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Quantum computation will likely provide significant advantages relative to classical architectures for certain computational problems in number theory and physics, and potentially in other areas such as optimization and machine learning. While some key theoretical and engineering problems remain to be solved, experimental advances in recent years have demonstrated the first beyond-classical quantum computation as well as the first experiments in error-corrected quantum computation. In this thesis, we focus on quantum computers with around one hundred qubits that can implement around one thousand operations, the so-called noisy-intermediate scale quantum (NISQ) regime or kilo-scale quantum (KSQ) regime, and develop algorithms tailored to these devices as well as techniques for error mitigation that require significantly less overhead than fault-tolerant quantum computation. In the first part, we develop quantum algorithms for diagonalizing quantum states (density matrices) and compiling quantum circuits. These algorithms use a quantum computer to evaluate a cost function which is classically hard to compute and a classical computer to adjust parameters of an ansatz circuit, similar to the variational principle in quantum mechanics and other variational quantum algorithms for chemistry and optimization. In the second part, we extend an error mitigation technique known as zero-noise extrapolation and introduce a new framework for error mitigation which we call logical shadow tomography. In particular, we adapt zero-noise extrapolation (ZNE) to the gate model and introduce new methods for noise scaling and (adaptive) extrapolation. Further, we analyze ZNE in the presence of time-correlated noise and experimentally show ZNE increases the effective quantum volume of various quantum computers. Finally, we develop a simple framework for error mitigation that enables (the composition of) several error mitigation techniques with significantly fewer resources than prior methods, and numerically show the advantages of our framework.
This volume presents papers on the topics covered at the National Academy of Engineering's 2018 US Frontiers of Engineering Symposium. Every year the symposium brings together 100 outstanding young leaders in engineering to share their cutting-edge research and innovations in selected areas. The 2018 symposium was held September 5-7 and hosted by MIT Lincoln Laboratory in Lexington, Massachusetts. The intent of this book is to convey the excitement of this unique meeting and to highlight innovative developments in engineering research and technical work.
Quantum computers are hypothetical quantum information processing (QIP) devices that allow one to store, manipulate, and extract information while harnessing quantum physics to solve various computational problems and do so putatively more efficiently than any known classical counterpart (5). Physical objects as they are, QIP devices are subject to the laws of physics. No doubt, the application of these laws is error-free, but noise - be it external influences or hardware imprecisions - can sometimes cause a mismatch between what the QIP device is supposed to do and what it actually does. In recent years the elimination of noise that result from external disturbances or from imperfect gates has become the "holy grail" within the quantum computing community, and a worldwide quest for a large scale, fault-tolerant, and computationally superior QIP device is currently taking place. Whether such machines are possible is an exciting open question, yet the debate on their feasibility has been so far rather ideological in character (45) (66)(110) (162). Remarkably, philosophers of science have been mostly silent about it: common wisdom has it that philosophy should not intervene in what appears to be (and is also presented as) an engineering problem, and besides, the mathematics employed in the theory of fault-tolerant quantum error correction (FTQEC henceforth) is rather daunting. It turns out, however, that behind this technical veil the central issues at the heart of the debate are worthy of philosophical analysis and, moreover, bear strong similarities to the conceptual problems that have been saturating a field quite familiar to philosophers, namely the foundations of statistical mechanics (SM henceforth). Reconstructing the debate on FTQEC with statistical mechanical analogies, this book aims to introduce it to readership outside the quantum computing community, and to take preliminary steps towards making it less ideological and mor
Quantum mechanics, the subfield of physics that describes the behavior of very small (quantum) particles, provides the basis for a new paradigm of computing. First proposed in the 1980s as a way to improve computational modeling of quantum systems, the field of quantum computing has recently garnered significant attention due to progress in building small-scale devices. However, significant technical advances will be required before a large-scale, practical quantum computer can be achieved. Quantum Computing: Progress and Prospects provides an introduction to the field, including the unique characteristics and constraints of the technology, and assesses the feasibility and implications of creating a functional quantum computer capable of addressing real-world problems. This report considers hardware and software requirements, quantum algorithms, drivers of advances in quantum computing and quantum devices, benchmarks associated with relevant use cases, the time and resources required, and how to assess the probability of success.
