I'm a quantum computing researcher and engineer working at the intersection of quantum tensor networks, reinforcement learning, and quantitative finance. I combine rigorous theoretical foundations with production-focused engineering — building systems that scale from research to real-world impact across automotive, fintech, and deep tech domains.

• PhD in Quantum Computing at IIT Bombay — exploring tensor network architectures and quantum-enhanced machine learning
• Master's in Finance (MScFE) from WorldQuant University — quantitative finance, derivative pricing, stochastic modeling
• Co-Founder & Quantum Director at Rune Technology (Portugal) — building quantum-enhanced AI systems for systematic trading
• Quantum Lead at Hero MotoCorp — applying tensor networks and physics-informed AI to battery optimization and thermal modeling

• Quantum Computing: Tensor networks (MPS, MPO, Tucker, CP decomposition), quantum simulation, physics-informed neural networks
• Generative AI & LLMs: Large language models, prompt engineering, AI-powered applications and infrastructure
• Reinforcement Learning: Macro-manager architectures, curriculum learning, multi-objective optimization
• Quantitative Finance: Long-short equity strategies, factor modeling, regime detection, crisis prediction
• Hardware Optimization: Tensor network methods for battery busbar design, thermal modeling, and automotive system optimization
• Machine Learning Systems: Production ML pipelines, signal generation, backtesting infrastructure, model validation

• Developed quantum-enhanced signal architectures with 100% crisis detection across 12+ major financial events (2000–2024)
• Built full-stack ML+RL trading systems with comprehensive backtesting and regime-aware portfolio optimization
• Applied tensor network methods to automotive battery busbar design optimization and thermal simulation
• Designed automated cloud pipelines (GCP) for quantum computation, ML inference, and trade generation
• Leveraged generative AI and LLMs for accelerating research, development, and system integration workflows
• Navigated cross-border fintech scaling: funding, investor relations, regulatory compliance (Portugal ↔ India)

• Bridging quantum computing theory and real-world applications in finance, automotive, and deep tech
• Rigorous scientific thinking: first-principles analysis, empirical validation, epistemic precision
• Production-grade systems: clean architecture, reproducibility, monitoring, and observability
• Cross-domain problem-solving: applying quantum/tensor methods to classical engineering challenges
• Communication: translating complex technical work into clear, actionable insights
• Mentorship: helping others develop strong technical foundations in quantum computing and scientific computing

• Collaborations on quantum machine learning, quantum simulation, physics-informed AI, and tensor network applications
• Technical discussions on tensor network architectures, RL theory, quantitative finance methodology, or battery optimization
• Opportunities at the intersection of deep tech, fintech, and automotive innovation
• Mentoring on quantum computing fundamentals, scientific computing best practices, or navigating early-stage deep tech ventures

• Scaling tensor network methods beyond simulation into real-world engineering and financial systems
• Regime detection and crisis prediction in complex adaptive systems
• Multi-objective RL with constraints (sector neutrality, leverage control, robustness)
• Physics-informed approaches to machine learning and signal processing
• Cross-border scaling of technical ventures (regulatory, operational, talent)

• Twitter: @quantum_mishra
• LinkedIn: Abhigyan Mishra
• Company: Rune Technology

Note: Much of my recent work involves proprietary systems and confidential research. I'm happy to discuss methodology, approach, and lessons learned within appropriate boundaries.