QuEra Computing is the leading provider of quantum computers based on neutral-atoms. Our mission is to build the most scalable quantum computers. Backed by SoftBank VF.
About the role
The approximate base salary range for this position is $110,000-$120,000. We consistently monitor external market data and update base salary ranges accordingly. We determine base compensation decisions on several factors, including as geographic placement, role-specific knowledge, skills, and/or experience. In addition to our base salary offerings, we also provide equity grants for all new hires.
What they're looking for
- Develop and deploy machine learning models for high-fidelity quantum operation inference and control prediction
- Design and prototype in-the-loop control mechanisms that adapt sequences based on measurement outcomes and system state
- Collaborate with physics, quantum error-correction, hardware, and control teams to validate new stack components using experimental data and system-level performance
- Ph.D. or equivalent experience in Physics, Computer Science, Electrical Engineering, or a related field, with a strong background in quantum computing or quantum physics
- Experience working with quantum computing platforms (neutral atoms, trapped ions, superconducting qubits, or similar)
- Demonstrated experience working with Machine Learning for inference and hardware in loop
More about this role
Summary
This role focuses on developing computational methods for in-the-loop stabilization of neutral atom Logical Quantum Processing Units (LQPUs). The position sits at the interface of quantum hardware and control systems, supporting both near-term experimental performance and the long-term development of control architectures for stable, fault-tolerant computing.
The successful candidate will design and prototype state-of-the-art methods to enable reliable quantum operations and translate device measurements into actionable control feedback. Responsibilities include advancing capabilities such as measurement-informed feedback control and machine learning–driven inference.
- Develop and deploy machine learning models for high-fidelity quantum operation inference and control prediction.
- Design and prototype in-the-loop control mechanisms that adapt sequences based on measurement outcomes and system state.
- Collaborate with physics, quantum error-correction, hardware, and control teams to validate new stack components using experimental data and system-level performance
Required Qualifications
- Ph.D. or equivalent experience in Physics, Computer Science, Electrical...
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