Magnetic-Field Learning Using a Single Electronic Spin in Diamond with One-Photon Readout at Room Temperature

Abstract

Nitrogen-vacancy (NV) centres in diamond are appealing nano-scale quantum sensors for temperature, strain, electric fields and, most notably, for magnetic fields. However, the cryogenic temperatures required for low-noise single-shot readout that have enabled the most sensitive NVmagnetometry reported to date, are impractical for key applications, e.g. biological sensing. Overcoming the noisy readout at room-temperature has until now demanded repeated collection of fluorescent photons, which increases the time-cost of the procedure thus reducing its sensitivity. Here we show how machine learning can process the noisy readout of a single NV centre at room-temperature, requiring on average only one photon per algorithm step, to sense magnetic field strength with a precision comparable to those reported for cryogenic experiments. Analysing large data sets from NV centres in bulk diamond, we report absolute sensitivities of 60 nTs 1/2 including initialisation, readout, and computational overheads. We show that dephasing times can be simultaneously estimated, and that time-dependent fields can be dynamically tracked at room temperature. Our results dramatically increase the practicality of early-term single spin sensors. arXiv:1807.09753v1 [quant-ph]

In-situ measurements of fabrication induced strain in diamond photonic-structures using intrinsic colour centres

“…strain during annealing in more detail may lead to improved post processing recipes for diamond quantum devices. Meanwhile, optimisation of the measurement parameters, and computational methods should enable even higher sensitivity measurements at room temperature 4 .…”

“…The fabrication of photonic structures coupled to single colour centres in diamond has been widely investigated [1][2][3][4][5] , as a key component in many quantum information processing or sensing fields 6,7 . Many attempts to fabricate structures coupled to nitrogen-vacancy (NV) centres have led to unwanted broadening of its spectral lines, reduction in its ground state coherence time, or blinking and ultimately the quenching of the NV centre's fluorescence.…”

Extending qubit coherence by adaptive quantum environment learning

“…. We note that, while this algorithm is based on a series of exponential sensing times, there are other possible strategies, such as sequential Monte Carlo protocols recently introduced for quantum sensing [37]. By using re-sampling strategies, these protocols may minimise the number of coefficients required in the Bayesian update, resulting in a more resource-efficient performance.…”

“…The techniques discussed above rely on manipulating the central spin and/or its nuclear environment, but alternatively one may track and compensate environmental fluctuations through Hamiltonian learning. Recent significant advances in computational power and high-speed programmable electronics have made real-time learning algorithms experimentally viable [34][35][36][37]. For example, by measuring the qubit faster than the fluctuations responsible for decoherence, one can adaptively compensate any detuning and maintain coherence [38].…”

Repetitive readout enhanced by machine learning

“…On the other hand, machine learning (ML) is designed to discover hidden data correlations, and it is widely used in classification problems [23]. It has been recently introduced in quantum information tasks to mitigate crosstalks in multi-qubit readout [24], to enhance quantum metrology [25,26], to identify quantum phases of matter and phase transitions [27][28][29], to identify entanglement [30][31][32], and even to determine existence of quantum advantage [33], to name a few. In particular, ML shows success in efficient interpretation of quantum state tomography (QST), by being robust to partial QST and state-preparation-and-measurement (SPAM) errors [32,[34][35][36].…”