Dennis Wayo

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.

This study presents a comprehensive numerical analysis of a quantum-dot-engineered heterostructure, PbS:Yb3+,Er3+/CuBiO, optimized for water splitting applications. Using density functional theory (DFT) coupled with machine learning, the study explores the electronic, optical, and catalytic properties of the material. The optimized PbS structure exhibited a direct bandgap of 1.191 eV, while co-doping with Yb and Er transitioned the material to a metallic state, enhancing charge carrier mobility and electron-hole separation. The final heterostructure displayed an indirect bandgap of 0.431 eV, favorable for visible-light absorption. Key findings include an internal electric field strength of 6.3 Debye, efficient charge transfer confirmed by Bader analysis, and strong optical absorption at 2.4 eV. Machine learning models, including DNN and LSTM, were employed to predict photon absorption rates, achieving mean squared errors as low as 0.0004. The synergistic effects of enhanced internal electric fields, optimized band structures, and strong photon absorption underscore the material’s potential for efficient hydrogen production. This study bridges advanced computational methods and machine learning, offering a framework for designing high-performance photocatalysts in renewable energy applications.

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.

Accurate multiphase flowing bottom-hole pressure prediction within wellbores is a critical requirement to improve tube design and production optimization. Existing models often struggle to achieve reliable accuracy across the full range of operational conditions encountered in oil and gas wells. This can lead to misallocating resources during well design, inefficient production strategies resulting in lost revenue, increased risk of wellbore damage, and poorly informed investment decisions. This research presents a data-driven hybrid approach that uses a Radial Basis Function Neural Network and a Particle Swarm Optimization algorithm to construct an automated hybrid machine learning model. The proposed model was compared with several well-established machine learning models in the literature using the same computational framework. The modeling results demonstrated the superiority of the hybrid approach. The model achieved superior performance with lower errors, as evidenced by a Relative Root Mean Squared Error (RRMSE) of 0.055. Furthermore, the model exhibited a low level of uncertainty throughout the analysis, indicating its high degree of reliability. These findings suggest the proposed data-driven approach offers a robust and practical solution for FBHP prediction in oil and gas wells.

Designing drilling mud rheology is a complex task, particularly when it comes to preventing filter cakes from obstructing formation pores and making sure they can be easily decomposed using breakers. Incorporating both multiphysics and data-driven numerical simulations into the design of mud rheology experiments creates an additional challenge due to their symmetrical integration. In this computational intelligence study, we introduced numerical validation techniques using 498 available datasets from mud rheology and images from filter cakes. The goal was to symmetrically predict flow, maximize filtration volume, monitor void spaces, and evaluate formation damage occurrences. A neural-objective and image optimization approach to drilling mud rheology automation was employed using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and an objective optimization tool. These methods utilized the Google TensorFlow Sequential API-DNN architecture, MATLAB-nftool, the MATLAB-image processing tool, and a single-objective optimization algorithm. However, the analysis emanating from the ANN-FF and non-ANN-FF (with neurons of 10, 12, and 18) indicated that, unlike non-ANN-FF, ANN-FF obtained the highest correlation coefficient of 0.96–0.99. Also, the analysis of SBM and OBM image processing revealed a total void area of 1790 M µm2 and 1739 M µm2, respectively. Both SBM and OBM exhibited notable porosity and permeability that contributed to the enhancement of the flow index. Nonetheless, this study did reveal that the experimental-informed single objective analysis impeded the filtration volume; hence, it demonstrated potential formation damage. It is, therefore, consistent to note that automating flow predictions from mud rheology and filter cakes present an alternative intelligence method for non-programmers to optimize drilling productive time.

Data-driven models with some evolutionary optimization algorithms, such as particle swarm optimization (PSO) and ant colony optimization (ACO) for hydraulic fracturing of shale reservoirs, have in recent times been validated as one of the best-performing machine learning algorithms. Log data from well-logging tools and physics-driven models is difficult to collate and model to enhance decision-making processes. The study sought to train, test, and validate synthetic data emanating from CMG’s numerically propped fracture morphology modeling to support and enhance productive hydrocarbon production and recovery. This data-driven numerical model was investigated for efficient hydraulic-induced fracturing by using machine learning, gradient descent, and adaptive optimizers. While satiating research curiosities, the online predictive analysis was conducted using the Google TensorFlow tool with the Tensor Processing Unit (TPU), focusing on linear and non-linear neural network regressions. A multi-structured dense layer with 1000, 100, and 1 neurons was compiled with mean absolute error (MAE) as loss functions and evaluation metrics concentrating on stochastic gradient descent (SGD), Adam, and RMSprop optimizers at a learning rate of 0.01. However, the emerging algorithm with the best overall optimization process was found to be Adam, whose error margin was 101.22 and whose accuracy was 80.24% for the entire set of 2000 synthetic data it trained and tested. Based on fracture conductivity, the data indicates that there was a higher chance of hydrocarbon production recovery using this method.