Research
My research connects quantum algorithms and hybrid quantum-classical learning with practical computational and experimental problems. Current work spans optimization, cryptanalysis, quantum workflows, and machine learning for biotechnology. I am also exploring quantum computing for drug discovery and quantum metrology.
Browse the themes below for related publications and presentation materials, or use the navigation above to explore the full lists.
Research at a glance
Quantum algorithms, hybrid learning, and data-driven biotechnology—from computational methods to experimental applications.
Based on the work listed on this site. Publications count distinct works; preprint and published versions count once. Journal papers include dataset articles.
Research themes
Quantum optimization
Designing QAOA variants that balance expressive power, parameter training, and circuit depth. I study how graph symmetries and perturbations affect MaxCut, and apply quantum optimization to time-series anomaly detection and constrained vehicle dynamics.
Hybrid quantum-classical learning
Combining quantum circuits with classical learning architectures for multivariate time series, pattern completion, classification, and clustering. My work includes quantum-enhanced recurrent models and quantum hyperdimensional computing, alongside talks on generative modelling and knowledge distillation.
Quantum algorithms & cryptanalysis
Studying Grover search and quantum walks within a common circuit framework for cryptanalysis. I examine how noise, circuit resources, and interactions with an environment change success probabilities and time-space trade-offs on near-term quantum devices.
Quantum workflow orchestration
Making quantum-enhanced tasks usable on computing clusters through modular workflows and accessible interfaces. At CERN, I developed an orchestration framework using Streamlit, Kubernetes, and Argo Workflows for tasks such as circuit cutting and sample-based diagonalization.
Machine learning for biotechnology
Using experimental data and predictive models to guide microfluidic liposome formulation for nanomedicine. This work connects formulation and flow conditions with particle size and polydispersity, supported by independent wet-lab validation, curated datasets, and an open-source design toolkit.
Emerging direction Quantum computing for drug discovery
Exploring how quantum computing could contribute to drug-discovery problems, including molecular modelling and hybrid computational workflows. This direction brings my interests in quantum algorithms and biotechnology into a shared research programme.
Emerging direction Quantum metrology
Exploring quantum approaches to parameter estimation and precision measurement. Related foundations include my work on perturbative descriptions of quantum systems and an early experimental study of Planck's constant using LEDs.
Research in practice
Quantum computing & connected systems
Biotechnology & molecular discovery
Applications
Application domains connecting my work in quantum computing, optimization, and machine learning with engineering problems.
Telecommunication networks
Network design, routing, and resource allocation as settings for graph-based optimization and hybrid computational methods.
Electrical networks & energy management
Planning and control of electrical networks, with optimization and predictive modelling for energy use, scheduling, and resource management.
Information engineering
Signal processing, multivariate time-series modelling, anomaly detection, and the implementation of quantum-classical computational systems.
Satellite constellations
Constellation design, connectivity, and coordination as large-scale optimization problems at the intersection of satellite systems and communication networks.