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Bulletin of the American Physical Society

Experimental demonstration of quantum-enhanced machine learning in NV center system

Wengang Zhang, Xiaolong Ouyang, Xianzhi Huang, Dong-Ling Deng, Luming Duan

Published Mar 6, 2020• 0 citations
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We demonstrate the quantum-enhanced supervised classification of vectors in a nitrogen-vacancy ($NV$)--center system using the algorithm of Lloyd, Mohseni, and Rebentrost (arXiv:1307.0411). A $^{13}\mathrm{C}$ nuclear spin is employed to encode the vectors with the spin-triplet state of the $NV$ center as an ancilla. We design efficient methods to prepare the initial electron-nuclear entangled state within the coherence time ${T}_{2}^{*}$ of the electron spin, and then compute the distance between the test vector and the center of each class by measuring the level population through maximum likelihood estimation. Our experiment allows more than one reference vector in each class, thus forming an important enabling step toward quantum-enhanced machine learning.

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Optimization and Error-Corrected Quantum Sensing with NV Centers in SiC: An Extension to EPR/ENDOR Characterization

Pacha Lohith Chowdary, Balagala Chethan, Swaminadhan Rajula

This extension method advances the characterization of NV centers in silicon carbide (SiC) by incorporating optimization strategies, machine learning (ML), error correction, and advanced sensing protocols. While the base method validated NV centers in 4H-SiC and 6H-SiC as promising solid-state qubits, our extension aims to enhance their performance and scalability for quantum technologies. First, we identified an optimal NV ensemble density of 1.0 × 10¹⁶ cm⁻3, balancing signal strength with minimized dipole–dipole interactions, thereby maximizing collective coherence. Second, a machine learning framework was developed to optimize experimental conditions, scanning laser power, excitation wavelength, and temperature to improve phase coherence time (T₂) by nearly two-fold. Third, advanced quantum sensing protocols were benchmarked, including Ramsey, Spin-Echo, CPMG, XY-8, and ML-optimized pulse sequences, achieving up to 3.5× higher sensitivity compared to conventional spin-echo. Fourth, dynamical decoupling (N=16) was applied to mitigate decoherence, extending T₂ up to ~60 μs at room temperature. Finally, error correction simulations at the NV ensemble level demonstrated logical error rate reductions by nearly two orders of magnitude, showcasing fault-tolerance potential. Overall, this extension transforms NV–SiC centers from characterized quantum defects into a practical platform with enhanced coherence, improved sensing capabilities, and integrated error correction. These results position SiC NV ensembles as a scalable candidate for next-generation quantum computing

International Conference on Electronics, Communication and Aerospace Technology (ICECA)•Nov 5, 2025•0 citations
Abstract With the advancement of computing power and algorithms, machine learning has been a powerful tool in numerous applications nowadays. However, the hardware limitation of classical computers and the increasing size of datasets urge the community to explore new techniques for machine learning. Quantum‐enhanced machine learning is such a rapidly growing field. It refers to quantum algorithms that are implemented in quantum computers, which can improve the computational speed of classical machine learning tasks and often promises an exponential speedup. In the past few years, the development of experimental quantum technologies leads to many experimental demonstrations of quantum‐enhanced machine learning in diverse physical systems. Here, the recent experimental progress in this field in two typical spin‐based quantum systems—nuclear magnetic resonance and nitrogen‐vacancy centers in diamond—is reviewed, and the ongoing challenges are discussed.
We demonstrate the quantum-enhanced supervised classification of vectors in a nitrogen-vacancy (NVNVNV)--center system using the algorithm of Lloyd, Mohseni, and Rebentrost (arXiv:1307.0411). A 13C^{13}\mathrm{C}13C nuclear spin is employed to encode the vectors with the spin-triplet state of the NVNVNV center as an ancilla. We design efficient methods to prepare the initial electron-nuclear entangled state within the coherence time T2∗{T}_{2}^{*}T2∗​ of the electron spin, and then compute the distance between the test vector and the center of each class by measuring the level population through maximum likelihood estimation. Our experiment allows more than one reference vector in each class, thus forming an important enabling step toward quantum-enhanced machine learning.
This extension method advances the characterization of NV centers in silicon carbide (SiC) by incorporating optimization strategies, machine learning (ML), error correction, and advanced sensing protocols. While the base method validated NV centers in 4H-SiC and 6H-SiC as promising solid-state qubits, our extension aims to enhance their performance and scalability for quantum technologies. First, we identified an optimal NV ensemble density of 1.0 × 10¹⁶ cm⁻3, balancing signal strength with minimized dipole–dipole interactions, thereby maximizing collective coherence. Second, a machine learning framework was developed to optimize experimental conditions, scanning laser power, excitation wavelength, and temperature to improve phase coherence time (T₂) by nearly two-fold. Third, advanced quantum sensing protocols were benchmarked, including Ramsey, Spin-Echo, CPMG, XY-8, and ML-optimized pulse sequences, achieving up to 3.5× higher sensitivity compared to conventional spin-echo. Fourth, dynamical decoupling (N=16) was applied to mitigate decoherence, extending T₂ up to ~60 μs at room temperature. Finally, error correction simulations at the NV ensemble level demonstrated logical error rate reductions by nearly two orders of magnitude, showcasing fault-tolerance potential. Overall, this extension transforms NV–SiC centers from characterized quantum defects into a practical platform with enhanced coherence, improved sensing capabilities, and integrated error correction. These results position SiC NV ensembles as a scalable candidate for next-generation quantum computing