We are seeking a highly skilled and motivated Researcher to join the Applications Team within the Theory Group.
The Quantum Applications Team is responsible for exploring practical applications and quantum algorithms that leverage Quantum Source's fault-tolerant quantum computing (FTQC) architecture — identifying high-impact use cases, developing and benchmarking quantum algorithms, and collaborating with industry and academic partners to demonstrate the advantage of our hardware.
As a Researcher on the team, your core focus will be quantum resource estimation — determining the runtime and physical footprint required to solve problems of practical significance on our hardware, for algorithms ranging from cryptanalysis to quantum simulation for chemistry and materials. Over time, you will help expand the team's application portfolio and demonstrate the advantages of Quantum Source's hardware across a growing range of use cases.
Responsibilities:
Perform quantum resource estimation for target algorithms and applications, deriving runtime, qubit count, and physical footprint for a given target accuracy.
Identify and analyze new high-impact use cases where Quantum Source's compound photon-atom quantum computer offers an advantage over classical and competing approaches.
Develop, adapt, and benchmark quantum algorithms against best-known classical and quantum alternatives, extending the state of the art as the team's application portfolio grows.
Collaborate with industry and academic partners to validate assumptions, incorporate emerging techniques, and stay current with the literature.
Work closely with other teams within the Theory Group, including the error correction/infra side, to ground algorithm-level estimates in realistic fault-tolerant hardware assumptions.
Requirements:
Ph.D. in Physics or Computer Science/Electrical Engineering/Mathematics with background in quantum algorithms/quantum information, or a related field (M.Sc. considered for outstanding candidates).
Strong mathematical proficiency and comfort with complexity and asymptotic analysis (O(n)-style scaling arguments).
Solid algorithmic skills and proven ability to implement complex algorithms in Python (or another language).
Ability to quickly digest current research literature and extract relevant results.
Steep learning curve and readiness to explore new application domains.
Excellent communication skills and ability to collaborate effectively in a multidisciplinary team environment.
