Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics
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 satisfyin...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Transactions on Quantum Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9384317/ |
| _version_ | 1819175332287086592 |
|---|---|
| author | Janusz Kusyk Samah M. Saeed Muharrem Umit Uyar |
| author_facet | Janusz Kusyk Samah M. Saeed Muharrem Umit Uyar |
| author_sort | Janusz Kusyk |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T20:53:11Z |
| format | Article |
| id | doaj.art-587be1c3b4114d05a3625d5791175bba |
| institution | Directory Open Access Journal |
| issn | 2689-1808 |
| language | English |
| last_indexed | 2024-12-22T20:53:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj.art-587be1c3b4114d05a3625d5791175bba2022-12-21T18:13:01ZengIEEEIEEE Transactions on Quantum Engineering2689-18082021-01-01211610.1109/TQE.2021.30683559384317Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to HeuristicsJanusz Kusyk0Samah M. Saeed1https://orcid.org/0000-0002-8107-3644Muharrem Umit Uyar2https://orcid.org/0000-0001-6268-8259Computer Systems Technology, New York City College of Technology, Brooklyn, NY, USADepartment of Electrical Engineering, City College of the City University of New York, New York, NY, USADepartment of Electrical Engineering, City College of the City University of New York, New York, NY, USAComputationally 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.https://ieeexplore.ieee.org/document/9384317/Artificial intelligence (AI)noisy intermediate scale quantum (NISQ)quantum algorithmsquantum circuit compilation (QCC)quantum circuit mappingquantum computing |
| spellingShingle | Janusz Kusyk Samah M. Saeed Muharrem Umit Uyar Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics IEEE Transactions on Quantum Engineering Artificial intelligence (AI) noisy intermediate scale quantum (NISQ) quantum algorithms quantum circuit compilation (QCC) quantum circuit mapping quantum computing |
| title | Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics |
| title_full | Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics |
| title_fullStr | Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics |
| title_full_unstemmed | Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics |
| title_short | Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics |
| title_sort | survey on quantum circuit compilation for noisy intermediate scale quantum computers artificial intelligence to heuristics |
| topic | Artificial intelligence (AI) noisy intermediate scale quantum (NISQ) quantum algorithms quantum circuit compilation (QCC) quantum circuit mapping quantum computing |
| url | https://ieeexplore.ieee.org/document/9384317/ |
| work_keys_str_mv | AT januszkusyk surveyonquantumcircuitcompilationfornoisyintermediatescalequantumcomputersartificialintelligencetoheuristics AT samahmsaeed surveyonquantumcircuitcompilationfornoisyintermediatescalequantumcomputersartificialintelligencetoheuristics AT muharremumituyar surveyonquantumcircuitcompilationfornoisyintermediatescalequantumcomputersartificialintelligencetoheuristics |