Material-Driven Optimization of Transmon Qubits for Scalable and Efficient Quantum Architectures
Jonnalagadda Gayatri, S.Saravana Veni

TL;DR
This paper presents a material-driven, simulation-based approach to optimize transmon qubits for scalable quantum computing, focusing on material effects on coherence and device performance.
Contribution
It introduces an integrated framework combining material analysis, simulation, and circuit design to enhance transmon qubit scalability and reliability.
Findings
Material choice significantly impacts qubit coherence.
Simulation identifies optimal materials and designs.
Design iteration improves qubit energy properties.
Abstract
One of the most crucial steps in creating practical quantum computers is designing scalable and efficient superconducting qubits. Coherence times, connections between individual qubits, and reduction of environmental noise are critical factors in the success of these qubits. Because they can be lithographically fabricated and are less sensitive to charge noise, superconducting qubits, especially those based on the Transmon architecture, have emerged as top contenders for scalable platforms. In this work, we use a combination of design iteration, material analysis, and simulation to tackle the superconducting qubit optimization challenge. We created transmon-based layouts for 4 qubits and 8 qubits using Qiskit Metal and conducted an individual analysis for each qubit. We investigated anharmonicity and extracted eigenfrequencies, computing participation ratios across several design…
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