Related papers: Noise-robust classification of single-shot electro…

Single-shot spin-state discrimination is essential for semiconductor spin qubits, but conventional threshold-based analysis of spin readout traces becomes unreliable under noisy conditions. Although recent neural-network-based methods…

Optically addressable electron spin clusters are of interest for quantum computation, simulation and sensing. However, with interaction length scales of a few tens of nanometers in the strong coupling regime, they are unresolved in…

In-situ classification of faulty sounds is an important issue in machine health monitoring and diagnosis. However, in a noisy environment such as a factory, machine sound is always mixed up with environmental noises, and noise-only periods…

We present a method for reading out the spin state of electrons in a quantum dot that is robust against charge noise and can be used even when the electron temperature exceeds the energy splitting between the states. The spin states are…

An important requirement for spin-based quantum information processing is reliable and fast readout of electron spin states, allowing for feedback and error correction. However, common readout techniques often require additional gate…

Establishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal…

Qubit readout schemes often deviate from ideal projective measurements, introducing critical issues that limit quantum computing performance. In this work, we model charge-sensing-based readout for semiconductor spin qubits in double…

The spin of an electron confined in semiconductor quantum dots is currently a promising candidate for quantum bit (qubit) implementations. Taking advantage of existing CMOS integration technologies, such devices can offer a platform for…

Strong coupling of semiconductor spin qubits to superconducting microwave resonators was recently demonstrated. These breakthroughs pave the way for quantum information processing that combines the long coherence times of solid-state spin…

Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A…

This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This…

Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a…

An outstanding hurdle for defect spin qubits in silicon carbide (SiC) is single-shot readout - a deterministic measurement of the quantum state. Here, we demonstrate single-shot readout of single defects in SiC via spin-to-charge…

Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) driven, semantic-segmentation pipeline that performs charge auto-tuning by locating…

Deep neural networks (DNNs) have found widespread applications in interpreting remote sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are vulnerable to different types of noises, particularly adversarial…

The efficient single photon emission capabilities of quantum dot molecules position them as promising platforms for quantum information processing. Furthermore, quantum dot molecules feature a "decoherence-free" subspace that enables spin…

Silicon spin qubits form one of the leading platforms for quantum computation. As with any qubit implementation, a crucial requirement is the ability to measure individual quantum states rapidly and with high fidelity. As the signal from a…

High fidelity single-shot readout of qubits is a crucial component for fault-tolerant quantum computing and scalable quantum networks. In recent years, the nitrogen-vacancy (NV) center in diamond has risen as a leading platform for the…

In semiconductor spin quantum bits (qubits), the radio-frequency (RF) gate-based readout is a promising solution for future large-scale integration, as it allows for a fast, frequency-multiplexed readout architecture, enabling multiple…

Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known that DNNs are highly sensitive to noise, whether adversarial…