Nadeem Khan

Quantum Computing engineer specialising in FPGA-accelerated quantum simulation and Quantum Error Correction (QEC), with 3+ years of experience spanning density matrix simulation, real-time syndrome decoding, and hybrid quantum-classical systems. Demonstrated expertise in deploying custom HDL and C++ kernels on Xilinx Alveo u55c for QEC and QML workloads, achieving up to 5.75× speedup over CPU baselines. QEC/FPGA engineer specializing in syndrome-processing and decoder implementation for hardware-accelerated pipelines, with 3+ years delivering FPGA-deployed quantum simulation/QEC infrastructure and verification-ready reference models/test vectors.

Senior Application Developer - CDAC Noida, MeitY - Noida

(2025-01)

• Deployed FPGA-based state-vector simulator (30 qubits) and density matrix simulator (12 qubits) on Xilinx Alveo u55c (AMD) using HDL and C++ kernels, achieving acceleration over GPU/CPU up to 10 qubits.
• Designed Kraus-operator-based noise frameworks to enable accurate modelling of quantum communication channels for QKD applications.
• Developed a LUT decoder to analyse quantum channel noise and its impact on secure key generation, secret key fraction, and protocol abort probability.
• Driving integration of simulators into the Quniverse platform, combining FPGA, GPU, vector, and QPU backends.
• Optimised simulation kernels and added an FPGA backend for Qiskit 1.0.2.

Product Research Engineer - Qkrishi

(2024-08 - 2024-09)

• Automated custom quantum feature maps via AutoQSVC to optimise parameters for mule account detection in banking.
• Supported integration of fraud detection and mule account detection solutions for a leading Indian bank.
• Led ideation for a quantum-inspired node-level regression solution in battery materials science for the BMW Quantum Challenge.
• Managed end-to-end product research workflows, accelerating POC delivery toward MVP.

Quantum Researcher - Qubrid LLC

(2023-06 - 2024-07)

• Led a team delivering a hybrid Quantum Conditional Boltzmann Machine (QCBM) and TabGAN generative AI model, achieving 96% accuracy on single-feature data for finance clients.
• Formulated 4 POCs, advancing one to MVP phase; generated synthetic training data via quantum simulation on NVIDIA GPUs.
• Integrated OQC, IonQ, and QuEra quantum hardware into the Qubrid platform; supervised CuQuantum, CUDA-Q, NEC GPU, and Oracle ARM chip integration.
• Led a cross-functional team of 15 DevOps engineers and 10 researchers; built the Qubrid AI platform testing community.
• Developed synthetically generated quantum circuit noise datasets for noise mitigation software as part of the NVIDIA Inception programme.
• Provided research consultation and POC demonstrations to secure client project approvals; mentored faculty in quantum image processing for healthcare.

Senior Researcher - Qkrishi

(2022-10 - 2023-05)

• Conducted research on synthetic data generation using QGANs, credit risk analysis, and portfolio optimisation.
• Performed quantum state tomography and evaluated data encoding efficiency for quantum computing algorithms.
• Represented Qkrishi at hackathons, winning Runner-Up at Quant-A-Maze (NMIT, Bengaluru) with a cash prize of ₹40,000.

Research Assistant - CQST, IIIT Hyderabad - Hyderabad

(2022-04 - 2022-08)

• Assisted research on quantum isometric evolution of quantum states, analysing separability spectrum problems.
• Applied DNN/CNN models to study and classify quantum states.

Thesis Researcher - IIT Delhi - Delhi

(2020-12 - 2021-11)

• Demonstrated a faster, more efficient optical tweezer atom arrangement using a dynamic hologram-generation algorithm via SLM, achieving a 98% trap rate for cold atom qubits in target configurations.
• Simulated a magneto-optical trap (MOT) experimental setup and designed faster atomic shuttling protocols.

M.Sc. - Physics (Photonics) - Jamia Millia Islamia, New Delhi (2019 - 2021)

B.Sc. - Computer Science, Physics & Mathematics - BN University, Udaipur (2016 - 2019)