Monit Sharma
Research Engineer · Quantum & AI

Monit
Sharma

Building quantum algorithms and AI systems across optimization, simulation, scientific discovery, agents, and language-model workflows — from Shor's algorithm and quantum differential-equation solvers to hybrid quantum–classical software.

12
Publications
4+
Years in Quantum
AI + QC
Agents · LLMs · Quantum
01 About

Working across quantum computing, quantum algorithms, and AI systems.

I am a Research Engineer at Singapore Management University working across quantum computing, quantum algorithms, and AI. My work spans hybrid quantum-classical optimization, Shor-style factoring experiments, quantum simulation and differential-equation solvers, LLM-guided research loops, AI agents, and scientific software systems.

AQuantum algorithms — optimization, factoring, simulation, differential equations, and resource-aware execution on near-term hardware.
BAI systems — language-model workflows, research agents, tool use, and LLM-guided experimentation for scientific discovery.
CScientific software — Python, C/C++, JavaScript/TypeScript, Java, CUDA, JAX, PyTorch, and high-performance benchmarking pipelines.
DQuantum tooling — Qiskit, PennyLane, Cirq, D-Wave workflows, tensor-network simulators, and open-source teaching materials.
02 Recent News
Jul 2026🎉 AutoQResearch accepted as a technical paper at IEEE QCE26 Quantum–GenAI Co-Design & Discovery (QGDD).
Oct 2025🎉 Two papers accepted at the AAAI 2026 Workshop on Quantum Computing — Hybrid Learning for CVRP and Learning-Based Graph Shrinking.
Sep 2024📄 New papers accepted as IEEE QCE24 Quantum Applications Technical Papers on Quantum Relaxation and Newsvendor Optimization.
Jan 2024🎤 Presented a poster on Quantum Newsvendor Optimization at QIP 2024.
03 Experience
2023 — PresentSchool of Computing & Information Systems, Singapore Management University

Research Engineer

Singapore
  • Lead quantum algorithm and AI research spanning optimization, LLM-guided policy search, agents, and scientific discovery workflows.
  • Build benchmarking infrastructure comparing quantum, tensor-network, GPU/TPU, and classical solvers on enterprise-scale workloads.
  • Develop open-source quantum and AI education through the Quantum Classroom, including Qiskit, quantum algorithms, language-model, and programming tracks.
2022 — 2023TATA Consultancy Services

Research & Development Engineer

Mumbai, India
  • Deployed D-Wave quantum annealing workflows for large-scale vehicle routing (200 nodes) and industrial optimization workloads.
  • Engineered qubit-efficient mappings and production software pipelines for constrained quantum hardware.
2021 — 2022IISER Mohali

M.S. Thesis Researcher

Mohali, India
  • Investigated high-energy physics simulation, quantum machine learning, and NISQ-era circuit design (Advisor: Dr. Satyajit Jena).
  • Demonstrated superior accuracy using single-qubit data re-uploading strategies over multi-qubit baselines.
04 Software
Open-source package · Apple Silicon quantum simulation

MettleQ

I worked on MettleQ, a trustworthy local quantum-circuit simulation package for Apple Silicon. It integrates with Qiskit and PennyLane, exposes exact statevector and matrix-product-state simulation paths, and uses MLX/Metal execution with measured dispatch policies and reproducible benchmark evidence.

0.2.0
Package version
386
Tests reported
29
SDK tutorials
MIT
License
MettleQ SDK CPU GPU crossover benchmark plot
Latest radix-16 idle SDK CPU/GPU crossover benchmark on Apple M3 Pro, depth-3 full-state circuits.

Selected results

Ratio above 1.0x means MettleQ GPU was faster. In this latest run, MettleQ crosses PennyLane Lightning at 16 qubits and Qiskit Aer at 20 qubits.

QubitsQiskit CPU/GPUPennyLane CPU/GPU
201.327x11.097x
244.554x18.713x
292.739x16.005x
Local-first AI research lab · Agents and foundation models

The Atelier Lab

I also built The Atelier Lab, a local AI research laboratory for reproducing modern AI systems from first principles. It combines a foundation-model track with an autonomous local agent that runs on a single MacBook using Ollama, local embeddings, RAG, durable memory, and test-verified build mode.

0-7
Agent phases complete
41
Eval tasks
73M
Nanochat baseline
286M
Scale-up model
The Atelier Lab foundation model scaling plot comparing throughput and validation loss for 73M and 286M nanochat models on Apple Silicon
Foundation-model scaling run on Apple Silicon: the larger local model trades throughput for lower validation loss.

Local foundation track

Baseline training measurements on an Apple M3 Pro, alongside a local agent stack for RAG, memory, code repair, and test-verified edits.

