Related papers: Identifying Pauli spin blockade using deep learnin…
Among supervised learning models, Support Vector Machine stands out as one of the most robust and efficient models for classifying data clusters. At the core of this method, a kernel function is employed to calculate the distance between…
Prediction-Powered Inference (PPI) is a recently proposed statistical inference technique for parameter estimation that leverages pseudo-labels on both labeled and unlabeled data to construct an unbiased, low-variance estimator. In this…
Extracting information from quantum many-body systems remains a key challenge in quantum technologies due to experimental limitations. In this work, we employ a single spin qubit to probe a strongly interacting system, creating an…
We propose a novel scheme to efficiently polarize and manipulate the electron spin in a quantum dot. This scheme is based on the spin-orbit interaction and it possesses following advantages: (1) The direction and the strength of the spin…
A short review of the pulsed electrically detected magnetic resonance (pEDMR) experiment is presented. PEDMR allows the highly sensitive observation of coherent electron spin motion of charge carriers and defects in semiconductors by means…
We introduce and explicitly construct a quantum code we coin a "Pauli Manipulation Detection" code (or PMD), which detects every Pauli error with high probability. We apply them to construct the first near-optimal codes for two tasks in…
A fault-tolerant quantum processor may be configured using stationary qubits interacting only with their nearest neighbours, but at the cost of significant overheads in physical qubits per logical qubit. Such overheads could be reduced by…
We propose realistic methods to detect local spin polarization, which utilize a quantum dot side coupled to the target system. By choosing appropriate states in the dot, we can put spin selectivity to the dot and detect spins in the target…
Nowadays, supervised deep learning techniques yield the best state-of-the-art prediction performances for a wide variety of computer vision tasks. However, such supervised techniques generally require a large amount of manually labeled…
I've been building Powerpoint-based quantum computers with electron spins in silicon for 20 years. Unfortunately, real-life-based quantum dot quantum computers are harder to implement. Materials, fabrication, and control challenges still…
This paper presents a challenging computer vision task, namely the detection of generic components on a PCB, and a novel set of deep-learning methods that are able to jointly leverage the appearance of individual components and the…
As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge…
We present recent progress towards the implementation of a scalable quantum processor based on fully-depleted silicon-on-insulator (FDSOI) technology. In particular, we discuss an approach where the elementary bits of quantum information -…
Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few…
We review several proposed spintronic devices that can provide new functionality or improve available functions of electronic devices. In particular, we discuss a high mobility field effect spin transistor, an all-metal spin transistor, and…
Charge-based qubits have been proposed as fundamental elements for quantum computers. One commonly proposed readout device is the single-electron transistor (SET). SETs can distinguish between localized charge states, but lack the…
Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning…
Utilizing the Pauli-blocking mechanism we show that shining circular polarized light on a singly-charged quantum dot induces spin dependent fluorescence. Employing the quantum-jump technique we demonstrate that this resonance luminescence,…
The performance of quantum classifiers is typically analyzed through global state distinguishability or the trainability of variational models. This study investigates how much class information remains accessible under locality-constrained…
Understanding quantum noise is an essential step towards building practical quantum information processing systems. Pauli noise is a useful model that has been widely applied in quantum benchmarking, error mitigation, and error correction.…