Monit Sharma
Research Engineer @ Singapore Management University
Things I Pretend to Be an Expert In
Table of Contents
- Research Interests
- Education
- Work Experience
- Research Projects
- 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
- Quantum Education Work
- Publications
- Media Coverage
Research Interests
Exploring the intersection of quantum computing, optimization, and algorithms to tackle complex computational challenges. My interests include:
Quantum Computing & Optimization
- Quantum Algorithms
- Hybrid Quantum-Classical Optimization
- Quantum Machine Learning
- Quantum-Assisted Constraint Programming
- Quantum Error Mitigation & Noise-Aware Computing
Classical & Constraint Optimization
- Constraint Programming (CP)
- Mixed-Integer Programming (MIP)
- Heuristics & Metaheuristics (Genetic Algorithms, Simulated Annealing)
- Combinatorial Optimization
- Integer Linear Programming (ILP)
- Large-Scale Supply Chain & Logistics Optimization
- Applications in Finance & Risk Management
Applied & Theoretical Interests
- Quantum Applications in Supply Chain & Logistics
- Benchmarking Quantum vs. Classical Solvers
- Quantum Advantage & Near-Term Quantum Computing
- Complexity Theory & Hardness of Approximation
GPU & HPC Integration
- Quantum-Classical Hybrid Computing on HPC Clusters
- GPU-Accelerated Tensor Networks for Quantum Simulations
- Parallel Computing for Large-Scale Optimization Problems
- CUDA and TensorFlow Quantum for Quantum Machine Learning
- Accelerated Quantum Circuit Simulation on Classical Hardware
Education
| Degree | Field | Institution | Date |
|---|---|---|---|
| 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 |
Work Experience
Research Engineer @ School of Computing and Information Systems, Singapore Management University
January 2023 – Present
- 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
Research Projects
A Comparative Study of Quantum Optimization Techniques for Solving Combinatorial Optimization Benchmark Problems
Publication: arXiv:2503.12121
- Overview: Quantum optimization holds promise for addressing classically intractable combinatorial problems, yet a standardized benchmarking framework is still lacking.
- Framework: Introduces a comprehensive approach to evaluate quantum optimization techniques against NP-hard problems such as the Multi-Dimensional Knapsack Problem (MDKP), Maximum Independent Set (MIS), Quadratic Assignment Problem (QAP), and Market Share Problem (MSP).
- Techniques: Benchmarks gate-based approaches like the Variational Quantum Eigensolver (VQE) and its CVaR variant, alongside advanced methods such as the Quantum Approximate Optimization Algorithm (QAOA) and its extensions.
- Additional Methods: Incorporates qubit compression techniques like Pauli Correlation Encoding (PCE) and Quantum Random Access Optimization (QRAO).
- Results: Provides insights into feasibility, optimality gaps, and scalability based on experiments with simulated quantum environments and classical solvers.
Quantum Monte Carlo Methods for Newsvendor Problem with Multiple Unreliable Suppliers
Publication: arXiv:2409.07183
- Overview: Addresses the challenges in inventory management amid increased supply chain uncertainties in a post-pandemic world.
- Focus: Integrates decision-makers’ risk preferences into the classic newsvendor model.
- Technique: Utilizes Quantum Monte Carlo (QMC) combined with Quantum Amplitude Estimation (QAE) for efficient probability and expectation value estimation—offering a near-quadratic speedup over classical methods.
- Impact: Illuminates the link between risk-aware decision-making and inventory management, contributing to enhanced supply chain resilience.
Quantum Enhanced Simulation-Based Optimization for Newsvendor Problems
Publication: IEEE Xplore (Poster Presented at QIP2023)
- Overview: Utilizes a maximum profit formulation for the Newsvendor Problem to broaden its applicability beyond traditional minimal loss approaches.
- Innovation: Employs Quantum Generative Adversarial Networks (qGANs) to model unknown demand distributions, creating more realistic scenarios.
- Improvement: Introduces a new comparison operator that reduces the number of qubits required in the simulation circuit.
Quantum Relaxation for Solving Multiple Knapsack Problems
Publication: IEEE Xplore
- Overview: Investigates the effectiveness of Quantum Random Access Optimization (QRAO) in tackling complex constrained supply chain issues.
- Application: Demonstrates the solution of a Multiple Knapsack Problem (MKP) using QRAO combined with classical methods like Linear Relaxation (LR) on a real-world risk-aware procurement optimization problem involving over 100 variables.
- Insight: Highlights the potential of integrating quantum relaxation techniques with traditional optimization methods to enhance problem-solving capabilities.
Code & Repositories
- SMU-Quantum
Explore our collaborative projects on quantum computing at SMU.
GitHub Repository
Quantum Education Work
Quantum Classroom Website
Learning Material
A website offering free resources to learn quantum computing, featuring both original content and contributions from the quantum computing community.
Visit Website
Medium Blogs
Educational Outreach
Regular blogs covering recent quantum computing papers, coding tutorials, and practical exercises.
View Blogs
Publications
-
M. Sharma and H. C. Lau
A Comparative Study of Quantum Optimization Techniques for Solving Combinatorial Optimization Benchmark Problems
arXiv preprint, arXiv:2503.12121 (2025) -
M. Sharma and H. C. Lau
Quantum Monte Carlo Methods for Newsvendor Problem with Multiple Unreliable Suppliers
arXiv preprint, arXiv:2409.07183 (2024) -
M. Sharma, Y. Jin, H. C. Lau, and R. Raymond
Quantum Relaxation for Solving Multiple Knapsack Problems
Proceedings of the 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, pp. 692–698.
DOI: 10.1109/QCE60285.2024.00086 -
M. Sharma, H. C. Lau, and R. Raymond
Quantum Enhanced Simulation-Based Optimization for Newsvendor Problems
Proceedings of the 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, pp. 457–468.
DOI: 10.1109/QCE60285.2024.00060
Media Coverage
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Celebrating Excellence: SMU Honours Four Research Staff Supporting Key Research
Read Article -
Quantum Computing: Marathon Not a Sprint
Read Article