Dennis Wayo

Dennis Wayo is a quantum computational scientist working at the interface of quantum software engineering, photonic architectures, fault tolerance, and large-scale simulation. His research integrates algorithm–hardware co-design with differentiable quantum programming, resource estimation, and error-aware modeling.

He is the creator of SchroSIM, a scalable quantum-photonic circuit simulator designed for Gaussian and non-Gaussian architectures, and lead developer of PhoQSim, a learning-augmented photonic compiler that integrates ML-surrogate models with nonlinear Schrödinger propagation and TDDFT-informed device physics. His work connects the physics of photonic hardware with compiler-level abstractions, a direction that aligns with current research in quantum error correction, MBQC-style cluster-state computation, and software frameworks for large-scale fault-tolerant systems.

Dennis is completing his PhD in Chemical Engineering at Universiti Malaysia Pahang, where he investigates photon activation in rare-earth ion systems and quantum optical models for hydrogen and CO$_2$ splitting. In parallel, he is pursuing the Master of Science in Computer Science (OMSCS) at the Georgia Institute of Technology with a focus on algorithms, systems, and quantum software. He also completed Princeton University’s specialization in Computer Architecture, deepening his understanding of cross-layer compiler/runtime design, a core requirement for emerging quantum architectures.

Quantum Software • Photonic Architectures • Fault-Tolerant Systems

His technical expertise includes:

• Quantum Simulation – TDDFT (GPAW), NLSE/SSFM photonics, MEEP-FDTD, Gaussian/non-Gaussian models
• Quantum Software – PennyLane, Qiskit, photonic QNNs, QLS, VQE/ADAPT-VQE
• Fault Tolerance – bosonic encodings, coherent-state codes, noise modeling, cluster-state intuition
• Scientific ML – PINNs, GNNs, SHAP/XAI, symbolic-graph models
• HPC & Systems – GPU pipelines, containerized workflows, distributed scientific computing

He has collaborated across disciplines spanning materials science, photonics, scientific ML, and quantum computing. His current research direction focuses on architecture-aware quantum software — the design of simulation, compilation, and resource estimation tools for scalable quantum systems, particularly in photonic and continuous-variable platforms.

Research Focus

Quantum Software & Compilation

• Photonic circuit simulation (Gaussian + non-Gaussian)
• Differentiable programming and variational algorithms
• based resource estimation and gate-depth prediction
• Algorithm decomposition and hardware mapping

Quantum Architectures & Fault Tolerance

• Cluster-state photonic computation (MBQC intuition)
• Bosonic codes and coherent-state redundancy schemes
• Error propagation modeling and QEC-inspired encodings

Computational Photonics & Materials Modeling

• TDDFT/LR-TDDFT for quantum emitters and rare-earth ions
• NLSE propagation, soliton/dispersion modeling, detector physics
• MEEP-FDTD validation of chip-scale photonic components

Current Projects

SchroSIM: A scalable quantum photonic circuit simulator integrating NLSE propagation, Gaussian/non-Gaussian transformations, and differentiable quantum-classical workflows.

PhoQSim: A learning-augmented photonic compiler combining ML surrogates with physics-based simulation for rapid resource estimation.

Q-UCSpec (IBM QAMP): Quantum feature encoding and QML benchmarking for spectroscopic signals (VQE, QSVM, EstimatorQNN, QLS).

TDDFT–FDTD Photonic Modeling: Atomistic-to-optical pipeline for quantum emitters, defect centers, and upconversion materials.

PiHOG: Photon-informed graph neural network for IR peak classification

XaiGis: Explainable AI framework for hydrogen reservoir mapping

TDDFT Filter Cake Stability Quantum modeling of chelating agents for wellbore cleanup

Affiliations & Recognition

• IEEE Member
• IBM QAMP (Mentor) 2025
• Womanium Quantum Scholar 2025
• IBM Quantum Excellence 2024
• Georgia Tech, UMPSA, NU Graduate Research Collaborations

• Contributor on Medium & Dev.to

  GitHub: DennisWayo