Quantum computers have the potential to solve several interesting problems in polynomial time for which no polynomial time classical algorithms have been found. However, one of the major challenges in building quantum devices is that quantum systems are very sensitive to noise arising from undesired interactions with the environment. Noise can lead to errors which can corrupt the results of the computation. Quantum error correction is one way to mitigate the effects of noise arising in quantum devices. With a plethora of quantum error correcting codes that can be used in various settings, one of the main challenges of quantum error correction is understanding how well various codes perform under more realistic noise models that can be observed in experiments. This thesis proposes a new decoding algorithm which can optimize threshold values of error correcting codes under different noise models. The algorithm can be applied to any Markovian noise model. Further, it is shown that for certain noise models, logical Clifford corrections can further improve a code's threshold value if the code obeys certain symmetries. Since gates and measurements cannot in general be performed with perfect precision, the operations required to perform quantum error correction can introduce more errors into the system thus negating the benefits of error correction. Fault-tolerant quantum computing is a way to perform quantum error correction with imperfect operations while retaining the ability to suppress errors as long as the noise is below a code's threshold. One of the main challenges in performing fault-tolerant error correction is the high resource requirements that are needed to obtain very low logical noise rates. With the use of flag qubits, this thesis develops new fault-tolerant error correction protocols that are applicable to arbitrary distance codes. Various code families are shown to satisfy the requirements of flag fault-tolerant error correction. We also provide circuits using a constant number of qubits for these codes. It is shown that the proposed flag fault-tolerant method uses fewer qubits than previous fault-tolerant error correction protocols. It is often the case that the noise afflicting a quantum device cannot be fully characterized. Further, even with some knowledge of the noise, it can be very challenging to use analytic decoding methods to improve the performance of a fault-tolerant scheme. This thesis presents decoding schemes using several state of the art machine learning techniques with a focus on fault-tolerant quantum error correction in regimes that are relevant to near term experiments. It is shown that even in low noise rate regimes and with no knowledge of the noise, noise can be further suppressed for small distance codes. Limitations of machine learning decoders as well as the classical resources required to perform active error correction are discussed. In many cases, gate times can be much shorter than typical measurement times of quantum states. Further, classical decoding of the syndrome information used in quantum error correction to compute recovery operators can also be much slower than gate times. For these reasons, schemes where error correction can be implemented in a frame (known as the Pauli frame) have been developed to avoid active error correction. In this thesis, we generalize previous Pauli frame schemes and show how Clifford frame error correction can be implemented with minimal overhead. Clifford frame error correction is necessary if the logical component of recovery operators were chosen from the Clifford group, but could also be used in randomized benchmarking schemes.
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This book targets computer scientists and engineers who are familiar with concepts in classical computer systems but are curious to learn the general architecture of quantum computing systems. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.
Many-body nuclear physics is the bridge that takes us from the fundamental laws governing individual nucleons to understanding how groups of them interact together to form the nuclei that lie at the heart of all atoms-the building blocks of our universe. Many powerful techniques of classical computation have been developed over the years in order to study ever more complex nuclear systems. However, we seem to be approaching the limits of such classical techniques as the complexity of many-body quantum systems grows exponentially. Yet, the recent development of quantum computers offers one hope as they are predicted to provide a significant advantage over classical computers when tackling problems such as the quantum many-body problem. In this thesis, we focus on developing and applying algorithms to tackle various many-body nuclear physics problems that can be run on the near-term quantum computers of the current noisy intermediate-scale quantum (NISQ) era. As these devices are small and noisy, we focus our algorithms on various many-body toy models in order to gain insight and create a foundation upon which future algorithms will be built to tackle the intractable problems of our time. In the first part, we tailor current quantum algorithms to efficiently run on NISQ devices and apply them to three pairing models of many-body nuclear physics, the Lipkin model, the Richardson pairing model, and collective neutrino oscillations. For the first two models, we solve for the ground-state energy while for the third, we simulate the time evolution and characterize the entanglement. In the second part, we develop novel algorithms to increase the efficiency and applicability of current algorithms on NISQ devices. These include an algorithm that compresses circuit depth to allow for less noisy computation and a variational method to prepare an important class of quantum states. Error mitigation techniques used to improve the accuracy of results are also discussed. All together, this work provides a road map for applications of the quantum computers of tomorrow to solve what nuclear phenomena mystify us today.
"The current state of quantum computers is characterized by its limited resources and high noise levels. These are known as Noisy Intermediate Scale Quantum Computing (NISQ). Reduction of noise is addressed at the technology level, while noise mitigation strategies have been proposed through the compilation process of quantum circuits. The compilation process entails a number of steps that are computationally intensive and scale poorly as the problems grow in size and resource usage. This paper addresses the problems associated with noise by proposing a noise aware qubit routing algorithm. This algorithm attempts to improve accuracy of circuits by using SWAP gates attempt to avoid links with high error rates. This differs from existing algorithms which try to minimize the amount of SWAP gates used. In addition, multiple metrics are evaluated for Qiskit's routing algorithms. The proposed algorithm improves the accuracy against circuits compiled using Qiskit’s basic routing algorithm, and against other more sophisticated routing algorithms, depending on the application circuit. Other metrics being considered, the routing algorithm also demonstrates to have minimal computational cost when compared to other approaches. Lastly, it is shown that different circuits benefit from different routing algorithms."--Abstract.