Resource-Efficient Quantum Computing by Breaking Abstractions
Yunong Shi, Pranav Gokhale, Prakash Murali, Jonathan M. Baker, Casey Duckering, Yongshan Ding, Natalie C. Brown, Christopher Chamberland, Ali Javadi-Abhari, Andrew W. Cross, David Schuster, Kenneth R. Brown, Margaret Martonosi, Frederic T. Chong
Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.
Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.
Abstract Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.
This short report explores the (non exhaustive) current state of quantum technologies, their potential applications, and the challenges that must be addressed to harness their full potential. In particular we will focus on the quantum computer architecture and its ecosystem. Such architecture represents a complex, multi-layered system that integrates quantum and classical components to enable the execution of quantum algorithms. This manuscript is then organized as follows : first we will introduce the quantum processing unit, the lowest layer of a quantum computer. Then we will progress from the lowest to the higher layer of the system architectures : measurement, circuit control, error correction and mitigation system, the quantum compiler and finally the software stack, with particular emphasis on the interactions between these components
There is a great deal of focus on how quantum computing as an accelerator differs from other traditional high-performance computing (HPC) resources, including accelerators like GPUs and field-programmable gate arrays. In classical computing, how to design the interfaces that connect the different layers of the software stack, from the applications and high-level programming language description, through compilers and schedulers, and down to the hardware and gate level, has been critical. Likewise, quantum computing’s interfaces enable access to quantum technology as a viable accelerator. From the ideation of the quantum application to the manipulation of the quantum chip, each interface has its challenges. In this article, we discuss the structure of this set of quantum interfaces, their many similarities to the traditional HPC compilation stack, and how these interfaces impact the potential of quantum computers as HPC accelerators.
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.
A quantum computing system naturally consists of two components, the software system and the hardware system. Quantum applications are programmed using the quantum software and then executed on the quantum hardware. However, the performance of existing quantum computing system is still limited. Solving a practical problem that is beyond the capability of classical computers on a quantum computer has not yet been demonstrated. In this review, we point out that the quantum software and hardware systems should be designed collaboratively to fully exploit the potential of quantum computing. We first review three related works, including one hardware-aware quantum compiler optimization, one application-aware quantum hardware architecture design flow, and one co-design approach for the emerging quantum computational chemistry. Then we discuss some potential future directions following the co-design principle.
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.