Quantum Research Scientist
1 month ago
Quantum Computing Research Scientist
At QpiAI, we are focused on providing quantum solutions for industries including finance, logistics, pharmaceuticals, materials, telecom, automotive, and energy. We are looking for a Quantum Research Scientist to advance quantum AI integration, develop hybrid quantum-HPC systems, and build a strong foundation in quantum algorithms and libraries.
Responsibilities:
Develop quantum or quantum-inspired algorithms in QML/QAOA/VQE/tensor networks to drive real-world applications in chemistry, optimization, AI, and quantum generative AI.
Design reusable quantum libraries for NISQ-oriented solutions with error mitigation and suppression. Integrate developed libraries with high-performance QC-HPC hybrid systems with multi-level computation stacks and distributed quantum-classical workflows.
Collaborate with quantum systems software development teams to build highly optimized ISAs, AI-based quantum compilers, schedulers, distributors, and orchestrators. Optimize algorithms and systems stack for device-specific or application-specific requirements.
Apply advanced quantum error correction methods (surface, concatenated, and topological codes) to enhance fault tolerance in quantum algorithms.
Collaborate with quantum engineers and scientists to simulate, benchmark, and optimize quantum algorithms on QC-HPC systems for high-throughput applications.
Work with client R&D and business units to identify impactful quantum use cases, prototype solutions, and provide technical roadmaps for quantum-enhanced processes.
Lead in filing patents and publishing research on quantum AI, optimization, and simulation, establishing QpiAI as a leader in applied quantum computing.
Required Skills and Experience:
Proficiency in quantum machine learning methods, including QAOA, VQE, parametrized quantum circuits, HHL algorithm, kernel methods, and quantum generative models.
Hands-on experience with quantum libraries and GPU/TPU-based acceleration.
Expertise in building AI, graph ML, hybrid quantum-classical ML, energy-based models, thermodynamic computing, and quantum-inspired algorithms.
Advanced knowledge of quantum error correction techniques and design of fault-tolerant quantum algorithms.
Practical experience with quantum simulators and hardware, understanding device-specific constraints, and optimizing algorithms for noise-resilient performance.
Exposure to modern AI, LLMs, Agentic AI workflows, and generative AI is a big plus.
Exposure to ML, DL, NAS, model compression, and MLOps is a big plus.
Qualifications:
MS/Ph.D. in Quantum Computing, Physics, Applied Mathematics, or a related field, with 2+ years of research or industry experience in quantum algorithm development.
Demonstrated research contributions in quantum computing through peer-reviewed publications, patents, and collaboration on innovative projects.
Strong technical communication skills for effective collaboration with software engineers, data scientists, and quantum algorithm developers in a dynamic, fast-paced environment.
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