Jeffrey Morais

Quantum Software Engineer @ BTQ
MSc Student @ UVic

Welcome, Traveler.

Quantum software engineer with 2+ years of experience building cryptographic and distributed systems, grounded in 8 years of computational physics. Currently working on post-quantum infrastructure at BTQ while completing an MSc in Physics at the University of Victoria. BSc Honours Physics, McGill University.

At BTQ, I design low-latency cryptographic and distributed systems, focusing on execution efficiency, fault tolerance, and scalable protocol design for post-quantum environments. My work spans error-correcting architectures, randomness generation for secure systems, and performance-critical protocol optimization.

At UVic, I develop high-performance simulation engines for quantum many-body systems, optimizing memory usage, parallel execution, and computational complexity to study emergent dynamics at scale. This work informs how physical constraints translate into computable and deployable systems.

I build high-performance scientific software and the systems that make it run.

Python C++ Julia HPC Clusters Parallel Computing Quantum Error Correction Post-Quantum Cryptography

Interests

High-performance computing
Scientific software engineering
Post-quantum cryptography
Large-scale simulation systems

Education

MSc in Physics, 2024 - 2026
University of Victoria
BSc in Honours Physics, 2019 - 2023
McGill University

Work

Production quantum software and post-quantum cryptographic systems developed at BTQ Technologies, from error correction toolkits and consensus protocol engineering to quantum random number generation and threat analytics.

QLDPC

Built an interactive 3D quantum circuit builder in Python at BTQ Technologies to accelerate error correction research, integrating surface code construction, LDPC code visualization, and belief propagation decoding into a single Qiskit-compatible toolkit with guided tutorials.

Topological Quantum Neural Networks

Developed topological quantum neural networks in Python to improve generalization in deep learning and boost quantum algorithm efficiency. Employed TQFT-inspired spin-network encoding with a real-time tensor network simulator and interactive classification GUI for robustness testing.

Leonne

Created modular consensus networks in Python at BTQ Technologies for cryptographic proof in blockchain, leveraging topological data analysis to strengthen post-quantum consensus protocols and characterize autonomous network evolution in quantum key distribution systems.

QRiNG

Built a quantum random number generation toolkit in Python at BTQ Technologies for consensus protocols, employing quantum key distribution through Quantum Consensus Networks to produce certifiable randomness with honest-majority verification, enhancing security in cryptographic and blockchain systems.

BasiQ and Qonductor

Developed two quantum-specialized LLM assistants in TypeScript and Next.js at BTQ Technologies to streamline research and education workflows. BasiQ serves as a domain-tuned tutor for quantum theory, computing, and machine learning. Qonductor provides an internal research assistant for academic and engineering teams.

QByte

Built a quantum industry analytics platform in TypeScript at BTQ Technologies to quantify cryptographic risk timelines and educate stakeholders on quantum threats to public-key infrastructure. Features a quantum risk calculator, threat timeline modeling, and post-quantum migration resources.

Publications

Early work on the swampland criteria and de Sitter vacua in string theory, published in the McGill Science Undergraduate Research Journal. Established the mathematical foundations that inform my approach to quantum software design.

Jeffrey Morais (2022). Conflicts with de Sitter Vacua in Superstring Theory. MSURJ.

Cite DOI

Research

Computational physics and quantum information research that underpins my engineering work, spanning quantum many-body thermalization, topological neural networks, holographic entanglement, and signal processing.

Eigenstate Thermalization Hypothesis

Master’s Thesis. Systematic verification of the eigenstate thermalization hypothesis across quantum many-body models, investigating thermalization behavior and quantum chaos in non-integrable systems. Applied scaling analysis and computational methods to characterize integrability transitions.

Collaborator: Prof. Thomas Baker

Consensus Optimization in Quantum Cryptography

Developed secure and scalable protocols for quantum and post-quantum cryptography, leveraging advanced techniques like topological data analysis and combinatorics. Focused on enhancing security and efficiency in quantum communication and consensus protocols.

Supervisor: Prof. Gavin Brennen

Consensus Optimization in Quantum Cryptography

Topological Quantum Neural Networks Learning

Developed topological quantum neural networks to enhance deep learning generalization and quantum algorithm efficiency. Modeled information flow using topological quantum field theory, enabling scalable quantum computing tasks.

Supervisors: Prof. Antonino Marcianò, Prof. Emanuele Zappala

Topological Quantum Neural Networks Learning

Holographic Qubit Entanglement Structure

Explored how topological wormholes influence qubit entanglement and quantum network stability. Analyzed tunneling events and wormhole dynamics to improve quantum algorithms and architectures.

Supervisor: Prof. Igor Boettcher

Holographic Qubit Entanglement Structure

de Sitter Cosmology in Quantum Gravity

Bachelor’s Thesis. Investigated the existence of de Sitter vacua in string theory and their role in modeling our universe. Analyzed quantum effects on compactified spaces to bypass theoretical constraints and enhance understanding of quantum gravity.

Supervisor: Prof. Keshav Dasgupta

Cosmic String Signal Extraction

Modeled cosmic string signals amidst non-linear noise from early-universe phase transitions. Utilized match-filtering techniques to identify signals and study their formation and evolution using quantum field theory.

Supervisor: Prof. Robert Brandenberger

Photon Recycling Effects in Light Propulsion

Studied photon-recycling propulsion systems, focusing on quantum effects and efficient momentum transfer. Analyzed light interactions and radiation pressure to optimize energy transfer for quantum and aerospace technologies.

Supervisor: Prof. Andrew Higgins

Photon Recycling Effects in Light Propulsion

Fast Radio Burst Repeater Correlations

Analyzed fast radio bursts signals to explore black-white hole tunneling connections. Processed CHIME data to correct for noise and characterize scintillation patterns, improving astrophysical signal interpretation.

Supervisor: Prof. Victoria Kaspi

Tensor Networks for Cancer Radiation Doses

Developed neural network models to optimize radiation therapies for tumors. Simulated helical trajectories to reduce damage to healthy tissues while enhancing targeting precision.

Supervisor: Prof. Marija Popovic

Tensor Networks for Cancer Radiation Doses

Gamma Ray Bursts in Tidal Disruption Events

Studied γ-rays in supernovae and tidal disruption events using Fermi-LAT data. Cleaned background noise and performed statistical analyses to identify and characterize high-energy sources.

Supervisor: Prof. Kenneth Ragan

Gamma Ray Bursts in Tidal Disruption Events

Quantum Trajectory Neural Networks

Solved quantum trajectories in pilot-wave theory using neural networks and the Crank-Nicolson method. Developed scripts to efficiently compute trajectories for diverse potentials in quantum systems.

Supervisor: Prof. Ivan Ivanov

Topological Confinement of Light in Circuits

Investigated topological confinement in nanobeam microcavities to optimize photonic circuits. Simulated resonant modes to control frequency, intensity, and phase for quantum and optical applications.

Supervisor: Prof. Pablo Bianucci