// Electronics · Neural Networks · Quantum Systems
Building at the
Edge of Physics
& Silicon
Engineer and researcher working across embedded electronics, machine learning architectures, and quantum circuit simulation. Bridging the physical and computational worlds.
Who I am
Systems thinker.
Circuit builder.
I work at the intersection of hardware and intelligent systems — designing circuits that sense and respond, training models that learn from probabilistic data, and simulating quantum phenomena that classical computers struggle to describe.
My approach is grounded in first principles: understanding a system from the physics up, then building the software to match. Whether that's a quantum circuit simulation, classical circuit, or neural network.
What I've built
Selected Projects
Quantum Chess Neural Network
Built a neural network in an AlphaZero style to play Chris Cantwell's probabilistic chess variant using ROCm, CUDA, PyTorch and the Quantum Chess SDK.
CPU from Scratch
Designed a simple 8-bit CPU built entirely from 4000-series logic gates, from ALU to control unit.
Quantum Guitar Effects Pedal
Uses the Quantum Forge SDK to implement quantum-probabilistic audio effects that evolve with the quantum circuit state.
Aircraft Navigation System
Grid-matrix terrain mapping with Manhattan distance pathfinding — implemented entirely in 4000-series logic gates.
Second Quantum Chess Engine
A second engine using Quantum Forge and the Quantum Chess SDK to benchmark Alpha-Beta vs MCTS algorithms in a quantum-probabilistic game tree.
Automatically Sorting Bin
Automated sorting system using a neural network to classify objects, driving motor control to route each item into the correct compartment.
What I've studied
Courses & Certifications
Quantum Computing — From Linear Algebra to Entanglement
MIT OpenCourseWare · 2024
Covered linear algebra fundamentals, qubit representations, quantum gates, entanglement, and the Deutsch-Jozsa, Grover, and Shor algorithms. Built intuition for why quantum systems outperform classical on specific problem classes.
Deep Learning Specialisation
Coursera / deeplearning.ai · Andrew Ng · 2023
Five-course series spanning neural network foundations, hyperparameter tuning, regularisation, CNNs, sequence models (RNNs, LSTMs, Transformers), and practical ML project strategy.
Digital Design & Computer Architecture
ETH Zürich / edX · 2023
From Boolean logic and combinational circuits through to pipelining, caches, and RISC-V architecture. Directly informed the CPU-from-scratch project using 4000-series gates.
Embedded Systems Programming on ARM
Udemy — FastBit Embedded Brain Academy · 2023
Bare-metal programming on STM32 Cortex-M microcontrollers: GPIO, timers, interrupts, DMA, SPI/I2C/UART, and the HAL/LL driver layers. Foundational for embedded ML deployment work.
CS231n: CNNs for Visual Recognition
Stanford University · 2024
Deep-dive into convolutional architectures (AlexNet → ResNet → Vision Transformers), backpropagation, batch normalisation, transfer learning, and object detection pipelines including YOLO and Faster R-CNN.
Quantum Machine Learning
PennyLane / Xanadu · 2024
Covering variational quantum circuits as ML models, quantum kernels, the barren plateau problem, and hybrid classical-quantum training loops using PennyLane's autodiff framework.
Advanced FPGA Design with Chisel
UC Berkeley / edX · Planned
Will cover hardware construction in Scala/Chisel, generating synthesisable RTL, and building parameterised hardware generators — aiming to accelerate neural network inference on FPGAs.
Reinforcement Learning from Human Feedback
Hugging Face Deep RL Course · 2025
Planned study of PPO, RLHF pipelines, reward modelling, and alignment techniques — exploring how human preference data shapes model behaviour in large language and policy models.
Get in touch
Let's build something
extraordinary
Open to collaborations in quantum simulation, embedded ML, and hardware-accelerated AI. Happy to discuss research ideas, consulting, or just interesting problems.