Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting

Abstract

In this paper, we report the use of a superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications. We first describe the realization and characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, such as the dispersive shift and the qubit anharmonicity. We then report on a Quantum Machine Learning application implemented on a single-qubit device to fit the u-quark parton distribution function of the proton. In the final section of the manuscript, we present a new microwave photon detection scheme based on two qubits coupled to the same 3D resonator. This could in principle decrease the dark count rate, favoring applications like axion dark matter searches.

Optimizing Josephson Junction Reproducibility in 30 kV E-Beam Lithography: An Analysis of Backscattered Electron Distribution

“…Various groups have developed distinct strategies to enhance the reproducibility of Josephson junctions using low-energy 30 kV electron beam lithography. These improvements primarily focus on variations in junction geometry and the employment of different resist stacks [18,19], which have facilitated the fabrication of superconducting qubits with relaxation and coherence times ranging from hundreds of nanoseconds to tens of microseconds [20][21][22] for diverse applications. Despite these advancements, the underlying mechanisms contributing to the enhanced reproducibility of the junctions remain ambiguous.…”

Multi-variable integration with a variational quantum circuit

“…The rapid development of quantum technologies leads us to believe that quantum circuits and QML tools can be exploited to improve on the performance, despite the constraints imposed by the current era of limited near-intermediate scale quantum [31] (NISQ) devices. In particular, we note quite some interest on the field of high energy physics where many new algorithms are being developed and tested leading to a very robust ecosystem of quantum computing tools focusing on particle physics [32][33][34][35][36][37][38][39] This paper is structured as follows, we expose the method in section 2, after a brief introduction to QML and circuits derivative calculation respectively in Section 2.1 and 2.3. In section 3 we apply the method to two situations, a toy-model represented by a d-dimensional trigonometric function and a real-life scenario motivated by particle physics.…”