Hrishee Shastri | Searcharxiv

arXiv2021

Quantum routing with fast reversals

We present methods for implementing arbitrary permutations of qubits under interaction constraints. Our protocols make use of previous methods for rapidly reversing the order of qubits along a path. Given nearest-neighbor interactions on a path of length $n$, we show that there exists a constant $ε\approx 0.034$ such that the quantum routing time is at most $(1-ε)n$, whereas any swap-based protocol needs at least time $n-1$. This represents the first known quantum advantage over swap-based routing methods and also gives improved quantum routing times for realistic architectures such as grids. Furthermore, we show that our algorithm approaches a quantum routing time of $2n/3$ in expectation for uniformly random permutations, whereas swap-based protocols require time $n$ asymptotically. Additionally, we consider sparse permutations that route $k \le n$ qubits and give algorithms with quantum routing time at most $n/3 + O(k^2)$ on paths and at most $2r/3 + O(k^2)$ on general graphs with radius $r$.

quant-ph↗

arXiv2020

Revisiting Locality in Binary-Integer Representations

Mutation and recombination operators play a key role in determining the speed and quality of Genetic and Evolutionary Algorithms (GEAs). Prior work has analyzed the effects of these operators on genotypic variation, often using locality metrics that measure the sensitivity and stability of genotype-phenotype representations to these operators. In this paper, we focus on an important subset of representations, namely nonredundant bitstring-to-integer representations, and analyze them through the lens of Rothlauf's widely used locality metrics. We first define locality metrics equivalent to Rothlauf's that are tailored to our domain: the \textit{point locality} for single-bit mutation and \textit{general locality} for recombination. With these definitions, we derive tight bounds and a closed form expected value for point locality. For general locality we show that it is asymptotically equivalent across all representations and operators. We also recreate three established GEA experiments to understand the predictive power of point locality on GEA performance, focusing on two popular and often juxtaposed representations: standard binary and binary reflected Gray. We show that standard binary has provably no worse locality than any Gray encoding, including binary reflected Gray. We discuss this result in the context of previous studies that found binary reflected Gray to outperform standard binary, and we argue that locality cannot be the explanation for strong performance. Finally, we provide empirical evidence that weak point locality representations can be beneficial to performance in the exploration phase of the GEA, while strong point locality representations are more beneficial in the exploitation phase.

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