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) center at JPMorgan Chase & Co.. The goal of GTAR is to design and conduct research across multiple frontier technologies, in order to enable novel discoveries and inventions, and to inform and develop next-generation solutions for the firm’s clients and businesses.

As a Quantum Computing Research Scientist – Executive Director within the Global Technology Applied Research (GTAR) center at JPMorgan Chase & Co., you will have the opportunity to advance the theoretical understanding of quantum algorithms for optimization, stochastic modelling, numerical analysis, machine learning and financial use cases. You will collaborate with other researchers to prove rigorous theoretical results and validate them in classical simulation and on hardware.

Job Responsibilities:

Advance the field of quantum algorithms and their applications to optimization, stochastic modelling, numerical analysis, machine learning and financial use cases Provide novel research solutions to problems faced by internal project teams Collaborate with quantum algorithms researchers to investigate the potential for implementing your work on hardware Work with other researchers to document your findings in scientific papers and present them at conferences Contribute to JPMC’s IP by pursuing necessary protections of generated IP

Required qualifications, capabilities, and skills

Ph.D. degree in computer science, physics, math, engineering or related fields, plus at least 6 years of experience (Industry or Academic) Demonstrated research ability in quantum computing or related fields Experience in scientific technical writing Strong communication skills and the ability to present findings to a non-technical audience Experience in one or more following domains: Theory of quantum algorithmic speedups (query complexity upper and lower bounds, universal techniques such as phase estimation, quantum counting, quantum walks, LCU and QSVT). Analysis of quantum algorithms for optimization (e.g., QAOA, quantum adiabatic algorithm, quantum annealing, quantum walks ). Quantum algorithms for machine learning (e.g., quantum algorithms for linear systems, PCA, classification). Quantum algorithms for linear algebra and numerical analysis (QSP, QSVT, and specific realizations) 

Preferred qualifications, capabilities, and skills

Preference is given to candidates with strong publication record Familiarity/Experience with quantum information, quantum complexity theory, and/or quantum error correction is desirable Experience in finance is a plus, though no prior familiarity with financial use cases is required. Preference is given to candidates with accepted papers on the quantum computing theory at conferences and journals on quantum computing or theoretical computer science such as STOC, FOCS, QIP and TQC.