Faster quantum algorithm for evaluating game trees

TL;DR

This paper introduces a quantum algorithm that evaluates size-n AND-OR formulas more efficiently than previous methods, using a novel graph-based approach with quantum walks and span program compositions.

Contribution

The paper presents a new quantum algorithm for evaluating AND-OR formulas with improved query complexity using a hybrid graph construction method.

Findings

Achieves O(sqrt n log n) query complexity for evaluating size-n AND-OR formulas.

Uses a novel combination of span program compositions to construct the underlying graph.

Provides a theoretical bound close to the adversary lower bound for quantum query complexity.

Abstract

We give an O(sqrt n log n)-query quantum algorithm for evaluating size-n AND-OR formulas. Its running time is poly-logarithmically greater after efficient preprocessing. Unlike previous approaches, the algorithm is based on a quantum walk on a graph that is not a tree. Instead, the algorithm is based on a hybrid of direct-sum span program composition, which generates tree-like graphs, and a novel tensor-product span program composition method, which generates graphs with vertices corresponding to minimal zero-certificates. For comparison, by the general adversary bound, the quantum query complexity for evaluating a size-n read-once AND-OR formula is at least Omega(sqrt n), and at most O(sqrt{n} log n / log log n). However, this algorithm is not necessarily time efficient; the number of elementary quantum gates applied between input queries could be much larger. Ambainis et al. have given a quantum algorithm that uses sqrt{n} 2^{O(sqrt{log n})} queries, with a poly-logarithmically greater running time.