Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

• Published in: IEEE transactions on quantum engineering Vol. 2; pp. 1 - 16
• Main Authors: Kusyk, Janusz, Saeed, Samah M., Uyar, Muharrem Umit
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Artificial intelligence; Artificial intelligence (AI); Computer architecture; Computers; Gates (circuits); Genetic algorithms; Greedy algorithms; Hardware; Heuristic methods; Logic gates; Machine learning; noisy intermediate scale quantum (NISQ); Optimization techniques; quantum algorithms; Quantum circuit; quantum circuit compilation (QCC); quantum circuit mapping; Quantum computers; quantum computing; Qubit; Qubits (quantum computing)
• ISSN: 2689-1808; 2689-1808
• DOI: 10.1109/TQE.2021.3068355

Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum architecture. Quantum circuit compilation (QCC) aims to generate feasible mappings such that a quantum circuit can be executed in a given hardware platform with acceptable confidence in outcomes. Physical constraints of a NISQ computer change frequently, requiring QCC process to be repeated often. When a circuit cannot directly be executed on a quantum hardware due to its physical limitations, it is necessary to modify the circuit by adding new quantum gates and auxiliary qubits, increasing its space and time complexity. An inefficient QCC may significantly increase error rate and circuit latency for even the simplest algorithms. In this article, we present artificial intelligence (AI)-based and heuristic-based methods recently reported in the literature that attempt to address these QCC challenges. We group them based on underlying techniques that they implement, such as AI algorithms including genetic algorithms, genetic programming, ant colony optimization and AI planning, and heuristics methods employing greedy algorithms, satisfiability problem solvers, dynamic, and graph optimization techniques. We discuss performance of each QCC technique and evaluate its potential limitations.