Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Toward an improved understanding of software change

Structural changes, including moving, renaming, merging and splitting are important design change decisions made by programmers. However, during the process of software evolution, this information often gets lost. Recovering instances of structural changes in the past, as well as understanding them, are essential for us to achieve a better understanding of how and why software changes. In this thesis, we propose an approach that helps to recover and understand the lost information of structural changes

Faster-than-Clifford Simulations of Entanglement Purification Circuits and Their Full-stack Optimization

Quantum Entanglement is a fundamentally important resource in Quantum Information Science; however, generating it in practice is plagued by noise and decoherence, limiting its utility. Entanglement distillation and forward error correction are the tools we employ to combat this noise, but designing the best distillation and error correction circuits that function well, especially on today's imperfect hardware, is still challenging. Here, we develop a simulation algorithm for distillation circuits with gate-simulation complexity of $\mathcal{O}(1)$ steps, providing for drastically faster modeling compared to $\mathcal{O}(n)$ Clifford simulators or $\mathcal{O}(2^n)$ wavefunction simulators over $n$ qubits. This new simulator made it possible to not only model but also optimize practically interesting purification circuits. It enabled us to use a simple discrete optimization algorithm to design purification circuits from $n$ raw Bell pairs to $k$ purified pairs and study the use of these circuits in the teleportation of logical qubits in second-generation quantum repeaters. The resulting purification circuits are the best-known purification circuits for finite-size noisy hardware and can be fine-tuned for specific hardware error models. Furthermore, we design purification circuits that shape the correlations of errors in the purified pairs such that the performance of the error-correcting code used in teleportation or other higher-level protocols is greatly improved. Our approach of optimizing multiple layers of the networking stack, both the low-level entanglement purification, and the forward error correction on top of it, are shown to be indispensable for the design of high-performance second-generation quantum repeaters