Global Technology Applied Research Senior Associate - Quantum Computing

JPMorgan Chase's Global Technology Applied Research team is looking for quantum computing researchers.

Preference will be given to candidates with strong publication record in one of the following domains: (i) practical aspects of error detection and error correction, (ii) high-performance simulation of quantum computers (MPS, tensor networks), (iii) theory of quantum algorithms for optimization and (iv) quantum-inspired algorithms.

No prior familiarity with finance or financial use cases is required .

About You
We are looking for innovative problem-solvers with a passion for turning scientific research into commercial, business value.

The focus of your role will be to:


* Advance the field of quantum algorithms and their applications to optimization, machine learning and financial use cases


* Provide novel research solutions to problems faced by internal project teams


* Work with other researchers to document your findings in scientific papers


* Contribute to JPMC's IP by pursuing necessary protections of generated IP

Key Qualifications/Skills:


* A Master's or Ph.D.

degree in computer science, physics, math, engineering 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


* Experience in one or more following domains:
+ Theory of quantum information (query complexity)
+ Quantum algorithms for optimization (QAOA, quantum adiabatic algorithm, quantum walks)
+ Quantum algorithms for machine learning (quantum algorithms for linear systems, PCA, classification)
+ Quantum linear algebra (LCU, QSVT)
+ Quantum error correction and fault tolerance (qLDPC codes, QEC decoders)
+ Simulation of quantum algorithms (MPS, PEPS, tensor networks)
+ High-performance computing (MPI, experience running computational tasks on 100+ nodes)
+ Quantum-inspired algorithms


* No prior familiarity with finance or financial use cases required


* Preference is given to candidates with a strong publication record in one of the following domains: (i) practical aspects of error detection and error correction (ii) high-performance simulation of quantum computers (MPS, tensor networks), (iii) theory of quantum algorithms for optimization and (iv) quantum-inspired algorithms.

No prior familiarity with finance or financial use cases is required.

We encourage candidates to include a link to their Google Scholar or Semantic Scholar profile in their resume

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