Dennis Wayo

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.

This study advances hydraulic fracturing simulations in shale reservoirs using two computational paradigms, Physics-Informed Neural Networks (PINNs) and the Variational Quantum Eigensolver (VQE). PINNs are employed to solve Nordgren’s equation, which governs fracture width evolution, by embedding physical laws into the neural network architecture. Using TensorFlow on Google Colab, the PINN training process incorporates Adam, L-BFGS, and Newton-CG optimizers, achieving an accuracy of 1.23x10-9 for fluid viscosity μ= 8.0 Pa.s, fracture height H = 2.0m, and leak-off coefficient CL = 1.0. However, this approach demands significant computational resources, with training times exceeding 1454 seconds and memory usage of 1136 MB in some cases. Conversely, the VQE framework leverages Qiskit on Qbraid to optimize the Hamiltonian representing the fracture system. With qubit-based ansatz circuits and classical optimizers (SPSA, COBYLA, and L-BFGS), VQE achieves rapid energy minimization, converging to approximately -0.583+0j in under 2 seconds with negligible memory requirements. Spatiotemporal fracture width predictions from VQE align well with expected trends but exhibit slight oscillations due to quantum noise. This comparative study highlights the trade-offs where PINNs excel in accuracy and physical adherence, while VQE offers unparalleled efficiency for rapid computations. The hybrid application of PINN precision and VQE speed provides a robust toolkit for optimizing hydrocarbon recovery. Future research will explore integrating these paradigms for scalable, high-fidelity simulations across complex geological settings.

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 , marking a step toward fault-tolerant PQC. Photon generation has also advanced with deterministic sources, such as quantum dots, achieving brightness levels exceeding 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.

Photocatalytic water splitting has emerged as a sustainable pathway for hydrogen production, leveraging sunlight to drive chemical reactions. This review explores the integration of density functional theory (DFT) with machine learning (ML) to accelerate the discovery, optimization, and design of photocatalysts. DFT provides quantum-mechanical insights into electronic structures and reaction mechanisms, while ML algorithms enable high-throughput analysis of material properties, prediction of catalytic performance, and inverse design. This paper emphasizes advancements in binary photocatalytic systems, highlighting materials like , , and , as well as novel heterojunctions and co-catalysts that improve light absorption and charge separation efficiency. Key breakthroughs include the use of ML architectures such as random forests, support vector regression, and neural networks, trained on experimental and computational datasets to optimize band gaps, surface reactions, and hydrogen evolution rates. Emerging techniques like quantum machine learning (QML) and generative models (GANs, VAEs) demonstrate the potential to explore hypothetical materials and enhance computational efficiency. The review also highlights advanced light sources, such as tunable LEDs and solar simulators, for experimental validation of photocatalytic systems. Challenges related to data standardization, scalability, and interpretability are addressed, proposing collaborative frameworks and open-access repositories to democratize DFT-AI tools. By bridging experimental and computational methodologies, this synergistic approach offers transformative potential for achieving scalable, cost-effective hydrogen production, paving the way for sustainable energy solutions.

Chelating agents such as Ethylene-Diamine-Tetra-Acetic (EDTA) had been suggested to be a newly designed biodegradable filter cake breakers that could enhance efficiency in filter cake removal. This chelate-based fluid had been proven to effectively dissolved the bridging agent in the filter cake which was calcium carbonate causing the removal of the filter cake to be successful. Besides that, the ability of the chelating agents to provide low corrosion potential made chelating agent as a great alternative for live mineral acids. This provided less aquatic toxicity, more human and environment friendly and readily. However, traditional EDTA cannot performance well in the high-pressure, high-temperature (HPHT) wellbore environments. Therefore, in this study, we explore the use of Titanium-enhanced ethylenediaminetetraacetic acid (Ti-EDTA) as a novel chemical breaker for the decomposition of filter cakes formed in high-pressure, high-temperature (HPHT) wellbore environments. Using ab initio molecular dynamics (AIMD) simulations within the Quantum Espresso framework version 7.2, we examine the molecular interactions, atomic displacements, and thermodynamic behavior of Ti-EDTA in contact with synthetic-based mud (SBM) residues. The simulations track the structural evolution of Ti-EDTA during filter cake decomposition, revealing significant molecular rearrangements and enhanced solubility of the filter cakes. Compared to traditional EDTA-based and silica-based breakers, Ti-EDTA shows improved potential energy and particle mobility, indicative of greater decomposition efficiency. Our results indicate that EDTA resulted in a more negative potential energy of −1035 Ry signifying a stable system, whereas Ti-EDTA simulation yielded a less stable system with a value of −935 Ry energy. The atomic interactions at both levels indicated that Ti-EDTA possesses higher temperature by 16000 K and with a coupled higher potential energy makes an effective chemical to enhance decomposition reactions. For a higher rate of potential energy, temperature and volume, Ti-EDTA molecule best beats EDTA in terms AIMD simulated efficiency. The integration of titanium ions into the EDTA structure significantly boosted the simulation performance, making it a promising candidate for environmentally friendly wellbore cleanup applications. The study provides insights into the molecular mechanisms driving filter cake degradation and sets the stage for future experimental validation of Ti-EDTA’s efficacy in field operations.