As classical computers have revolutionised the 20th century, quantum computing could do the same in the 21st. For quantum supremacy, quantum error correcting codes will require an estimated million to billions of qubits. Regarding controlling and reading every qubit, classical electronics must be as close to the quantum processor as possible to necessitate active feedback. Silicon quantum dots (QDs) are a promising candidate for quantum computation due to their scalability and co-integration with classical electronics. However, a less researched implementation is the operation of quantum dots as novel circuit components that can be integrated with the quantum processing unit. In this thesis, we focus on novel radio frequency (RF) implementations of silicon quantum dots as charge sensors and frequency multipliers.

A major concern with large arrays of silicon QDs is the requirement of complex tuning protocols due to device inhomogeneity, which is currently done heuristically. Therefore, for a fully integrated system, the first step will be to develop a scalable automated procedure to tune large QD arrays. To address this issue, we present an algorithm to extract the two most prominent slopes in a stability diagram, allowing users to control each quantum dot independently and counteract cross-capacitance effects. To extend to larger arrays, we outline how pre-calibrated QDs can act as charge sensors to measure otherwise undetectable transitions due to slow tunnel rates. In this line of research, we demonstrate how a quantum dot connected to a reservoir, known as a single electron box (SEB), can be operated as a sensitive charge sensor, allowing one to measure a neighbouring double quantum dot to the few-electron regime. We then perform state-of-the-art single-shot measurements across a Pauli spin-blockaded transition and obtain a fidelity of 99.2% in 5.6 μs, making this technology a viable competitor to the more commonly adopted single electron transistor (SET).

By exploiting the non-linearity of the quantum capacitance, we operate the same device as a frequency multiplier and showcase ideal frequency conversion up to ten times multiplication. This can be useful for high-frequency RF reflectometry set-ups and EDSR pulses for spin manipulation. By driving the system at lower RF frequencies, we detected slow tunnelling events that would have been otherwise unmeasurable. This phenomenon requires better understanding, as it will be invaluable in characterising quantum dots that are physically distant from a reservoir, which will commonly be the case for large 2D arrays.

There are ample research avenues to explore the operation of quantum dots as novel circuit components, such as parametric amplification, frequency mixers and multipliers, and charge sensors. In particular, the development of new quantum dot architectures where such circuit components are co-integrated with qubits. Operating such complex arrays will require automated tuning protocols, preferably running in parallel and using low-power devices, such as FPGAs, which can be run locally on the 4K plate. Due to the low power budget, such algorithms must be very efficient, making current deep neural networks unfeasible. This thesis provides valuable insights into the potential use of silicon quantum dots as cryo-CMOS components for quantum computing.