About Me
I am a Research Engineer at Singapore Management University, bridging the gap between near-term quantum hardware and industrial applications. My work focuses on designing hybrid quantum-classical algorithms for large-scale optimization problems in logistics and supply chain management. I specialize in leveraging GPU/TPU acceleration to benchmark quantum approaches against state-of-the-art classical heuristics.
Recent News
- 2025-10 🎉 Two papers accepted at AAAI 2026 Workshop on Quantum Computing! Check out my work on Hybrid Learning for CVRP and Graph Shrinking.
- 2024-09 📄 New papers accepted at IEEE QCE 2024 on Quantum Relaxation and Newsvendor Optimization.
- 2024-01 🎤 Presented a poster on Quantum Newsvendor Optimization at QIP 2024.
Experience
Research Engineer
School of Computing & Information Systems, Singapore Management University · Singapore
- Lead hybrid optimization (Quantum, RL, Classical) for complex logistics problems like routing and inventory control.
- Built benchmarking infrastructure comparing quantum vs. tensor-network solvers on enterprise-scale workloads.
- Directed the Quantum Classroom initiative, delivering workshops and open-source tools to the community.
Research & Development Engineer
TATA Consultancy Services · Mumbai, India
- Deployed D-Wave quantum annealing workflows for large-scale vehicle routing (200 nodes), reducing fleet latency.
- Engineered qubit-efficient mappings for supply chains, enabling execution on constrained hardware.
M.S. Thesis Researcher
Indian Institute of Science Education and Research Mohali · Mohali, India
- Investigated High-Energy Physics simulations on NISQ devices (Advisor: Dr. Satyajit Jena).
- Demonstrated superior accuracy using single-qubit data re-uploading strategies over multi-qubit baselines.
Selected Publications
Learning-Based Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems
- Introduced a learning-based graph shrinking technique to reduce instance size while preserving solution structure.
- Achieved substantial qubit reductions on benchmark combinatorial instances with minimal loss in optimisation quality.
Hybrid Learning and Optimization methods for solving Capacitated Vehicle Routing Problem
- Soft Actor-Critic policies tune augmented Lagrangian penalties for both classical and quantum solvers, accelerating convergence and feasibility.
- Benchmarks across synthetic and real CVRP instances quantify runtime and solution-quality trade-offs between hybrid and classical baselines.
Quantum Enhanced Simulation-Based Optimization for Newsvendor Problems
- Employed qGANs to learn demand distributions, reducing qubit requirements with a tailored comparator.
- Expanded the newsvendor formulation to maximise profit and support broader decision scenarios.
Quantum Relaxation for Solving Multiple Knapsack Problems
- Combined Quantum Random Access Optimisation with linear relaxation to solve large-scale procurement problems.
- Showed feasibility and optimality preservation on instances exceeding 100 decision variables.
Other Publications
Transferable Equivariant Quantum Circuits for TSP: Generalization Bounds and Empirical Validation
- Leveraged permutation-equivariant quantum circuits to transfer policies from small to larger TSP instances with strong zero-shot performance.
- Derived generalization bounds that tie structural dissimilarity between training and target graphs to expected performance and validated them empirically.
Cutting Slack: Quantum Optimization with Slack-Free Methods for Combinatorial Benchmarks
- Dual ascent, bundle, and augmented Lagrangian updates enforce constraints without auxiliary slack variables, reducing qubit counts.
- Validated on TSP, MDKP, and MIS using both simulators and hardware executions.
Adaptive Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems
- Introduced adaptive graph coarsening with verification to shrink QUBO instances while preserving feasibility.
- Delivered 40–80% qubit reductions for MDKP, MIS, and QAP, with minimal quality loss.
A Comparative Study of Quantum Optimization Techniques for Solving Combinatorial Optimization Benchmark Problems
- Evaluated VQE, CVaR-VQE, QAOA variants, and compression techniques such as PCE and QRAO across NP-hard benchmarks.
- Delivered actionable guidance on feasibility gaps, scaling behaviour, and resource allocation for hybrid pipelines.
Quantum Monte Carlo Methods for Newsvendor Problem with Multiple Unreliable Suppliers
- Integrated decision-maker risk profiles into a quantum Monte Carlo framework for multi-supplier newsvendor problems.
- Secured near-quadratic speed-ups in expectation estimation via Quantum Amplitude Estimation.
Education
M.S., Physics and Data Science
Indian Institute of Science Education and Research Mohali
B.S., Physics and Data Science
Indian Institute of Science Education and Research Mohali