CMOS Quantum Computing: Toward A Quantum Computer System-on-Chip

Quantum computing is experiencing the transition from a scientific to an engineering field with the promise to revolutionize an extensive range of applications demanding high-performance computing. Many implementation approaches have been pursued for quantum computing systems, where currently the main streams can be identified based on superconducting, photonic, trapped-ion, and semiconductor qubits. Semiconductor-based quantum computing, specifically using CMOS technologies, is promising as it provides potential for the integration of qubits with their control and readout circuits on a single chip. This paves the way for the realization of a large-scale quantum computing system for solving practical problems. In this paper, we present an overview and future perspective of CMOS quantum computing, exploring developed semiconductor qubit structures, quantum gates, as well as control and readout circuits, with a focus on the promises and challenges of CMOS implementation

Scalable and high-sensitivity readout of silicon quantum devices

Quantum computing is predicted to provide unprecedented enhancements in computational power. A quantum computer requires implementation of a well-defined and controlled quantum system of many interconnected qubits, each defined using fragile quantum states. The interest in a spin-based quantum computer in silicon stems from demonstrations of very long spin-coherence times, high-fidelity single spin control and compatibility with industrial mass-fabrication. Industrial scale fabrication of the silicon platform offers a clear route towards a large-scale quantum computer, however, some of the processes and techniques employed in qubit demonstrators are incompatible with a dense and foundry-fabricated architecture. In particular, spin-readout utilises external sensors that require nearly the same footprint as qubit devices. In this thesis, improved readout techniques for silicon quantum devices are presented and routes towards implementation of a scalable and high-sensitivity readout architecture are investigated. Firstly, readout sensitivity of compact gate-based sensors is improved using a high-quality factor resonator and Josephson parametric amplifier that are fabricated separately from quantum dots. Secondly, an integrated transistor-based control circuit is presented using which sequential readout of two quantum dot devices using the same gate-based sensor is achieved. Finally, a large-scale readout architecture based on random-access and frequency multiplexing is introduced. The impact of readout circuit footprint on readout sensitivity is determined, showing routes towards integration of conventional circuits with quantum devices in a dense architecture, and a fault-tolerant architecture based on mediated exchange is introduced, capable of relaxing the limitations on available control circuit footprint per qubit. Demonstrations are based on foundry-fabricated transistors and few-electron quantum dots, showing that industry fabrication is a viable route towards quantum computation at a scale large enough to begin addressing the most challenging computational problems

Cryogenic Control Beyond 100 Qubits

Quantum computation has been a major focus of research in the past two decades, with recent experiments demonstrating basic algorithms on small numbers of qubits. A large-scale universal quantum computer would have a profound impact on science and technology, providing a solution to several problems intractable for classical computers. To realise such a machine, today's small experiments must be scaled up, and a system must be built which provides control and measurement of many hundreds of qubits. A device of this scale is challenging: qubits are highly sensitive to their environment, and sophisticated isolation techniques are required to preserve the qubits' fragile states. Solid-state qubits require deep-cryogenic cooling to suppress thermal excitations. Yet current state-of-the-art experiments use room-temperature electronics which are electrically connected to the qubits. This thesis investigates various scalable technologies and techniques which can be used to control quantum systems. With the requirements for semiconductor spin-qubits in mind, several custom electronic systems, to provide quantum control from deep cryogenic temperatures, are designed and measured. A system architecture is proposed for quantum control, providing a scalable approach to executing quantum algorithms on a large number of qubits. Control of a gallium arsenide qubit is demonstrated using a cryogenically operated FPGA driving custom gallium arsenide switches. The cryogenic performance of a commercial FPGA is measured, as the main logic processor in a cryogenic quantum control system, and digital-to-analog converters are analysed during cryogenic operation. Recent work towards a 100-qubit cryogenic control system is shown, including the design of interconnect solutions and multiplexing circuitry. With qubit fidelity over the fault-tolerant threshold for certain error correcting codes, accompanying control platforms will play a key role in the development of a scalable quantum machine

Coherent Transport of Quantum States by Deep Reinforcement Learning

Some problems in physics can be handled only after a suitable \textit{ansatz }solution has been guessed. Such method is therefore resilient to generalization, resulting of limited scope. The coherent transport by adiabatic passage of a quantum state through an array of semiconductor quantum dots provides a par excellence example of such approach, where it is necessary to introduce its so called counter-intuitive control gate ansatz pulse sequence. Instead, deep reinforcement learning technique has proven to be able to solve very complex sequential decision-making problems involving competition between short-term and long-term rewards, despite a lack of prior knowledge. We show that in the above problem deep reinforcement learning discovers control sequences outperforming the \textit{ansatz} counter-intuitive sequence. Even more interesting, it discovers novel strategies when realistic disturbances affect the ideal system, with better speed and fidelity when energy detuning between the ground states of quantum dots or dephasing are added to the master equation, also mitigating the effects of losses. This method enables online update of realistic systems as the policy convergence is boosted by exploiting the prior knowledge when available. Deep reinforcement learning proves effective to control dynamics of quantum states, and more generally it applies whenever an ansatz solution is unknown or insufficient to effectively treat the problem.Comment: 5 figure