LEARN
Learning with physical systems
Quantum reservoir computing is the center of gravity: using fixed or structured quantum dynamics as temporal feature maps, then asking when memory, representation, measurement and readout design actually help.
ρₜ → hₜᑫ → π*
Research Associate at Fractal Analytics’ QuantumAI Lab working on quantum machine learning, quantum reservoir computing, scientific machine learning, quantum control, temporal and dynamical learning, and hybrid quantum-classical systems. I also contribute to quantum-technology policy and ecosystem research through QETCI, with an emerging finance lens around uncertainty, resource allocation, deep technology, and decision-making.
India
Research Associate — QuantumAI Lab, Fractal Analytics
LEARN
Quantum reservoir computing is the center of gravity: using fixed or structured quantum dynamics as temporal feature maps, then asking when memory, representation, measurement and readout design actually help.
CONTROL
Physics-constrained sequence models, quantum state reconstruction, feedback control and AI-assisted architecture design, with learning mechanisms that respect the geometry and physical constraints of the systems they act on.
TRANSLATE
Quantum technologies are also infrastructure and ecosystem problems. This track studies state capabilities, value chains, supply constraints, policy design and pathways from scientific capacity to technological and economic impact.
DECIDE
An emerging layer connecting finance training with uncertainty, resource allocation, optimization, technology strategy and the economics of deep technology.
This site includes public, accepted, published, presented, and openly available work only. Confidential and under-review research is intentionally excluded.