JPMorgan Quantum-Inspired - Summer Associate 

United States, New York, New York 

897226072

As a 2025 Quantum Computing Applied Research Summer Associate within the Global Technology Applied Research (GTAR) division of JPMorgan Chase, you will have the unique opportunity to advance the state of the art of theory and practice of quantum algorithms. Your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, interaction with senior stakeholders and more. You will have the opportunity to advance the field of quantum-inspired and randomized algorithms and implement the developed algorithms in performant software. This role offers a unique opportunity to work in a dynamic and growing team in a fast-paced and challenging area.

Job Responsibilities:

• Solve problems innovatively with a passion for advancing the state of the art of quantum-inspired algorithms.
• Advance the field of quantum-inspired and randomized algorithms and their applications to optimization, machine learning and financial use cases.
• Implement the developed algorithms in performant software.
• Contribute to JPMC’s IP by pursuing necessary protections of generated IP.

Required qualifications, capabilities, and skills

• 1+ years of experience with quantum-inspired algorithms. Enrolled in a Master’s or Ph.D. degree program in math, science, engineering, computer science or related fields.
• Demonstrated research ability in quantum-inspired algorithms or related fields
• Experience in scientific technical writing.
• Proficiency in Python or C/C++.
• Experience developing performant codes.
• Strong communication skills and the ability to present findings to a non-technical audience.

Preferred qualifications, capabilities, and skills

• Experience in Randomized algorithms for big-data matrix operations for machine learning (e.g., matrix sketching techniques of least-square regression, locality sensitive hashing techniques for clustering etc).
• Experience in Randomized algorithms for accelerating optimization (e.g., randomized stochastic gradient descent methods).
• Experience in Tensor networks for machine learning and discrete optimization.
• Experience in Marko Chain Monte Carlo or Path Integral Monte-Carlo techniques for discrete Ising solver type optimization
• Experience in accelerating continuous optimization using Nesterov or related acceleration techniques.
• Experience in High-performance computing (e.g., MPI, experience running computational tasks on 100+ nodes).