Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics
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- @Article{Kusyk:2021:QE,
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author = "Janusz Kusyk and Samah M. Saeed and
Muharrem Umit Uyar",
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title = "Survey on Quantum Circuit Compilation for Noisy
Intermediate-Scale Quantum Computers: Artificial
Intelligence to Heuristics",
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journal = "IEEE Transactions on Quantum Engineering",
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year = "2021",
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volume = "2",
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pages = "Art no. 2501616",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Artificial
intelligence (AI), noisy intermediate scale quantum
(NISQ), quantum algo-rithms, quantum circuit
compilation (QCC), quantum circuit mapping, quantum
computing",
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DOI = "doi:10.1109/TQE.2021.3068355",
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ISSN = "2689-1808",
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size = "16 pages",
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abstract = "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.",
-
notes = "Also known as \cite{9384317}",
- }
Genetic Programming entries for
Janusz Kusyk
Samah M Saeed
Muharrem Umit Uyar
Citations