Distributed quantum approximate counting algorithm

Zijian Diao, Chunfeng Huang, Ke Wang

Huaijing Huang, Daowen Qiu

In this article, we propose a distributed quantum algorithm for solving counting problem using Grover operator and a classical post-processing procedure. We apply the proposed algorithm to estimate inner products and Hamming distances. Simulations are conducted on the Qisikit platform, further demonstrating the effectiveness of our algorithm and its suitability for the NISQ era. Compared to existing counting algorithms, the proposed algorithm has advantages in terms of the number of qubits, circuit depth, and the number of quantum gates.

Takenori Kobayashi, Hirotaka Ono, Kunihiko Sadakane +1 more

Julien Drapeau, Shreya Banerjee, Stefanos Kourtis

Abstract We introduce a variational algorithm based on the quantum alternating operator ansatz (QAOA) for the approximate solution of computationally hard counting problems. Our algorithm, dubbed VQCount, is based on the equivalence between random sampling and approximate counting and employs QAOA as a solution sampler. We first prove that VQCount improves upon previous work by reducing exponentially the number of samples needed to obtain an approximation within an arbitrary small multiplicative factor of the exact count. Using tensor network simulations, we then study the typical performance of VQCount with shallow circuits on synthetic instances of two #P-hard problems, positive #NAE3SAT and positive #1-in-3SAT. We employ the original quantum approximate optimization algorithm version of QAOA, as well as the Grover-mixer variant which guarantees a uniform solution probability distribution. We observe a tradeoff between QAOA success probability and sampling uniformity, which we exploit to achieve an empirical efficiency gain over both naive rejection sampling and Grover-based quantum counting. Our results highlight the potential and limitations of variational algorithms for approximate counting.

Sreraman Muralidharan

Ming‐Xing Luo