ModelThroughputRun costLoss
73.5M~18.7k tok/s~1.2 h1.1664 BPB
286.2M~4.47k tok/s~5.1 h1.0954 BPB
05 Publications
2026
AutoQResearch: LLM-Guided Closed-Loop Policy Search for Adaptive Variational Quantum Optimization
with H. C. Lau · Quantum-GenAI co-design · arXiv:2604.24283
  • Introduced an LLM-guided experimentation loop that searches adaptive solver-control policies for variational quantum optimization.
  • Evaluated on MIS and CVRP, showing that staged confirmation is essential to avoid proxy-evaluation failures.
IEEE QCE26 Quantum–GenAI Co-Design & Discovery (QGDD) Technical Paper
2026
Learning-Based Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems
with H. C. Lau · Learning-guided graph shrinking for quantum optimization
  • Introduced a learning-based graph shrinking approach for reducing constrained combinatorial optimization instances.
  • Targets hardware-limited quantum optimization workflows while preserving problem structure for downstream solution recovery.
Springer QC+AI 2026 · Communications in Computer and Information Science 2872
2026
Hybrid Learning and Optimization Methods for Solving Capacitated Vehicle Routing Problem
with H. C. Lau · Hybrid RL + Quantum CVRP · Springer QC+AI 2026
  • 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.
Springer QC+AI 2026 · Communications in Computer and Information Science 2872
2024
Quantum Enhanced Simulation-Based Optimization for Newsvendor Problems
with H. C. Lau & R. Raymond
  • 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.
IEEE Xplore · IEEE QCE24 Quantum Applications Technical Paper · Poster at QIP 2024
2024
Quantum Relaxation for Solving Multiple Knapsack Problems
with Y. Jin, H. C. Lau & R. Raymond
  • 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.
IEEE Xplore · IEEE QCE24 Quantum Applications Technical Paper
2026
From Circuits to Hardware: Benchmarking Standard and Qubit-Efficient Quantum Optimization on Real Hardware
with H. C. Lau · Quantum hardware benchmarking for hard combinatorial optimization · arXiv:2607.11637
  • Benchmarked gate-based quantum optimization across MDKP, MIS, QAP, and MSP on IBM Heron processors under a common protocol.
  • Compared VQE, CVaR-VQE, QAOA variants, PCE, and QRAO, identifying fidelity limits and noise-dominated execution regimes.
2026
Securing the Flow: Maritime Energy Resilience under Correlated and Decision-Dependent Disruptions
with H. C. Lau · Maritime energy resilience · arXiv:2605.11990
  • Developed a two-stage stochastic multi-commodity flow model for resilient energy supply under correlated chokepoint disruptions.
  • Calibrated to Indian maritime energy imports, identifying LPG exposure and structural joint-failure resilience.
2026
Diagnosing Simulation and Hardware Barriers to Cross-Size Transfer in Equivariant Quantum Reinforcement Learning
with H. C. Lau · Equivariant quantum reinforcement learning transfer diagnostics · arXiv:2510.14533
  • Diagnosed cross-size transfer for EQC reinforcement-learning policies across simulators, noisy execution, and hardware.
  • Identified finite-shot, circuit-depth, and hardware-noise barriers that limit scaling beyond validated transfer regimes.
2026
Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum Execution
with H. C. Lau · Quantum CVRP decomposition · arXiv:2604.22194
  • Converted CVRP into bounded-width quantum subproblems using Lagrangian relaxation and per-vehicle knapsack QUBOs.
  • Combined learned multiplier updates with hardware-aware execution policies for noisy, heterogeneous quantum resources.
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.
2025
A Comparative Study of Quantum Optimization Techniques for Combinatorial 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.
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.
06 Writing
N° 01 · 2026

When Cooking Gas Becomes a National Security Problem

A non-technical walkthrough of maritime energy resilience, chokepoint disruptions, and stochastic optimization for national supply networks.

Energy SecurityMaritimeOptimization
Read on Medium ↗
N° 02 · 2026

The Key to the Quantum Garden

India's 1,000 km quantum communication demonstration, what QKD changes, and why quantum security is becoming infrastructure.

QKDQuantum SecurityIndia
Read on Medium ↗
N° 03 · 2026

To You, 10,000 Qubits From Now

A look at ambitious new resource estimates for quantum factoring and what they imply for Shor's algorithm at cryptographic scale.

FactoringCryptographyQubits
Read on Medium ↗
N° 04 · 2026

The Fault in Our Qubits

What it would actually take to break RSA with a quantum computer, from hardware assumptions to fault-tolerant overheads.

Quantum HardwareRSAShor
Read on Medium ↗
N° 05 · 2026

The Emperor Has No Factors

An honest-metrics tour through published Shor's algorithm results and the classical post-processing baselines behind them.

ShorFactoringBenchmarks
Read on Medium ↗
N° 06 · 2026

Sometimes It's Too Slow... for Shor!!

A hands-on deep dive into running Shor's factoring algorithm on IBM quantum hardware and the limits visible today.

ShorHardwareQPE
Read on Medium ↗
N° 07 · 2025

Solving the Multi-Dimensional Knapsack Problem

Exploring the complexity of the MDKP and how quantum algorithms attempt to tackle this constrained optimization challenge.

QuantumKnapsackOptimization
Read on Medium ↗
N° 08 · 2025

Solving the Maximum Independent Set Problem

A deep dive into the MIS problem, its importance in network analysis, and the potential of quantum approaches.

QuantumGraph TheoryOptimization
Read on Medium ↗
N° 09 · 2025

Solving the Quadratic Assignment Problem

Analyzing the QAP, known for its extreme computational difficulty, and benchmarking quantum solvers against it.

QuantumQAPOptimization
Read on Medium ↗
07 Education
June 2022

M.S., Physics & Data Science

Indian Institute of Science Education and Research, Mohali
June 2020

B.S., Physics & Data Science

Indian Institute of Science Education and Research, Mohali
08 Contact

Let's talk quantum
& AI systems. Say hello ↗

Open to research collaborations, talks, and hard problems across quantum computing, algorithms, agents, and scientific AI.