Efficient Circuits for Quantum Walks

TL;DR

This paper introduces an efficient quantum circuit construction method for implementing quantum walk operators based on arbitrary sparse classical random walks, improving scalability and precision over previous methods.

Contribution

It presents a new linear-scaling approach for quantum walk implementation that reduces complexity in sparsity and precision compared to prior techniques.

Findings

Scales linearly with sparsity parameter

Poly-logarithmic in inverse of precision

Applicable to quantum algorithms for permanents and sampling

Abstract

We present an efficient general method for realizing a quantum walk operator corresponding to an arbitrary sparse classical random walk. Our approach is based on Grover and Rudolph's method for preparing coherent versions of efficiently integrable probability distributions. This method is intended for use in quantum walk algorithms with polynomial speedups, whose complexity is usually measured in terms of how many times we have to apply a step of a quantum walk, compared to the number of necessary classical Markov chain steps. We consider a finer notion of complexity including the number of elementary gates it takes to implement each step of the quantum walk with some desired accuracy. The difference in complexity for various implementation approaches is that our method scales linearly in the sparsity parameter and poly-logarithmically with the inverse of the desired precision. The best previously known general methods either scale quadratically in the sparsity parameter, or polynomially in the inverse precision. Our approach is especially relevant for implementing quantum walks corresponding to classical random walks like those used in the classical algorithms for approximating permanents and sampling from binary contingency tables. In those algorithms, the sparsity parameter grows with the problem size, while maintaining high precision is required.