Recent breakthroughs in quantum computing hardware have brought us closer to realizing the dream of quantum computers accelerating domains such as materials discovery, chemistry, and beyond. Further shrinking the timeline for solving useful problems with quantum computing requires a flexible compilation stack that can adapt to emerging hardware. My research develops robust tools and techniques for optimizing quantum programs to be run on arbitrary quantum computing architectures.
Recent breakthroughs in quantum computing hardware have brought us closer to realizing the dream of quantum computers accelerating domains such as materials discovery, chemistry, and beyond. Further shrinking the timeline for solving useful problems with quantum computing requires a flexible compilation stack that can adapt to emerging hardware. My research develops robust tools and techniques for optimizing quantum programs to be run on arbitrary quantum computing architectures.
Quantum computing has emerged as a transformative frontier of computation. In recent years, quantum hardware has scaled at an unprecedented rate. As this momentum continues, the central challenge is shifting upward in the stack—from hardware-level feasibility toward system-level scalability. This thesis will focus on quantum computer architecture and compiler, introducing the challenges and opportunities to efficiently harness device capabilities and lower the demands on hardware technology, thereby accelerating timelines for practical quantum advantage.
Quantum computing is a revolutionary approach to computation that leverages the principles of quantum mechanics, enabling computers to process information in fundamentally different ways than classical computers. Unlike traditional bits, which represent data as either 0s or 1s, quantum bits, or qubits, can exist in multiple states simultaneously due to superposition. Additionally, qubits can be entangled, allowing them to be interconnected in ways that enhance computational power and efficiency. This unique behavior has the potential to run certain types of computation tasks much faster than classical computers can, marking a significant leap forward in technology. When building actual quantum computers to run practical quantum algorithms, one needs efficient ways to map the algorithms to the hardware components, similar to compiling classical algorithms onto CPU/GPU hardware. This thesis aims to tackle some of the important problems in the mapping process. First, can we address hardware errors and algorithm errors at the same time? Second, can we build a better circuit complexity metric to inspire better quantum architecture design, and can we design better quantum algorithms and compilation techniques using this improved metric? Lastly, can we use physics-level simulation results to inspire better circuit-level optimization techniques? We hope to provide positive answers to all three questions above through three separate architectural innovations.
Quantum circuits utilizing real time feedback techniques (such as active reset and mid-circuit measurement) are a powerful tool for NISQ-era quantum computing. Such techniques are crucial for implementing error correction protocols, and can reduce the resource requirements of certain quantum algorithms. Realizing these capabilities requires flexible, low-latency classical control. We have developed a custom FPGA-based processor architecture for QubiC, an open source platform for superconducting qubit control. Our architecture is distributed in nature, and consists of a bank of lightweight cores, each configured to control a small (1-3) number of signal generator channels. Each core is capable of executing parameterized control and readout pulses, as well as performing arbitrary control flow based on mid-circuit measurement results. We have also developed a modular compiler stack and domain-specific intermediate representation for programming the processor. Our representation allows users to specify circuits using both gate and pulse-level abstractions, and includes high-level control flow constructs (e.g. if-else blocks and loops). The compiler stack is designed to integrate with quantum software tools and programming languages, such as TrueQ, pyGSTi, and OpenQASM3. In this work, we will detail the design of both the processor and compiler stack, and demonstrate its capabilities with a quantum state teleportation experiment using transmon qubits at the LBNL Advanced Quantum Testbed.
In the past 20 years, many researchers shifted their focus to developing computers based on quantum mechanical phenomenon as current computers started to plateau in performance. Some problems such as integer factorization have been shown to perform much more efficiently on a quantum computer than on its classical counterpart. However, quantum computers will continue to remain the object of theoretical research unless it can be physically manifested, and quantum circuit optimization hopes to be a useful aid in turning the theory into a reality. My project looks at a possible approach to solving the issue of circuit optimization by incorporating classical algorithms as well as touching on another possible application of machine learning. The research and implementation is incomplete, but a glance at the material shows it has potential to be fairly effective.
Quantum computing has recently transitioned from a theoretical prediction to a nascent technology. Nevertheless, there is still a gap between the computational ability and reliability of current QIP technologies and the requirements of the first useful quantum computing applications. It will require tremendous efforts and investment to solve the engineering and research challenges to close the gap. Because of the uncertainties and difficulties in relying on hardware breakthroughs, it will also be crucial to close the gap using higher-level quantum optimizations and software-hardware co-design, which could maximally utilize noisy devices and potentially provide an accelerated pathway to real-world quantum computing applications. This thesis develops methods to explore the vast design space for highly optimized and reliable quantum computing architectures and software and focuses on the compilation, verification, and error correction aspects of the quantum computing stack. Specifically, the three main chapters in this thesis discuss, respectively, a direct-to-pulse compiler, a protocol to fault-tolerantly prepare approximate GKP (Gottesman-Kitave-Preskill) qubit states, and a verification framework that helps quantum programmers write provably correct compiler components through formal verification. Though touching many aspects of the stack and based on different technologies in physics and computer science, the majority of this thesis can be connected by a main theme --- improving the efficiency of existing quantum computing stacks by breaking the abstraction between layers in the quantum computing stack.
Quantum computing is at an inflection point, where 53-qubit (quantum bit) machines are deployed, 100qubit machines are just around the corner, and even 1000-qubit machines are perhaps only a few years away.These machines have the potential to fundamentally change our concept of what is computable and demonstrate practical applications in areas such as quantum chemistry, optimization, and quantum simulation.Yet a significant resource gap remains between practical quantum algorithms and real machines.A promising approach to closing this gap is to selectively expose to programming languages and compilers some of the key physical properties of emerging quantum technologies.I will describe some of our recent work that focuses on compilation techniques that break traditional abstractions, including compiling directly to analog control pulses, compiling for machine variations, and compiling with ternary quantum bits.