Machine-learning-assisted electron-spin readout of nitrogen-vacancy center in diamond

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, they are thus robust and free. Our machine learning method implies a wide range of applications in precision measurement and optical detection of states.

arXiv comment5 pagers, 4figures, comments are welcome

References in corpus (26)

• The nitrogen-vacancy colour centre in diamond
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Silicon-Vacancy Spin Qubit in Diamond: A Quantum Memory Exceeding 10 ms with Single-Shot State Readout
• Spin dynamics in the optical cycle of single nitrogen-vacancy centres in diamond
• Experimental fault-tolerant universal quantum gates with solid-state spins under ambient conditions
• Universal enhancement of the optical readout fidelity of single electron spins
• A Neural Decoder for Topological Codes
• Phonon-Induced Population Dynamics and Intersystem Crossing in Nitrogen-Vacancy Centers
• Assessing the progress of trapped-ion processors towards fault-tolerant quantum computation
• Experimental Machine Learning of Quantum States
• Spin Readout Techniques of the Nitrogen-Vacancy Center in Diamond
• Machine learning for discriminating quantum measurement trajectories and improving readout
• Nano-fabricated solid immersion lenses registered to single emitters in diamond
• A Separability-Entanglement Classifier via Machine Learning
• Deep Neural Network Probabilistic Decoder for Stabilizer Codes
• Observation of quantum jumps of a single quantum dot spin using sub-microsecond single-shot optical readout
• Efficient signal processing for time-resolved fluorescence detection of nitrogen-vacancy spins in diamond
• Experimental time-optimal universal control of spin qubits in solids
• Bayesian estimation for quantum sensing in the absence of single-shot detection
• Machine learning assisted readout of trapped-ion qubits
• Magnetic-field-learning using a single electronic spin in diamond with one-photon-readout at room temperature
• Detecting dynamical quantum phase transition via out-of-time-order correlations in a solid-state quantum simulator
• Dynamically Polarizing Spin Register of NV Centers in Diamond using Chopped Laser Pulses
• Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine Learning Methods
• Quantum state tomography of a single electron spin in diamond with Wigner reconstruction
• High contrast quantum imaging with time-gated fluorescence detection

Cited by in corpus (6)

• Frequentist Parameter Estimation with Supervised Learning
• Machine Learning-Assisted Manipulation and Readout of Molecular Spin Qubits
• Preserving Entanglement in a Solid-Spin System Using Quantum Autoencoders
• Noise prediction and reduction of single electron spin by deep-learning-enhanced feedforward control
• Direct Readout of Nitrogen-Vacancy Hybrid-Spin Quantum Register in Diamond by Photon Arrival Time Analysis
• Multi-shot readout error benchmark of the nitrogen-vacancy center's electronic qubit