Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work with Global Technology Applied Research (GTAR) division of JPMorgan Chase & Co.. The goal of GTAR is to design and conduct research across multiple frontier technologies, to enable novel discoveries and inventions, and to inform and develop next-generation solutions for the firm’s clients and businesses.

 

As a 2025 Quantum Computing Applied Research Summer Associate within the Global Technology Applied Research (GTAR) division of JPMorgan Chase & Co., you will have the unique opportunity to advance the state of the art of theory and practice of quantum algorithms. You will collaborate with other researchers on a research project in areas like theoretical analysis, numerical benchmarking and implementation on hardware of quantum algorithms, quantum error correction and compilation to fault-tolerant architectures. Your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, and interaction with senior stakeholders.

Job Responsibilities: 

Advance the state of the art of theory and practice of quantum algorithms  Work with other researchers to document your findings in scientific papers Contribute to JPMC’s IP by pursuing necessary protections of generated IP

 

Required qualifications, capabilities, and skills

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

 

Preferred qualifications, capabilities, and skills

Experience in Quantum algorithms for optimization (e.g., QAOA, quantum adiabatic algorithm, quantum walks).  Experience in Quantum algorithms for machine learning (e.g., quantum algorithms for linear systems, PCA, classification).  Experience in Quantum linear algebra (QSP, QSVT, and specific realizations).  Experience in Simulation of quantum algorithms (e.g., MPS, PEPS, tensor networks).  Experience in High-performance computing (e.g., MPI, experience running computational tasks on 100+ nodes).  Experience in Theory of quantum algorithmic speedups (query complexity upper and lower bounds, universal techniques such as phase estimation, quantum counting, quantum walks, LCU and QSVT).  Experience in Quantum error correction and fault tolerance (e.g., qLDPC codes, QEC decoders) and Experience in Compilation of quantum algorithms to fault-tolerant architectures