Achieving Robust Quantum Control through Deep Reinforcement Learning [SELF-FUNDED STUDENTS ONLY]

The success of quantum computing, simulation, and sensing applications hinges on achieving robust control over quantum systems. However, the delicate nature of these systems makes them susceptible to errors, and current methods for quantifying robustness, such as Monte Carlo sampling, are computationally and experimentally expensive.

This PhD project explores the synergy between advanced reinforcement learning (RL) and robust quantum control. Unlike quantum error correction which focuses on fixing errors after they occur, robust quantum control offers an orthogonal solution, allowing us to design controls that proactively minimize errors. This project aims to develop a framework for achieving this using hierarchical RL approaches.

The core of your research will involve developing and implementing RL algorithms tailored to the specific challenges of quantum control. You will leverage deep neural networks to represent models and policies for the RL agent. Additionally, you will explore the integration of neuro-differentiable models, which allow us to make model and policy representations more meaningful, representing actual physical knowledge about the underlying systems, leading to improved control performance and robustness. You will be working on:

·    Hierarchical RL for Decomposed Control: You will investigate RL approaches that decompose complex quantum operations into smaller subgoals. This allows the RL agent to find controls for each subgoal independently, ultimately achieving a more efficient and robust overall operation.

·    Exploration and Optimization: You will explore approaches that separate learning the system dynamics (exploration) from implementing the specific target (optimization). This separation allows the RL agent to first understand the behaviour of the quantum system and then leverage that knowledge to design robust control sequences.

·    Lifelong Learning for Enhanced Robustness: Finally, you will investigate lifelong learning approaches where the RL agent continuously refines its control strategies based on new data. This holds the potential to achieve ever-increasing robustness against noise and decoherence, even as the system environment changes.

By mitigating errors, robust control will unlock the full potential of quantum technologies and establish a new quantum control tool, potentially simplifying hardware and paving the way for a new generation of control strategies.

This work will be part of a collaboration with Bristol University, Swansea University and the University of Southern California.

To learn more about this project, its requirements, and details please get in touch with Frank Langbein (LangbeinFC@cardiff.ac.uk).

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. A strong background in computer science, mathematics, physics, or related fields is required. A passion for reinforcement learning, experience with quantum control, and an eagerness to explore novel approaches are key assets.

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.

How to apply:

This project is accepting applications all year round, for self-funded candidates

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below.

Please submit your application via Computer Science and Informatics - Study - Cardiff University

In order to be considered candidates must submit the following information:

·       Supporting statement

·       CV

·       In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD

·       In the funding field of your application, please provide details of your funding source.

·       Qualification certificates and Transcripts

·       References x 2

·       Proof of English language (if applicable)

Interview - If the application meets the entrance requirements, you will be invited to an interview. 

If you have any additional questions or need more information, please contact: COMSC-PGR@cardiff.ac.uk