Monalisa Singh Roy

WORK HISTORY

EDUCATION

ABOUT MONALISA SINGH ROY

Quantum physicist developing quantum algorithms and quantum-inspired tensor-network methods for real-world combinatorial optimisation, with focus on finance and logistics.\"I design scalable, noise-aware methods connecting theory, emerging hardware, and business use cases. This allows you to make an informed decision on whether quantum or quantum-inspired approaches can deliver measurable advantages today while preparing for tomorrow’s hardware.\"If you are exploring optimisation challenges or want to test an idea, I would be glad to connect. DM me here or email: m••••••••@planqc.eu-I work at the intersection of quantum theory and algorithm design. I develop optimisation methods for real-world industry use cases. My focus is on translating theoretical insights into approaches that run on both classical and quantum hardware, delivering solutions that are rigorous, scalable, and noise-aware.With more than ten years in quantum many-body physics, I bring expertise in tensor networks, hybrid quantum–classical methods, large-scale simulations, and digital quantum circuit algorithms. I apply these tools to tackle combinatorial optimisation problems in finance, logistics, and mobility, where efficiency and robustness provide measurable advantages. My work helps teams test whether quantum and tensor-network approaches offer practical benefits today while also preparing for the opportunities of emerging hardware.Earlier in my career, I studied how measurements, noise, and dissipation affect entanglement and quantum phase transitions, leading to NISQ-compatible algorithms tested on IBM devices. During my PhD, I advanced state-of-the-art DMRG methods for strongly correlated systems, including cold atomic gases, frustrated magnets, and Fermionic systems designed to test for topological modes. I also developed statistical models linking first-principles theory with experimental data to explain emergent behaviour in complex systems, and worked on problems in game theory and self-organised criticality.My passion and profession combine two elements. On the one hand, I enjoy brainstorming ambitious ideas and “moonshots.” On the other, I apply algorithmic expertise to build the backend and ensure those ideas can be realised in practice for today’s problems. I love to communicate ideas and techniques in terms accessible for both technical teams and non-specialist audiences.If you are interested in testing potential quantum / optimisation ideas: how they can be applied to your problems or adapted to your hardware specs, please send a message!