Using the properties of quantum superposition, we propose a quantum classification algorithm to efficiently perform multi-class classification tasks, where the training data are loaded into parameterized operators which are applied to the basis of the quantum state in quantum circuit composed by \emph{sample register} and \emph{label register}, and the parameters of quantum gates are optimized by a hybrid quantum-classical method, which is composed of a trainable quantum circuit and a gradient-based classical optimizer. After several quantum-to-class repetitions, the quantum state is optimal that the state in \emph{sample register} is the same as that in \emph{label register}. %A structure of loading data many times is performed as a quantum version of neural network to improve the expression ability of quantum circuit. For a classification task of
L-class, the analysis shows that the space and time complexity of the quantum circuit are
O(L∗logL) and
O(logL), respectively. The numerical simulation results of 2-class task and 5-class task show that the proposed algorithm has a higher classification accuracy, faster convergence and higher expression ability. The classification accuracy and the speed of converging can also be improved by increasing the number times of applying multi-qubit controlled operators on the quantum circuit, especially for multiple classes classification.
One of the key features required to realize fault-tolerant quantum computation is the robustness of quantum gates against errors. Since geometric quantum gate is naturally insensitivity to noise, it appears to be a promising routine to achieve high-fidelity, robust quantum gates. The implementation of geometric quantum gate however faces some troubles such as its complex interaction among multiple energy levels. Moreover, traditional geometric schemes usually take more time than equivalent dynamical ones. Here, we experimentally demonstrate a geometric gate scheme with the time-optimal control (TOC) technique in a superconducting quantum circuit. With a transmon qubit and operations restricted to two computational levels, we implement a set of geometric gates which exhibit better robustness features against control errors than the dynamical counterparts. The measured fidelities of TOC X gate and X/2 gate are 99.81% and 99.79% respectively. Our work shows a promising routine toward scalable fault-tolerant quantum computation.
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multidimensional parameter space. Dubbed the "ray-based classification (RBC) framework," we use this machine learning approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82% accuracy benchmark from the experimental implementation of image-based classification techniques from prior work, while reducing the number of measurement points needed by up to 70%. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward toward the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multiqubit regime, performs when tuning in the two-dimensional and three-dimensional parameter spaces defined by plunger and barrier gates that control the QDs. This work provides experimental validation of both efficient state identification and optimization with machine learning techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.