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Abhay Suresh

@abhaysuresh

Quantum computing researcher training in quantum technology, applying QUBO/QAOA and quantum-inspired optimization to allocation problems.

India

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What I'm looking for

I’m looking for a research-focused role where I can apply quantum and quantum-inspired optimization (QUBO/QAOA) and quantum ML to real problems, benchmark against classical baselines, and contribute to publishable, high-quality work in quantum technologies.

I’m a quantum technology M.Tech student at the Indian Institute of Technology, Jodhpur, focused on turning quantum and quantum-inspired methods into usable optimization and machine learning solutions. As a Junior Research Intern at QKrishi, I worked on industrial optimization problems using QUBO formulations and quantum-inspired solvers for resource allocation and planning, and I contributed toward publishable pre-print work and intellectual property in quantum computing applications.

My research also spans quantum systems modeling and quantum ML: at the University of Hyderabad I performed numerical simulation of single-photon and double-photon damping on quantum states under varying parameters, and I pursued theoretical reading on Schrodinger CatQubits. I’ve built and compared quantum-assisted and de-quantized approaches through projects like a Quantum-Assisted Leak Detection and Localization pipeline for severe class imbalance, a QPF quantum protein folding formulation using QAOA with qubit-efficient encoding, and de-quantized analysis inspired by the HHL algorithm for linear systems—while benchmarking against classical baselines. I’m motivated by rigorous experimentation, clear comparisons, and impactful research output, and I’ve secured 1st and 2nd positions at IBM Qiskit Fall Fest (IIT Jodhpur) in 2024 and 2025.

Experience

Work history, roles, and key accomplishments

Quantum-Assisted Leak Detection

Indian Institute of Technology, Jodhpur

May 2025 - May 2026 (1 year)

Built cascading anomaly detection and multiclass localization models for severe class-imbalanced leak scenarios across different leak types and pipeline network complexity. Compared classical and quantum ML approaches of similar models.

Anomaly Detection Multiclass Localization Class Imbalance Learning Leak Detection

Junior Research Intern

QKrishi

Jun 2025 - Dec 2025 (6 months)

Worked on industrial optimization problems using QUBO formulations and quantum-inspired solvers for resource allocation and planning. Worked on publishing pre-print papers and developing quantum computing IP, including a similarity-driven QUBO framework for multi-period supply chain allocation benchmarked against simulated quantum annealing and classical metaheuristics.

Quantum Inspired Solvers Resource Allocation Planning Quantum Computing Hybrid QUBO Similarity Kernel Multi Period Supply Chain Allocation Time Multiplexed Coherent Ising Machines Simulated Quantum Annealing Metaheuristic Optimization

Research Intern

University of Hyderabad

Dec 2024 - Dec 2024 (0 months)

Conducted numerical simulations of single-photon and double-photon damping on quantum states across varying parameters. Performed theoretical study related to Schrödinger cat qubits.

Single Photon Damping Double Photon Damping Quantum State Evolution Schrödinger Cat Qubits

Discrete Time Quantum Walk

Indian Institute of Information Technology, Raichur

Jan 2024 - Apr 2024 (3 months)

Modeled and simulated 1D and 2D discrete time quantum walks while varying parameters.

Discrete Time Quantum Walk 1D Quantum Walk 2D Quantum Walk

Quantum Neural Network Classification

Indian Institute of Information Technology, Raichur

Jul 2023 - Dec 2023 (5 months)

Built a binary quantum neural network classification approach using Qiskit’s adhoc dataset and reviewed its performance against other classification techniques.

Qiskit Classification

De-quantized Algorithm for Linear Systems

Indian Institute of Technology, Jodhpur

Oct 2025 - Present (9 months)

Performed theoretical and numerical analysis of a de-quantized algorithm for solving linear systems and recommendation systems, inspired by the HHL algorithm.

De Quantized Algorithms Recommendation Systems HHL Inspired Methods

QPF: Quantum Protein Folding

Indian Institute of Technology, Jodhpur

Oct 2025 - Present (9 months)

Formulated 3D protein folding as a combinatorial optimization problem using hydrophobic (HP) interactions and Miyazawa–Jernigan contact potentials while penalizing invalid structures for lattice models. Implemented QAOA for low-energy structure search with qubit-efficient encoding.

Protein Folding Combinatorial Optimization Hydrophobic (HP) Model Miyazawa–Jernigan Potentials Lattice Invalid Structure Penalization QAOA Qubit Efficient Encoding