Monit Sharma
Hello there! I am Monit, a researcher passionate about quantum computing and its applications.
Research Interests
Quantum Physics, Quantum Computation, Quantum Optimization and Algorithms
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)
Work experience
Research Engineer @ School of Computing and Information Systems, Singapore Management University (January 2023 - present)
- 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
Work
Quantum Monte Carlo methods for Newsvendor problem with Multiple Unreliable Suppliers
- In the post-pandemic world, manufacturing enterprises face increasing uncertainties, especially with vulnerabilities in global supply chains. Although supply chain management has been extensively studied, the critical influence of decision-makers (DMs) in these systems remains underexplored.
- This study studies the inventory management problem under risk using the newsvendor model by incorporating DMs risk preferences. By employing the Quantum Monte Carlo (QMC) combined with Quantum Amplitude Estimation (QAE) algorithm, the estimation of probabilities or expectation values can be done more efficiently.
- This offers near-quadratic speedup compared to classical Monte Carlo methods. Our findings illuminate the intricate relationship between risk-aware decision-making and inventory management, providing essential insights for enhancing supply chain resilience and adaptability in uncertain conditions
Quantum Enhanced Simulation Based Optimization for Newsvendor Problems
- We utilize the maximum profit formulation for the Newsvendor Problem, which has broader applicability compared to the minimal loss formulation commonly found in the literature.
- Our approach involves an unknown demand distribution, where a function may not precisely capture the demand in such settings, to address this, we employ Quantum Generative Adversarial Networks (qGANs) to load the unknown demand distribution, thereby creating a more realistic scenario.
- We improve the simulation-based optimization method in by introducing a new comparison operator, thereby reducing the number of qubits needed in the circuit.
Quantum Relaxation for Solving Multiple Knapsack Problems
- Explored the effectiveness of QRAO in addressing the inherent complexities of constrained supply chain problems, by solving a Multiple Knapsack Problem (MKP) and comparing it with the well-studied QAOA approach.
- Scaled up a real-world multiple knapsack-based Risk-Aware Procurement Optimization problem involving ≥ 100 variables and demonstrating the prospect of combining QRAO with a classical method in operations research, namely Linear Relaxation (LR)
Quantum Education Work
Quantum Classroom Website
Learning Material
Made a website that contains free resources to learn quantum computing, some written by me, and some by the vast quantum computing community. For more details, please Visit Website.
Medium Blogs
Education Work
I write about recent quantum papers that I read, and try to code them out. Also explain some coding tutorials and exercises.
View Blogs
Publications
-
Monit Sharma, and Hoong Chuin Lau. “Quantum Monte Carlo methods for Newsvendor problem with Multiple Unreliable Suppliers.” arXiv preprint arXiv:2409.07183 (2024).
-
Monit Sharma,Yan Jin, Hoong Chuin Lau and Rudy Raymond. Quantum Relaxation for Solving Multiple Knapsack Problems, e-Print: 2404.19474 [quant-ph], [IEEE QCE2024 Quantum Week Conference presented]
-
Monit Sharma, Hoong Chuin Lau and Rudy Raymond. Quantum-Enhanced Simulation-Based Optimization for Newsvendor Problems, e-Print: 2403.17389 [quant-ph] [QIP2024 Taiwan Poster presented], [IEEE QCE2024 Quantum Week Conference presented]