About this role
Summary: This internship involves establishing the integration of AI agents with quantum control hardware to realize AI-enhanced quantum optimal control. The role focuses on using machine learning models to optimize control pulses, stabilize gate fidelities, and reduce calibration overhead in superconducting and hybrid qubit...
Machine learning (ML) provides a powerful framework to enhance quantum control by enabling adaptive, data-driven optimization of control pulses in the presence of complex noise, drift, and hardware constraints. Instead of relying solely on analytic pulse design or gradient-based optimal control (e.g., GRAPE), ML models, such as reinforcement learning agents, Bayesian optimizers, or neural-network-based surrogate models, can learn the effective system dynamics directly from experimental data, including non-Markovian noise and control distortions. In real time, ML can update pulse parameters to compensate for frequency shifts, crosstalk, and amplitude nonlinearities, thereby stabilizing gate fidelities and reducing calibration overhead. Moreover, model-based ML approaches can infer Hamiltonian parameters and decoherence channels on-the-fly, enabling closed-loop quantum control and adaptive error suppression. When integrated with quantum error correction protocols (e.g., surface codes or bosonic encodings), ML-enhanced controllers can dynamically adjust syndrome extraction timing and feedback operations, pushing superconducting and hybrid qubit platforms toward higher coherence, lower latency control, and scalable fault tolerance. In this project, we will establish the integration of AI agent with the quantum control hardware to realize AI-enhance quantum optimal control.
Education and Experience Requirements
- The entirety of the appointment must be conducted within the United States.
- Applicants must be: (1) Currently enrolled in undergraduate or graduate studies at an accredited institution. (2) Graduated from an accredited institution within the past 3 months; or (3) Actively enrolled in a graduate program at an accredited institution.
- Must be 18 years or older at the time the appointment begins.
- Must possess a cumulative GPA of 3.0 on a 4.0 scale.
- Must be a U.S. citizen or Legal Permanent Resident at the time of application.
- If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies.
Job Family
DOE Seasonal Intern
Job Profile
DOE - SULI (Science Undergraduate Laboratory Internship)
Worker Type
Contingent Worker
Time Type
Full time
Scheduled Weekly Hours
40
EEO Information
*As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.*
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.