| Metadata | Details |
|---|
| Publication Date | 2021-02-22 |
| Journal | Applied Physics Letters |
| Authors | Peng Qian, Lin Xue, Feifei Zhou, Runchuan Ye, Yunlan Ji |
| Institutions | Hefei University of Technology |
| Citations | 25 |
| Analysis | Full AI Review Included |
- Core Breakthrough: Introduced Machine Learning (ML) via linear regression to optimize room-temperature Nitrogen-Vacancy (NV) center electron-spin readout precision using time-resolved photoluminescence (PL).
- Problem Solved: The ML method overcomes the fundamental trade-off inherent in traditional time-gated methods, which forces optimization between maximizing signal contrast (C) and minimizing total variance (V).
- Performance Improvement: The technique achieved a 7% reduction in spin readout error compared to the best traditional metric (minimal-total-variance, mini-V).
- Variance Reduction: The ML model resulted in a 39.6% lower average variance compared to the maximal-contrast (max-C) metric, without sacrificing signal contrast.
- Efficiency and Robustness: The improvement is achieved purely through data processing, requiring only the recording of photon time traces and consuming no additional experimental time, making the method robust and cost-effective.
- Mechanism: The ML model adaptively learns optimal, non-binary weights for each time bin in the fluorescence trace, maximizing the information extracted from shot noise-limited data.
| Parameter | Value | Unit | Context |
|---|
| Excitation Laser Wavelength | 532 | nm | Used for optical addressability of NV centers. |
| Time Tagger Resolution | 2 | ns | Self-made FPGA-based module used for recording time traces. |
| Operating Temperature | Room | Temperature | System operates under ambient conditions. |
| Traditional Max Contrast Gate Width | 234 | ns | Optimal gate width for maximizing signal contrast (C). |
| Traditional Min Variance Gate Width | 476 | ns | Optimal gate width for minimizing total variance (V). |
| ML Readout Error Reduction | 7 | % | Reduction compared to the minimal-total-variance (mini-V) traditional metric. |
| ML Average Variance Reduction | 39.6 | % | Reduction compared to the maximal-contrast (max-C) traditional metric. |
| Training Set Size (Boundary Traces) | 106 to 109 | Repetitions | Used for training the linear regression coefficient model. |
| Test Set Size (Rabi Data) | 5 x 105 | Repetitions | Used to validate the performance of the ML model. |
| NV Center Spin State Contrast | ~30 | % | Contrast between ms = 0 and ms = ±1 total photon counts. |
- System Setup: Room-temperature NV center in diamond was excited using a 532 nm laser via a homebuilt confocal microscopy system.
- Data Acquisition: Time-resolved fluorescence traces were recorded using a self-made FPGA-based time tagger with 2 ns resolution. Data was accumulated over high repetition counts (up to 109) for training sets.
- Traditional Baseline Establishment: Readout performance was evaluated using the traditional time-gated method, where the gate width (Ît) was optimized separately for maximal contrast (C) and minimal total variance (V).
- ML Model Selection: Linear Regression was chosen as the prediction model to map the time-binned photon vector (x) to the spin population probability (p).
- Loss Function Modification: The standard mean squared error loss function was physically augmented by adding a term representing the total variance (V = ÎŁÏ2j) of the training examples.
- Model Training: The gradient descent algorithm was used to iteratively update the regression coefficients (ai). The modified loss function ensured that the model balanced prediction accuracy (contrast) against variance minimization (precision), preventing coefficients from going to extremes.
- Coefficient Application: The resulting regression coefficients provided non-binary weights for each time bin, matching the trend of the differential fluorescence signal (Fig. 3a).
- Validation: The trained coefficient model was applied to independent Rabi oscillation test data (5 x 105 repetitions) to calculate the resulting state population and variance metrics, demonstrating improved alignment and reduced deviation.
- Quantum Information Processing: Enhancing the fidelity and speed of qubit state discrimination, particularly in systems relying on time-resolvable fluorescence readout (e.g., trapped ions, superconducting qubits, single quantum dots).
- Precision Quantum Sensing: Directly improving the sensitivity of NV-center-based sensors used for magnetometry, thermometry, and electric field sensing by raising the fundamental level of spin readout performance.
- Fault-Tolerant Quantum Computing: Providing the highly accurate, low-error state readout necessary for constructing robust, fault-tolerant quantum error correcting codes.
- Solid-State Qubit Readout: Applicable to other solid-state defects and color centers, such as silicon-vacancy centers in diamond, which utilize time-resolved PL for single-shot state readout.
- Biomedical Fluorescence Spectroscopy: The ML method can be extended to help distinguish different enzymatic reaction steps or differentiate malignant tumors from non-malignant tissues in cancer detection, where time-resolved PL is critical.
View Original Abstract
Machine learning is a powerful tool in finding hidden data patterns for quantum information processing. Here, we introduce this method into the optical readout of electron-spin states in diamond via single-photon collection and demonstrate improved readout precision at room temperature. The traditional method of summing photon counts in a time gate loses all the timing information crudely. We find that changing the gate width can only optimize the contrast or the state variance, not both. In comparison, machine learning adaptively learns from time-resolved fluorescence data and offers the optimal data processing model that elaborately weights each time bin to maximize the extracted information. It is shown that our method can repair the processing result from imperfect data, reducing 7% in spin readout error while optimizing the contrast. Note that these improvements only involve recording photon time traces and consume no additional experimental time, and they are, thus, robust and free. Our machine learning method implies a wide range of applications in the precision measurement and optical detection of states.
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- 2019 - Magnetic-field learning using a single electronic spin in diamond with one-photon readout at room temperature [Crossref]
- 2019 - Bayesian estimation for quantum sensing in the absence of single-shot detection [Crossref]
- 2018 - Machine learning assisted readout of trapped-ion qubits [Crossref]
- 2017 - Solving the quantum many-body problem with artificial neural networks [Crossref]
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