Continuous-time quantum computing

Quantum computation using continuous-time evolution under a natural hardware Hamiltonian is a promising near- and mid-term direction toward powerful quantum computing hardware. Continuous-time quantum computing (CTQC) encompasses continuous-time quantum walk computing (QW), adiabatic quantum computing (AQC), and quantum annealing (QA), as well as other strategies which contain elements of these three. While much of current quantum computing research focuses on the discrete-time gate model, which has an appealing similarity to the discrete logic of classical computation, the continuous nature of quantum information suggests that continuous-time quantum information processing is worth exploring. A versatile context for CTQC is the transverse Ising model, and this thesis will explore the application of Ising model CTQC to classical optimization problems. Classical optimization problems have industrial and scientific significance, including in logistics, scheduling, medicine, cryptography, hydrology and many other areas. Along with the fact that such problems often have straightforward, natural mappings onto the interactions of readily-available Ising model hardware makes classical optimization a fruitful target for CTQC algorithms. After introducing and explaining the CTQC framework in detail, in this thesis I will, through a combination of numerical, analytical, and experimental work, examine the performance of various forms of CTQC on a number of different optimization problems, and investigate the underlying physical mechanisms by which they operate.Open Acces

Protected ground states in short chains of coupled spins in circuit quantum electrodynamics

The two degenerate ground states of the anisotropic Heisenberg (XY) spin model of a chain of qubits (pseudo-spins) can encode quantum information, but their degree of protection against local perturbations is known to be only partial. We examine the properties of the system in the presence of non-local spin-spin interactions, possibly emerging from the quantum electrodynamics of the device. We find a phase distinct from the XY phase admitting two ground states which are highly protected against all local field perturbations, persisting across a range of parameters. In the context of the XY chain we discuss how the coupling between two ground states can be used to observe signatures of topological edge states in a small controlled chain of superconducting transmon qubits.Comment: 10 pages, 11 figure

Improved maximum-likelihood quantum amplitude estimation

Quantum amplitude estimation is a key subroutine in a number of powerful quantum algorithms, including quantum-enhanced Monte Carlo simulation and quantum machine learning. Maximum-likelihood quantum amplitude estimation (MLQAE) is one of a number of recent approaches that employ much simpler quantum circuits than the original algorithm based on quantum phase estimation. In this article, we deepen the analysis of MLQAE to put the algorithm in a more prescriptive form, including scenarios where quantum circuit depth is limited. In the process, we observe and explain particular ranges of `exceptional' values of the target amplitude for which the algorithm fails to achieve the desired precision. We then propose and numerically validate a heuristic modification to the algorithm to overcome this problem, bringing the algorithm even closer to being useful as a practical subroutine on near- and mid-term quantum hardware.Comment: 22+2 pages, 10 figure

Using copies can improve precision in continuous-time quantum computing

In the quantum optimisation setting, we build on a scheme introduced by Young et al (2013 Phys. Rev. A 88 062314), where physical qubits in multiple copies of a problem encoded into an Ising spin Hamiltonian are linked together to increase the logical system's robustness to error. We introduce several innovations that improve the error suppression of this scheme under a special model of control noise, designed to understand how limited precision could be overcome. First, we note that only one copy needs to be correct by the end of the computation, since solution quality can be checked efficiently. Second, we find that ferromagnetic links do not generally help in this 'one correct copy' setting, but anti-ferromagnetic links do help on average, by suppressing the chance of the same error being present on all of the copies. Third, we find that minimum-strength anti-ferromagnetic links perform best, by counteracting the spin-flips induced by the errors. We have numerically tested our innovations on small instances of spin glasses from Callison et al (2019 New J. Phys.21 123022), and we find improved error tolerance for three or more copies in configurations that include frustration. Interpreted as an effective precision increase, we obtain several extra bits of precision on average for three copies connected in a triangle. This provides proof-of-concept of a method for scaling quantum annealing beyond the precision limits of hardware, a step towards fault tolerance in this setting

Using Categorial Grammar to Label Translation Rules

Adding syntactic labels to synchronous context-free translation rules can improve performance, but labeling with phrase structure constituents, as in GHKM (Galley et al., 2004), excludes potentially useful translation rules. SAMT (Zollmann and Venugopal, 2006) introduces heuristics to create new non-constituent labels, but these heuristics introduce many complex labels and tend to add rarely-applicable rules to the translation grammar. We introduce a labeling scheme based on categorial grammar, which allows syntactic labeling of many rules with a minimal, well-motivated label set. We show that our labeling scheme performs comparably to SAMT on an Urdu–English translation task, yet the label set is an order of magnitude smaller, and translation is twice as fast

Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor

We present progress on Joshua, an opensource decoder for hierarchical and syntaxbased machine translation. The main focus is describing Thrax, a flexible, open source synchronous context-free grammar extractor. Thrax extracts both hierarchical (Chiang, 2007) and syntax-augmented machine translation (Zollmann and Venugopal, 2006) grammars. It is built on Apache Hadoop for efficient distributed performance, and can easily be extended with support for new grammars, feature functions, and output formats.

Learning to Translate with Products of Novices: A Suite of Open-Ended Challenge Problems for Teaching MT

Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-ofthe-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.