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Quantum Computing Research Scientist

Nexus Innovations
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
20 Mei 2026
Deadline
20 Mei 2027

Job Description

Join Nexus Innovations at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking a visionary Quantum Computing Research Scientist to develop next-generation algorithms and systems that will redefine computational boundaries. In this role, you'll collaborate with Nobel laureates and industry pioneers in our state-of-the-art San Francisco R&D lab, working on projects that will shape the future of artificial intelligence, cryptography, and materials science. Our team operates at the intersection of theoretical physics and practical application, offering unparalleled resources and mentorship for quantum research excellence.

Responsibilities

  • Design and implement quantum algorithms for complex optimization problems in AI and logistics
  • Develop error-correction protocols for fault-tolerant quantum systems
  • Lead experimental validation of quantum supremacy benchmarks on our 1000-qubit processor
  • Collaborate with hardware teams to co-design quantum-classical hybrid architectures
  • Publish breakthrough research in top-tier journals and industry whitepapers
  • Mentor junior researchers and contribute to our quantum education initiatives

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science with 3+ years research experience
  • Expertise in quantum circuit design and quantum machine learning frameworks
  • Published work in Nature/Science or equivalent quantum computing journals
  • Proficiency with quantum programming languages (Q#, Qiskit, Cirq)
  • Deep understanding of quantum decoherence mitigation techniques
  • Experience with high-performance computing clusters and quantum simulators
  • Demonstrated ability to translate theoretical concepts into practical implementations

Required Skills

Quantum Computing Quantum Algorithms Quantum Error Correction Q# Qiskit Machine Learning High-Performance Computing Theoretical Physics

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