We are seeking an experienced High-Performance Computing (HPC) / Quantum Software Engineer to develop, optimize, and scale advanced quantum simulation workloads. The successful candidate will be responsible for implementing, benchmarking, and scaling quantum algorithms leveraging NVIDIA’s CUDA-Q platform and cuQuantum SDK hardware-accelerated backends. This role bridges classical GPU acceleration with hybrid quantum-classical computing frameworks.
Core Responsibilities
Algorithm Implementation: Successfully program, deploy, and execute the following target quantum algorithms within the simulation workplace:
o Variational &
State-Vector: Variational Quantum Eigensolver (VQE), Shor’s Algorithm (full state-vector), and Grover’s Algorithm (full state-vector).
o Optimization &
Transforms: Quantum Approximate Optimization Algorithm (QAOA) and Quantum Fourier Transformation (QFT).
o Quantum Machine Learning (QML): Quantum Convolutional Neural Networks (QCNN) and HLL algorithms.
Hardware &
Noise Simulation: Design, construct,
and validate realistic Quantum Noise Simulations using custom noise channels and decoherence models.
Performance Tuning: Optimize circuit execution using cuQuantum SDK primitives (cuStateVec and cuTensorNet) across multi-GPU and multi-node hardware setups.
Hybrid Workflows: Build high-throughput, low-latency hybrid classical-quantum loops coupling GPU kernels with classical optimizers.
Required
Technical
Requirements
Skills &
Qualifications
Education: B-tech/Master in Computer Science/Quantum Computing/Physics, Applied Mathematics, or a related quantitative field.
Programming: Advanced proficiency in C++ and Python within Linux/HPC cluster settings.
NVIDIA Stack: Direct hands-on experience using NVIDIA CUDA-Q (formerly CUDA Quantum) and the cuQuantum SDK.
Parallel Computing: Background in GPU programming (CUDA C++/PyCUDA), multi
GPU scaling, and cluster communication protocols (MPI, NCCL).
Quantum Theory: Solid foundational knowledge of quantum gate mechanics, tensor networks, and state-vector simulation techniques.