Monit Sharma

Research Engineer · Singapore Management University

Quantum Computing, AI & Accelerated Optimisation

Quantum algorithms and machine learning, hybrid quantum-classical optimisation, and large-scale GPU/TPU/parallel programming for multi-domain research initiatives.

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Expertise

Quantum systems, AI, accelerated computing.

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Experience

Industry-scale optimisation and R&D leadership.

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Research Publications

Peer-reviewed work on hybrid quantum methods.

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Open-Source & Teaching

Curricula, toolkits, and community outreach.

Table of Contents

• Expertise
• Experience
• Research Publications
  • Hybrid Learning and Optimization methods for solving Capacitated Vehicle Routing Problem
  • Cutting Slack Quantum Optimization with Slack Free Methods for Combinatorial Benchmarks
  • Adaptive Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems
  • A Comparative Study of Quantum Optimization Techniques for Solving Combinatorial Optimization Benchmark Problems
  • Quantum Monte Carlo Methods for Newsvendor Problem with Multiple Unreliable Suppliers
  • Quantum Enhanced Simulation-Based Optimization for Newsvendor Problems
  • Quantum Relaxation for Solving Multiple Knapsack Problems
• Education
• Open-Source & Teaching
  • Quantum Classroom Platform
  • Medium Articles & Research Explainability
  • Curated Learning Paths & Toolkits
• Media Coverage

Quantum Optimisation Systems

• Constraint-preserving mappings for routing and allocation on annealing and gate-based hardware.
• Variational circuit co-design with classical solvers to balance noise and solution quality.
• Hardware-in-the-loop validation emphasising robustness, interpretability, and resource-aware benchmarking.

AI & Reinforcement Learning

• Reinforcement and imitation learning policies that adaptively steer hybrid optimisation pipelines.
• Generative and sequence models for demand profiling, scenario generation, and fast approximations.
• LLM-driven knowledge distillation to translate research insights into operational guidance.

Accelerated Computing & Infrastructure

• GPU/TPU kernel optimisation for tensor networks, simulation, and large-batch training.
• Reproducible HPC workflows across clusters and cloud accelerators with automated telemetry.
• Production-grade engineering: containerised deployments, CI, and rigorous validation pipelines.

Experience

Research Engineer

Jan 2023 – Present

School of Computing & Information Systems, Singapore Management University

Singapore

• Leads hybrid optimisation programmes spanning capacitated routing, multi-knapsack planning, and stochastic inventory control across quantum, classical, and reinforcement learning paradigms.
• Builds benchmarking infrastructure comparing variational quantum algorithms, tensor-network relaxations, and classical heuristics on enterprise-scale workloads.
• Designs curriculum and outreach for the Quantum Classroom initiative through workshops, technical blogs, and open-source tooling.

Research & Development Engineer

2022

TATA Consultancy Services

Mumbai, India

• Deployed quantum annealing workflows on D-Wave hardware to solve vehicle routing across 200 customers and 25 vehicles, reducing cost and latency.
• Developed qubit-efficient mappings for mixed-integer supply-chain models, enabling quantum formulations within current hardware budgets.

M.S. Thesis Researcher

2021 – 2022

Indian Institute of Science Education and Research Mohali

Mohali, India

• Collaborated with Dr. Satyajit Jena to investigate intersections between high-energy physics simulations and near-term quantum computing.
• Demonstrated that data re-uploading strategies achieve higher accuracy with single-qubit architectures than legacy approaches requiring larger qubit registers.

Research Publications

2025

Hybrid Learning and Optimization methods for solving Capacitated Vehicle Routing Problem

with H. C. Lau · Hybrid RL + Quantum CVRP · arXiv:2509.15262

• 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.

Publication · Code

2025

Cutting Slack: Quantum Optimization with Slack-Free Methods for Combinatorial Benchmarks

with H. C. Lau · Slack-free QUBO formulations · arXiv:2507.12159

• 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.

Publication · Code

2025

Adaptive Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems

with H. C. Lau · Constraint-aware graph compression · arXiv:2506.14250

• 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.

Publication

2025

A Comparative Study of Quantum Optimization Techniques for Solving Combinatorial Optimization Benchmark Problems

with H. C. Lau · Standardised benchmarking suite · arXiv:2503.12121

• 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.

Publication · Algorithms · Benchmarks

2024

Quantum Monte Carlo Methods for Newsvendor Problem with Multiple Unreliable Suppliers

with H. C. Lau · Risk-aware inventory analytics · arXiv:2409.07183

• 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.

Publication

2023

Quantum Enhanced Simulation-Based Optimization for Newsvendor Problems

with H. C. Lau & R. Raymond

IEEE QCE 2024

• 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.

Publication

2023

Quantum Relaxation for Solving Multiple Knapsack Problems

with Y. Jin, H. C. Lau & R. Raymond

IEEE QCE 2024

• 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.

Publication

Education

M.S., Physics and Data Science

Indian Institute of Science Education and Research Mohali · June 2022

B.S., Physics and Data Science

Indian Institute of Science Education and Research Mohali · June 2020

Open-Source & Teaching

Quantum Classroom Platform

A living curriculum that aggregates lecture notes, exercises, and community contributions for learning quantum computing.

Visit Quantum Classroom

Medium Articles & Research Explainability

In-depth explainers that translate recent quantum optimisation papers, accompanied by runnable notebooks and code artefacts.

Read the series

Curated Learning Paths & Toolkits

• Learn-Quantum-Computing-For-Free: Structured pathways covering mathematics, physics, and algorithm foundations.
• Learn-Quantum-Computing-with-Qiskit and Qiskit-Hindi-Tutorials: Hands-on labs for diverse audiences.

Media Coverage

• Celebrating Excellence: SMU Honours Four Research Staff Supporting Key Research — Read the feature
• Quantum Computing: Marathon Not a Sprint — Read the interview