Classical Simulation Techniques for Photonic Quantum Computing

Dennis Delali Kwesi Wayo, Rodrigo Alves Dias, Masoud Darvish Ganji +2 more

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.

Rafael Pereira da Silva

Jun-Jie He, Ke-Ming Hu, Yu-Ze Zhu +12 more

We introduce DeepQuantum, an open-source, PyTorch-based software platform for quantum machine learning and photonic quantum computing. This AI-enhanced framework enables efficient design and execution of hybrid quantum-classical models and variational quantum algorithms on both CPUs and GPUs. For photonic quantum computing, DeepQuantum implements Fock, Gaussian, and Bosonic backends, catering to different simulation needs. To our knowledge, it is the first framework to realize closed-loop integration of three paradigms of quantum computing, namely quantum circuits, photonic quantum circuits, and measurement-based quantum computing, thereby enabling robust support for both specialized and universal photonic quantum algorithm design. Furthermore, DeepQuantum supports large-scale simulations based on tensor network techniques and a distributed parallel computing architecture. We demonstrate these capabilities through comprehensive benchmarks and illustrative examples. With its unique features, DeepQuantum is intended to be a powerful platform for both AI for Quantum and Quantum for AI.

John R. Scott

Zhenwei Yang, Jiacheng Bao, H. J. Sun +1 more

A Boson sampling device is believed to be a specific quantum computer which is more efficient than its classical counterpart. Recently, a number of experimental realizations have been reported, all were based on multi-photon interference in multimode interferometers. Because of the probabilistic nature of single photons, the technique of coincidence counting has to be used in these schemes, it remains a great challenge to scale up the number of photons. Here, we present a novel way to simulate the solution of the boson sampling problem based on classical electronic circuits. Although an exponential frequency bandwidth is used, thus limiting the scalability of the scheme, the running time of our scheme is equivalent to those based on quantum optics. It is also notable that our method has good stability of classical circuits. In addition, some other related problems, which are computationally hard for classical computers, can also be simulated using our scheme. Thus, our findings are advantageous for information processing in the era of big data.

Alán Aspuru‐Guzik, Philip John Walther