Auto-tuned quantum dots in silicon as a candidate platform for scalable quantum computing and quantum neuromorphic devices

Spin qubits in gate defined quantum dots in silicon present a robust architecture for quantum information processing due to long coherence times, tune-ability, and scalability. However, the fabrication of such qubits leads to strong device variability, making it challenging to scale up the number of qubits for quantum computing applications. To address this challenge, we present our new device design and an automation protocol development to tune individual qubits. This automation protocol works to counteract device variability, resulting in greater scalability and versatility for the device. From this versatility we will explore the potential of using these auto-tuned quantum dots as quantum neuromorphic devices by engineering a non-Markovian bath for one or more qubits.