arXiv subjects

Hugh Churchill

Publications and source records attributed to Hugh Churchill.

22 records · Page 2Linked to original sources

arXiv2023

Fairness in Visual Clustering: A Novel Transformer Clustering Approach

Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal. This is because of the limitation of large-scale balanced data with well-annotated labels for sensitive or protected attributes. In this paper, we first evaluate demographic bias in deep clustering models from the perspective of cluster purity, which is measured by the ratio of positive samples within a cluster to their correlation degree. This measurement is adopted as an indication of demographic bias. Then, a novel loss function is introduced to encourage a purity consistency for all clusters to maintain the fairness aspect of the learned clustering model. Moreover, we present a novel attention mechanism, Cross-attention, to measure correlations between multiple clusters, strengthening faraway positive samples and improving the purity of clusters during the learning process. Experimental results on a large-scale dataset with numerous attribute settings have demonstrated the effectiveness of the proposed approach on both clustering accuracy and fairness enhancement on several sensitive attributes.

cs.CV↗

arXiv2023

Voltage Activated Parametric Entangling Gates on Gatemons

We describe the generation of entangling gates on superconductor-semiconductor hybrid qubits by ac voltage modulation of the Josephson energy. Our numerical simulations demonstrate that the unitary error can be below $10^{-5}$ in a variety of 75-ns-long two-qubit gates (CZ, $i$SWAP, and $\sqrt{i\mathrm{SWAP}}$) implemented using parametric resonance. We analyze the conditional ZZ phase and demonstrate that the CZ gate needs no further phase correction steps, while the ZZ phase error in SWAP-type gates can be compensated by choosing pulse parameters. With decoherence considered, we estimate that qubit relaxation time needs to exceed $70μ\mathrm{s}$ to achieve the 99.9% fidelity threshold.

quant-ph↗

arXiv2021

Tuning supercurrent in Josephson field effect transistors using h-BN dielectric

The transparent interface in epitaxial Al-InAs heterostructures provides an excellent platform for potential advances in mesoscopic and topological superconductivity. Semiconductor-based Josephson Junction Field Effect Transistors (JJ-FETs) fabricated on these heterostructures have a metallic gate that tunes the supercurrent. Here we report the fabrication and measurement of gate-tunable Al-InAs JJ-FETs in which the gate dielectric in contact with the InAs is produced by mechanically exfoliated hexagonal boron nitride (h-BN) followed by dry transfer using a van der Waals-mediated pick up process. We discuss the fabrication process that enables compatibility between layered material transfer and Al-InAs heterostructures to avoid chemical reactions and unintentional doping that could affect the characteristics of the JJ-FET. We achieve full gate-tunablity of supercurrent by using only 5~nm thick h-BN flakes. We contrast our process with devices fabricated using a conventional AlO$_{\rm x}$ gate dielectric and show that h-BN could be an excellent competing dielectric for JJ-FET devices. We observe that the product of normal resistance and critical current, I$_{\rm c}$R$_{\rm n}$, is comparable for both types of devices, but strikingly higher R$_{\rm n}$ for the h-BN-based devices indicating that the surface is doped less compared to AlO$_{\rm x}$ gate dielectric.

cond-mat.mes-hall↗

arXiv2020

Defining Quantum Neural Networks via Quantum Time Evolution

This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computationally expensive. For example, in image classification, representing an image pixel by pixel using classical information requires an enormous amount of computational memory resources. Hence, exploring methods to represent images in a different paradigm of information is important. Quantum Neural Networks (QNNs) have been explored for over 20 years. The current forefront work based on Variational Quantum Circuits is specifically defined for the Continuous Variable (CV) Model of quantum computers. In this work, a model is proposed which is defined at a more fundamental level and hence can be inherited by any variants of quantum computing models. This work also presents a quantum backpropagation algorithm to train our QNN model and validate this algorithm on the MNIST dataset on a quantum computer simulation.

cs.LG↗