Senior Quantum Researcher (Optimization)

Description

As a Senior Quantum Researcher focusing on optimization, you will develop cutting edge quantum-classical hybrid algorithms for combinatorial optimization problems under hardware noise. You will pushing the edge of quantum optimization algorithms and work closely with strategic customers to adapt them to their applications. 

The role combines algorithm development with hands-on computational research: designing and running experiments, analyzing results, and using those findings to guide further development. You will develop new methods and improve existing ones, taking into account application requirements, classical benchmarks, noise characteristics, and the constraints of real quantum hardware. 

Responsibilities

• Develop and improve quantum and hybrid quantum-classical algorithms for combinatorial optimization. 
• Adapt and optimize Qedma’s error-reduction methods for optimization algorithms, applications, and hardware settings. 
• Design and conduct computational experiments using classical simulations and real quantum hardware to evaluate algorithm performance and guide research. 
• Analyze experimental results, assess their statistical significance, and identify opportunities for improvement through systematic comparison with classical and quantum approaches. 
• Work closely with strategic customers to understand their optimization problems, translate application requirements into research objectives, and develop tailored algorithmic solutions. 
• Take ownership of research projects, from exploring possible approaches through implementation, benchmarking, and proof-of-concept demonstrations, in collaboration with Qedma’s research and software teams. 

Requirements

• PhD in computer science, mathematics, operations research, quantum computing, physics, or a related field; or an MSc in a related field with equivalent industry experience. 
• Substantial research or industry experience in at least one of the following fields: combinatorial optimization or quantum algorithms. Experience in both is a strong advantage. 
• Familiarity with quantum information or quantum computing through research, industry experience, or relevant academic coursework. 
• Strong programming skills, preferably in Python, and experience implementing and evaluating computational methods. 
• Experience designing computational experiments and analyzing numerical or experimental data, including assessing uncertainty and drawing statistically sound conclusions. 
• Ability to organize and drive computational research projects, explore uncertain research directions, and adapt approaches based on evidence. 
• Excellent technical and scientific communication skills in English, both verbal and written, and the ability to collaborate effectively with researchers, software developers, and customers. 
  
  Advantages 
• Experience with classical algorithms, heuristics, and solvers for combinatorial optimization problems. 
• Experience implementing quantum algorithms and running experiments on quantum hardware. 
• Background in machine learning or data science, particularly involving systematic experimentation and analysis of large datasets. 
• Experience adapting research methods to practical applications or working with industry partners. 
• Experience with AI-assisted research and development workflows.