Multidimensional quantum walks and the multiplicative ladder adversary

Quantum computing offers the potential to solve problems more efficiently than classical methods, yet understanding its capabilities requires new conceptual frameworks. This dissertation explores quantum algorithms with a focus on maintaining connections to classical intuition, clarifying both their strengths and limitations.
In Part I, we explore the capabilities of a class of quantum algorithms known as quantum walks. These quantum walks, the quantum analogs of classical random walks, serve as powerful yet accessible tools for solving a variety of computational problems. Our key contribution is the development of a novel way to construct quantum walks, resulting in multidimensional quantum walks. By applying these walks to the $k$-distinctness problem, we achieve a time-efficient algorithm matching the best-known query upper bound up to polylogarithmic factors. Additionally, applying them to the welded tree problem results in exponential speedups, marking the first instance of a discrete quantum walk to do so. Furthermore, we extend the well-known link between quantum walks and electrical networks to the multidimensional quantum walks.
In Part II, we turn to the limitations of quantum algorithms by examining techniques for establishing quantum query lower bounds, representing the minimum computational cost required for any quantum algorithm to solve a given computational problem, regardless of the quantum algorithm used. To this goal, we introduce the multiplicative ladder adversary method, a simplified version of the multiplicative adversary method which unifies the compressed oracle technique within the broader adversary framework while extending its applicability, offering a more intuitive approach to establishing quantum lower bounds.