Quantum Computing Research Engineer Focused on quantum algorithms (VQE, QPE, Shor), numerical simulation, and physics-driven modelling, with an emphasis on building practical PyPI packages.
I have a First Class MSci in Theoretical Physics & Mathematics from Lancaster University and I build research-grade open-source projects across quantum computing, photonics, numerical methods, and applied optimisation. My work spans quantum chemistry, quantum error mitigation, classical simulations of quantum algorithms, and high-performance scientific modelling.
I'm currently in Sydney, Australia with a working holiday VISA (subclass 417), looking for work!
A modular, reproducible PennyLane-based quantum chemistry simulation suite for small-molecule benchmarks (H₂, LiH, H₂O, H₃⁺), shipped as a versioned Python package with CLI tooling and consistent caching.
- VQE (ground state) + ADAPT-VQE
- Excited states: LR-VQE (tangent-space / TDA), QSE, SSVQE, VQD
- QPE (noisy + noiseless) and VarQITE/QITE (McLachlan updates)
- Unified
common/layer: Hamiltonians, molecule registry, geometry scans, plotting, persistence - PyPI: vqe-pennylane
Reusable quantum machine learning library built on PennyLane, engineered with the same modular architecture as my VQE toolkit.
- Variational Quantum Classifier (VQC) with configurable ansatz layers and optimizers
- Quantum kernel workflows integrated with sklearn SVC
- Variational regression with standardized training and evaluation pipelines
- Deterministic experiment configs, structured result persistence, and consistent plotting utilities
- CLI + API workflows; notebooks serve as thin usage examples
- PyPI: qml-pennylane
Quantum optimisation toolkit for portfolio problems, engineered as a clean Python library (notebooks are thin clients).
- Binary selection: cardinality-constrained QUBO/Ising formulation solved with VQE and QAOA
- Fractional allocation: simplex-constrained ansatz for long-only weights (constraint by construction)
- QUBO → Ising Hamiltonian pipeline from covariance matrices and expected returns
- CLI + API workflows, λ-sweeps, efficient frontier utilities
- PyPI: vqe-portfolio
Lightweight PennyLane-based toolkit for spectral transformations via bounded polynomials, designed to make Quantum Singular Value Transformation (QSVT) more experimentally accessible through reusable Python components.
- Polynomial functional calculus perspective on QSVT and QSP
- Bounded polynomial design utilities (Chebyshev approximations, sign, inverse-like, filters)
- Thin wrappers around PennyLane QSVT primitives for scalar and diagonal transforms
- Classical spectral reference implementations for verification and intuition
- Modular utilities for reusable polynomial workflows
- CLI + API workflows; notebooks emphasise spectral interpretation and reproducibility
- PyPI: qsvt-pennylane
A full simulation suite exploring nonlinear gain/loss, edge modes, and stability regimes in non-Hermitian topological lattices (NRSSH & Diamond models). Includes phase diagrams, time-evolution solvers, Hamiltonian construction, and analysis relevant to photonics, nonlinear optics, and topological quantum systems. This is revamped code from my uni dissertation: "Dynamics of Topological Photonics with Nonlinear Saturable Gain and Loss".
A pure-Python, matrix-based classical simulation of Shor’s quantum factoring algorithm. Implements superposition, modular exponentiation, IQFT, probability visualisation, runtime scaling, and educational tooling without relying on quantum frameworks.
A numerical physics project comparing Euler, Midpoint, Heun, and RK4 schemes through gravitational simulations. Includes projectile motion, two-body and three-body orbits, chaos behaviour, and energy-conservation analysis implemented primarily in R.
Explores the fundamentals of quantum error correction and early implementations of small error-correcting codes. A growing project aligned with my long-term interest in fault-tolerant quantum computing.
MSci Theoretical Physics & Mathematics (First Class) – Lancaster University Former Data Analyst with experience automating workflows, building analytical pipelines, and using data-driven insights for decision support.
LinkedIn: https://www.linkedin.com/in/sid-richards-21374b30b/
If my projects help your research or work, consider sponsoring development. GitHub Sponsors helps maintain documentation, examples, and new features.