Repetitive readout enhanced by machine learning

Quantum reinforcement learning in the presence of thermal dissipation

A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose, a nondissipative quantum reinforcement learning protocol is adapted to the presence of thermal dissipation. Analytical calculations as well as numerical simulations are carried out, obtaining evidence that dissipation does not significantly degrade the performance of the quantum reinforcement learning protocol for sufficiently low temperatures, in some cases even being beneficial. Quantum reinforcement learning under realistic experimental conditions of thermal dissipation opens an avenue for the realization of quantum agents to be able to interact with a changing environment, as well as adapt to it, with many plausible applications inside quantum technologies and machine learning.

Efficient Entanglement of Spin Qubits Mediated by a Hot Mechanical Oscillator

Localized electronic and nuclear spin qubits in the solid state constitute a promising platform for storage and manipulation of quantum information, even at room temperature. However, the development of scalable systems requires the ability to entangle distant spins, which remains a challenge today. We propose and analyze an efficient, heralded scheme that employs a parity measurement in a decoherence free subspace to enable fast and robust entanglement generation between distant spin qubits mediated by a hot mechanical oscillator. We find that high-fidelity entanglement at cryogenic and even ambient temperatures is feasible with realistic parameters and show that the entangled pair can be subsequently leveraged for deterministic controlled-NOT operations between nuclear spins. Our results open the door for novel quantum processing architectures for a wide variety of solid-state spin qubits.

Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213; no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149; no OSA, n = 46) and test data (OSA, n = 64; no OSA, n = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models—logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)—and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population.

Sequential Bayesian experiment design for optically detected magnetic resonance of nitrogen-vacancy centers

In magnetometry using optically detected magnetic resonance of nitrogen vacancy (NV-) centers, we demonstrate more than one order-of-magnitude speed up with sequential Bayesian experiment design as compared with conventional frequency-swept measurements. The NV- center is an excellent platform for magnetometry with potential spatial resolution down to few nanometers and demonstrated single-defect sensitivity down to nT/Hz1/2. The NV- center is a quantum defect with spin S = 1 and coherence time up to several milliseconds at room temperature. Zeeman splitting of the NV- energy levels allows detection of the magnetic field via photoluminescence. We compare conventional NV- center photoluminescence measurements that use pre-determined sweeps of the microwave frequency with measurements using a Bayesian inference methodology. In sequential Bayesian experiment design, the settings with maximum utility are chosen for each measurement in real time based on the accumulated experimental data. Using this method, we observe an order of magnitude decrease in the NV- magnetometry measurement time necessary to achieve a set precision.

Quantum machine learning and quantum biomimetics: A perspective

Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quantum reinforcement learning aims at developing ‘intelligent’ quantum agents that may interact with the outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm inside quantum machine learning is that of quantum autoencoders, which may allow one for employing fewer resources in a quantum device via a training process. Moreover, the field of quantum biomimetics aims at establishing analogies between biological and quantum systems, to look for previously inadvertent connections that may enable useful applications. Two recent examples are the concepts of quantum artificial life, as well as of quantum memristors. In this Perspective, we give an overview of these topics, describing the related research carried out by the scientific community.

Introducing Machine Learning: Science and Technology

Due to the remarkable progress of ever-growing digitalisation and computing capabilities, data has become increasingly abundant, and machine learning has emerged as a key ingredient in many enabling technologies within modern society. Its potential for pushing the frontiers of science is now also clear and has been demonstrated in various domains extending from novel materials design, quantum physics and the simulation of molecules and chemical systems, to particle physics, medical imaging, space science, climate science and drug discovery. Conceived in close consultation with the community, Machine Learning: Science and Technology has been launched as a unique multidisciplinary, open access journal that will bridge the application of machine learning across the natural sciences with new conceptual advances in machine learning methods as motivated by physical insights.