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.
