as 15 ps, ensuring precise photon counting and manipulation. Moreover, demonstrations of boson sampling with over 100 photons underscore the growing computational power of photonic systems, surpassing classical limits. The integration of machine learning has optimized photonic circuit design, while frequency multiplexing and time-bin encoding have increased system scalability. Together, these advances bridge the gap between theoretical potential and practical implementation, positioning PQC as a transformative technology for computing, communication, and quantum sensing.
Abstract Quantum key distribution (QKD) represents a cornerstone of secure communication in the quantum era. While discrete-variable QKD (DV-QKD) protocols were historically the first to demonstrate secure key exchange, continuous-variable QKD (CV-QKD) has emerged as a more practical alternative due to its seamless compatibility with current telecommunications infrastructure. CV-QKD relies on coherent and squeezed states of light, offering significant advantages for integration into modern optical networks. This review comprehensively explores the theoretical underpinnings, technological advancements, and practical challenges of CV-QKD. Special attention is given to the role of photonic integrated circuits (PICs) in enabling scalable and efficient implementation of CV-QKD systems. Furthermore, recent advances in machine learning have been leveraged to optimize CV-QKD performance, with data-driven techniques enhancing noise estimation, parameter optimization, and system security. Additionally, tensor networks provide efficient computational tools for analyzing complex quantum correlations, improving the efficiency and robustness of quantum key distribution protocols. These developments, combined with ongoing improvements in quantum photonic integration, pave the way for the practical deployment of large-scale, high-speed quantum-secure networks.