Distributed Exact Generalized Grover's Algorithm
Xu Zhou, Xusheng Xu, Shenggen Zheng, Le Luo

TL;DR
This paper introduces DEGGA, a distributed quantum algorithm for exact multi-target search that reduces circuit depth and gate usage, demonstrating practical advantages in NISQ-era quantum computing.
Contribution
The paper presents DEGGA, a novel distributed exact generalized Grover's algorithm that achieves 100% accuracy with optimized circuit depth and no auxiliary qubits, suitable for NISQ devices.
Findings
Reduces quantum circuit depth by 91.3%.
Decreases quantum gate usage by 90.7%.
Achieves 100% success probability in target identification.
Abstract
Distributed quantum computation has garnered immense attention in the noisy intermediate-scale quantum (NISQ) era, where each computational node necessitates fewer qubits and quantum gates. In this paper, we focus on a generalized search problem involving multiple targets within an unordered database and propose a Distributed Exact Generalized Grover's Algorithm (DEGGA) to address this challenge by decomposing it into arbitrary components, where . Specifically, (1) our algorithm ensures accuracy, with a theoretical probability of identifying the target states at ; (2) if the number of targets is fixed, the pivotal factor influencing the circuit depth of DEGGA is the partitioning strategy, rather than the magnitude of ; (3) our method requires a total of qubits, eliminating the need for auxiliary qubits; (4) we elucidate the resolutions (two-node and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Mechanics and Entropy · Neural Networks and Applications
