Codesign of Quantum Software and Hardware: Towards Scalable and Robust Quantum Computing System

Quantum computing is becoming a hot topic and is considered one of the most promising new computing paradigms, especially with its enormous potential in fields such as machine learning, finance, cryptography, and chemistry. However, current quantum computing is still in a very immature state, with significant challenges at every level from software to hardware. This dissertation explores how to help build scalable and robust quantum computing systems from the perspective of software and hardware co-design. Specifically, the dissertation is divided into two main directions: 1) Paradigm shift from gate-level to pulse-level and cross-layer co-design; and 2) Establishing a deeper level of classical-quantum cooperation. In the first direction, this dissertation discussed certain sophisticated quantum operations that may derive substantial benefits from circumventing the conventional decomposition into basic gates at the circuit level. Instead, these operations can be more effectively implemented directly at the physical layer. The advent of applications such as quantum simulations and quantum machine learning indicate that the classic "gate-to-circuit-to-program" paradigm may no longer serve as the most efficient or intuitive approach for quantum design. Exploring designs at the pulse level, as opposed to the gate level, could offer significant advantages. Utilizing quantum pulses over quantum gates has the potential to provide enhanced flexibility, superior fidelity, and greater scalability, along with the capacity for real-time adjustments. In the second direction, a key focus is how to deeply promote the integration of classical machine learning or optimization algorithms with quantum algorithms. Given the current scarcity and high cost of quantum computing resources, it's challenging to conduct large-scale experiments. Therefore, we discuss two hybrid classical-quantum computing frameworks to solve this challenge: one involves sacrificing some classical computing resources to preheat quantum algorithms, and the other divide problems into parts solved by classical computers and parts solved by quantum computers.