Related papers: Identifying Pauli spin blockade using deep learnin…
Silicon quantum dot spin qubits provide a promising platform for large-scale quantum computation because of their compatibility with conventional CMOS manufacturing and the long coherence times accessible using $^{28}$Si enriched material.…
A theoretical spin-based scheme for performing a variety of quantum computations is presented. It makes use of an array of multiple identical computer vectors of phosphorus-doped silicon where the nuclei serve as logical qubits and the…
In this letter, we present fast readout of Pauli spin blockade phenomena and interdot coupling tunability in a silicon double quantum dot (DQD) fabricated using industry-compatible processes. The interdot couplings are tuned with a second…
In semiconductor nanostructures, spin blockade (SB) is the most scalable mechanism for electrical spin readout, requiring only two bound spins for its implementation. In conjunction with charge sensing techniques, SB has led to…
Donor spin qubits in silicon offer one- and two-qubit gates with fidelities beyond 99%, coherence times exceeding 30 seconds, and compatibility with industrial manufacturing methods. This motivates the development of large-scale quantum…
We report the dispersive readout of the spin state of a double quantum dot formed at the corner states of a silicon nanowire field-effect transistor. Two face-to-face top-gate electrodes allow us to independently tune the charge occupation…
We implement silicon quantum dot devices with two layers of gate electrodes using a self-alignment technique, which allows for ultra-small gate lengths and intrinsically perfect layer-to-layer alignment. In a double quantum dot system, we…
We present measurements on spin blockade in a laterally integrated quantum dot. The dot is tuned into the regime of strong Coulomb blockade, confining ~ 50 electrons. At certain electronic states we find an additional mechanism suppressing…
We propose a scheme to read out the spin of a single electron quantum bit in a surface Paul trap using oscillating magnetic field gradients. The readout sequence is composed of cooling, driving, amplification and detection of the electron's…
Spin qubits in silicon quantum dots offer a promising platform for a quantum computer as they have a long coherence time and scalability. The charge sensing technique plays an essential role in reading out the spin qubit as well as tuning…
Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the $ab$ $initio$ framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based…
In this work, we study hole transport in a planar silicon metal-oxide-semiconductor based double quantum dot. We demonstrate Pauli spin blockade in the few hole regime and map the spin relaxation induced leakage current as a function of…
Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting…
As quantum computers approach the fault tolerance threshold, diagnosing and characterizing the noise on large scale quantum devices is increasingly important. One of the most important classes of noise channels is the class of Pauli…
Employing spins in quantum dots for fault-tolerant quantum computing in large-scale qubit arrays with on-chip control electronics requires high-fidelity qubit operation at elevated temperature. This poses a challenge for single spin…
The simulation of charge transport in ultra-scaled electronic devices requires the knowledge of the atomic configuration and the associated potential. Such "atomistic" device simulation is most commonly handled using a tight-binding…
Quantum Machine Learning models typically require expensive on-chip training procedures and often lack efficient gradient estimation methods. By employing Pauli propagation, it is possible to derive a symbolic representation of observables…
Spin wave computing device where an algorithm can be encoded by recording a corresponding magnetization pattern onto a hard magnetic material was previously proposed1 and a particular implementation of a vector-matrix algorithm was…
The challenge to achieve practical quantum computing considering current hardware size and gate fidelity is the sensitivity to errors and noise. Recent work has shown that by learning the underlying noise model capturing qubit cross-talk,…
Quantum signal processing (QSP), which enables systematic polynomial transformations on quantum data through sequences of qubit rotations, has emerged as a fundamental building block for quantum algorithms and data re-uploading quantum…