Qubit mapping of one-way quantum computation patterns onto 2D nearest-neighbor architectures

Sanaei, Sajjad; Mohammadzadeh, Naser

doi:10.1007/s11128-019-2177-x

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…We observe that several AI and heuristic based QCC methods reported in literature are specifically designed to work on a particular quantum hardware architecture, including grid based layouts (e.g., [37] and [32]) and nearest neighbor architectures (e.g., [36], [51] and [30]). Although they perform well for the target architectures that they were intended for, such as methods that rely on searching for isomorphic sub-graphs (e.g., [43], [52] and [44]), it is not clear how easily they can be adapted to different NISQ platforms and various types of quantum algorithms.…”

Section: Evaluation Of Methods and Future Directionsmentioning

confidence: 99%

“…The QCC techniques based on token swapping to reduce quantum circuit depth and the number of qubits of a target quantum circuit are examples of graph optimization based methods [54]. In another method [51], nearest neighbor architecture of a quantum circuit is explored using eigenvector centrality concept of graph theory [94], which ranks qubits in a coupling graph-based on their connectivity with other qubits. First the qubit with maximum centrality is selected and placed at the center of a grid, to which, neighbor qubits are added at each iteration by prioritizing the ones with higher centrality.…”

Section: Graph Optimization For Qccmentioning

confidence: 99%

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

Kusyk

Saeed

2021

IEEE Trans. Quantum Eng.

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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 survey, we present AI 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.

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