Jishnu Rajendran
Affiliations. QpiAI India Pvt Ltd · Universita degli Studi di Catania.
Quantum Engineer — Device Theory
QpiAI India Pvt Ltd, Bengaluru
PhD · Universita degli Studi di Catania
Dipartimento di Fisica e Astronomia
"Ettore Majorana"
Quantum Technology Researcher & Specialist
About Me
I am a quantum technology researcher and specialist with a PhD in Quantum Technology from the Universita degli Studi di Catania, Department of Physics and Astronomy “Ettore Majorana”. Currently at QpiAI India Pvt Ltd, I work across the full stack of quantum research — from device theory and Hamiltonian modeling to quantum error correction, algorithm design, and AI-driven optimization. My work bridges theory and experiment: collaborating with experimentalists, chip layout engineers, and QEC teams to translate rigorous physics into hardware-ready solutions. I bring expertise in quantum algorithms, superconducting qubit architectures, optimal control, and the application of machine learning to quantum systems — combining analytical depth with a strong collaborative, cross-disciplinary approach.
Expertise
Quantum Computation and InformationSuperconducting Qubit Device TheoryHamiltonian Engineering & Circuit QEDFluxonium Qubits & FTF/FFF ArchitecturesQuantum Error Correction (Surface Code, Stabilizer Codes)Autonomous QEC & Hardware-Aware Co-designQuantum Optimal Control (GRAPE, Krotov, CRAB)Open Quantum Systems & Ultra-Strong CouplingQuantum Walks & Quantum AlgorithmsComputational Physics & Numerical SimulationEvolutionary AlgorithmsDeep Learning Methods in Optimization
Research Interests
My research sits at the intersection of quantum hardware and quantum error correction. I am particularly focused on superconducting qubit architectures — modeling fluxonium-based devices, engineering Hamiltonians for high-fidelity two-qubit gates, and co-designing hardware with QEC constraints in mind. I am also interested in quantum optimal control techniques that push gate fidelities toward fault-tolerant thresholds, and in applying machine learning methods to accelerate quantum process optimization and simulation.
latest posts
| Feb 18, 2025 | Ultrastrong Coupling in Quantum Computation |
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| Jul 15, 2024 | Hydra game |