Quantum computation has advanced to the point where system-level solutions can help close the gap between emerging quantum technologies and real-world computing requirements. Empirical studies of practical quantum architectures are just beginning to appear in the literature. Elementary architectural concepts are still lacking: How do we provide quantum storage, data paths, classical control circuits, parallelism, and system integration? And, crucially, how can we design architectures to reduce error-correction overhead? The authors describe a proposed architecture that uses code teleportation, quantum memory refresh units, dynamic compilation of quantum programs, and scalable error correction to achieve system-level efficiencies. They assert that their work indicates the underlying technology's reliability is crucial; practical architectures will require quantum technologies with error rates between 10/sup -6/ and 10/sup -9/.
Quantum computation has advanced to the point where system-level solutions can help close the gap between emerging quantum technologies and real-world computing requirements. Empirical studies of practical quantum architectures are just beginning to appear in the literature. Elementary architectural concepts are still lacking: How do we provide quantum storage, data paths, classical control circuits, parallelism, and system integration? And, crucially, how can we design architectures to reduce error-correction overhead? The authors describe a proposed architecture that uses code teleportation, quantum memory refresh units, dynamic compilation of quantum programs, and scalable error correction to achieve system-level efficiencies. They assert that their work indicates the underlying technology's reliability is crucial; practical architectures will require quantum technologies with error rates between 10/sup -6/ and 10/sup -9/.
As the complexity of quantum computers increases, typical architectural issues such as communication, layout, and efficient design need to be addressed. This document addresses some basic architectural ideas for quantum computers.
To be useful in reasonable calculations, quantum states will need constant error correction. This need guides how best to lay out physical components to minimize error-correction overhead. I propose a layout that minimizes communication overhead, and discuss the implementation of error-correction algorithms on that layout. I compare the cost overhead of purely local communication to communication by teleportation, and calculate the break-even point at which teleportation becomes efficient.
Additionally, the overhead of error correction can be reduced by using a memory hierarchy to more efficiently store data not currently being computed on. The main requirement is the same as for a classical computer's cache: temporal locality. I show that an important quantum routine can be rearranged to take advantage of a small quantum memory cache, and compute the achieved savings.
In a quantum system, complex operators are built up from the basic operators allowed by a given technology model. I show that the set of operators required to implement any complex operator in an error-corrected system can be approximated to arbitrary precision, given two elementary operators. I give results for all the operators in the set.
Finally, I examine methods to initialize a quantum system. Quantum operators are reversible, so data cannot simply be written over. Instead, initialization entails compressing the entropy of a set of quantum bits into a small subset of those bits, leaving the rest of the bits in a known, non-random state. I examine three such compression algorithms. The best of these itself requires a pool of known states, and so cannot be used directly. The other two algorithms, however, produce less-than-optimal results. I explore why they produce suboptimal results, and propose that one of the suboptimal algorithms be used to compress the entropy of a subsystem, using the resulting known state to run the optimal algorithm.
Quantum computing is at an inflection point, where 127-qubit (quantum bit) machines are deployed, and 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 design architectural interfaces that 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 techniques that break traditional abstractions, including compiling programs directly to analog control pulses, adapting programs for machine variations, computing with ternary quantum bits, efficient error correction using 2.5D structures, and designing just enough tunability in qubits to avoid crosstalk.
Quantum computers may solve some problems far beyond the reach of classical digital computers. However, emerging quantum systems are typically noisy and difficult to control. These noises create significant difficulties for the practical usage of quantum systems, leaving a substantial gap between the requirements of quantum applications and the realities of noisy devices. Bridging this gap is crucial – the dissertation shows that the system software can adapt to the constraints of large applications and noisy hardware. In particular, this dissertation combines systems techniques at three main levels: i. Gate-level noise mitigation, which enhances the robustness of quantum processors through coordinated control instructions that reduce errors; ii. Program-level quantum memory management, which addresses problems of qubit allocation throughout a quantum program and implements automated tools to selectively reuse qubits, much like in garbage collection for classical programs; iii. Architecture-level quantum error correction with low overhead, which seeks to provide correctness guarantees for quantum applications by encoding quantum bits so that errors can be detected and corrected, analogous to classical error-correcting codes.
Quantum computing is an emerging field that offers revolutionary and exciting new ways to process, store, and transport information. Moreover, realizing large-scale quantum systems requires advances in science and engineering that are now very close to reality. Such systems will need many classical and quantum parts that must fit together into one architecture, in which each component plays an integral role over the course of an application execution. Therefore, it is important that we draw upon the experiences gained from traditional architectures, to develop a model for a large-scale quantum system that provides the methodology necessary for the many different components to work together.
In this thesis, I describe the design of the Quantum Logic Array (QLA) architecture, which tackles critical scalability issues such as the cost of error correction and distributing data over large-distances. To accurately model the performance of the QLA architecture—the ion-trap technology is used since every component necessary for universal quantum computation has been demonstrated in the laboratory. However, due to its design approach for maximizing parallelism, the QLA system suffers from large area overhead. To combat the area problem, I describe an architecture that uses the concept of hardware specialization employed in traditional architectures. The new architecture is based on the QLA model, but is divided into separately optimized quantum memory hierarchy and logic specific regions. The result is a scalable system design that exploits the available parallelism to balance both quantum and classical resources, while both reducing the area of the chip and increasing its performance.
Quantum computing has the potential to solve complex problems that are outside the scope of classical systems, making it a standard shifter. Current devices, which are considered as Noisy Intermediate-Scale Quantum (NISQ) systems, are insufficient in their practical utility due to hardware limitations, error rates, and qubit counts. The dynamic growths wanted to exceed the NISQ era and grow scalable quantum algorithms and architectures that can achieve useful quantum benefit in real-world applications are reviewed in this research. This research gives a comprehensive examination of the complications in scaling quantum systems, adjacent resource efficacy, qubit connectivity, and error correction. In addition to connecting architectural innovations like modular quantum computing, error-resilient circuit design, and distributed quantum systems, the paper provides new hybrid quantum-classical algorithms planned to exploit performance in noisy environments. Results from benchmarking in arenas like machine learning, optimization, and quantum chemistry show how these methods can provide short-term quantum benefit. Lastly, this paper proposes a research roadmap for quantum computing that highlights the requirement of co-designing algorithms, software, and hardware. The goal of this effort is to straighten the progress of quantum systems that can resolve problems in the real world at scale by addressing the inadequacies of NISQ devices and emphasizing scalable solutions.