Optimal Control for Quantum Sensing with Spins in Crystal
Bibliographic record
Abstract
The precise control of quantum systems is crucial for, e.g., the implementation of gate operations, to prepare specific quantum states, or to enhance the sensitivity or polarization for quantum sensing. To tackle such challenges, quantum optimal control (QOC) provides a set of tools in order to bring quantum technologies to their full potential. This thesis revolves around the development of such control strategies, with a focus on spins in crystals often used for quantum sensing applications. I have a look at polarization techniques under elaborate experimental conditions, the assessment of gate evaluation metrics, and the development of a software package for practical utilization on optimization problems in simulation and experiment. In particular, a strategy to speed up and increase the polarization of protons in a naphthalene crystal through dynamic nuclear polarization from the optically accessible electron spin of pentacene molecules is presented. Due to a complex experimental setup, we use closed-loop control to optimize the applied microwave pulse shapes. The resulting protocol enables an efficient strategy for enhanced macroscopic hyperpolarization of the sample for use in nuclear magnetic resonance experiments, that can potentially be transferred to different experimental scenarios. Control strategies need to be viewed in the context of experimental constraints and the control hyperparameters have to be tuned together with the specific control problem. We therefore created a software package called the Quantum Optimal Control Suite (QuOCS) in Python, to provide a unified framework for the development of control algorithms. Its modular and open-source nature enables a straightforward extension with new ideas. It is easy to use and thus lowers the hurdle to make use of QOC for a wide range of problems. Its features are discussed and examples for the application through open-loop optimizations based on simulation as well as closed-loop control with communication to an experiment are shown. The software is then used to investigate various gate and gate-set evaluation metrics for their suitability in closed-loop optimization. An ensemble of nitrogen vacancy centers in diamond serves as the experimental test-bed for control objectives derived from methods such as quantum process tomography, gate-set tomography and randomized benchmarking. It becomes clear that gate operations have to be viewed in the context of their application and an extensive cross-comparison provides a better understanding for the choice among the tested methods for control tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".