Graph Neural Network Assisted Quantum Compilation for Qubit Allocation

LeCompte, Travis; Qi, Fang; Yuan, Xu; Tzeng, Nian-Feng; Najafi, M. Hassan; Peng, Lü

doi:10.1145/3583781.3590300

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Early quantum computing research was focused on designing quantum hardware [28], instruction set architecture [8], and quantum computer microarchitecture [9], [10], [32], [33]. Afterward, the temporal and spatial noise variation challenges of SC quantum computers were studied to discover mapping and allocation-enhanced compilations to make algorithm execution more robust to diverse errors [20], [21], [22], [31], [46]. Additionally, works such as [18] contribute to the understanding of how noise, fidelity, and computational cost interplay in quantum processing, enriching the broader discourse on quantum system performance.…”

Section: B Related Work On Quantum Sr Estimationmentioning

confidence: 99%

Quantum Vulnerability Analysis to Guide Robust Quantum Computing System Design

Qi,

Smith,

LeCompte

et al. 2024

IEEE Trans. Quantum Eng.

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While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the state-of-the-art metric used to gauge quantum program performance. The ESP suffers poor prediction since it fails to account for the unique combination of circuit structure, quantum state, and quantum computer properties specific to each program execution. Thus, an urgent need exists for a systematic approach that can elucidate various noise impacts and accurately and robustly predict quantum computer success rates, emphasizing application and device scaling. In this article, we propose quantum vulnerability analysis (QVA) to systematically quantify the error impact on quantum applications and address the gap between current success rate (SR) estimators and real quantum computer results. The QVA determines the cumulative quantum vulnerability (CQV) of the target quantum computation, which quantifies the quantum error impact based on the entire algorithm applied to the target quantum machine. By evaluating the CQV with well-known benchmarks on three 27-qubit quantum computers, the CQV success estimation outperforms the estimated probability of success state-of-the-art prediction technique by achieving on average six times less relative prediction error, with best cases at 30 times, for benchmarks with a real SR rate above 0.1%. Direct application of QVA has been provided that helps researchers choose a promising compiling strategy at compile time.

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Section: B Related Work On Quantum Sr Estimationmentioning

confidence: 99%

Quantum Vulnerability Analysis to Guide Robust Quantum Computing System Design

Qi,

Smith,

LeCompte

et al. 2024

IEEE Trans. Quantum Eng.

Self Cite

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“…While this is helpful, it is difficult to extrapolate from smaller to larger sizes due to growing complexity. Other works similarly search the set of possible layouts while guided by fidelity or other success methods [15], [27], [28]. All of these approaches aim to minimize the vulnerability of the circuit through the choice of an initial layout through a variety of different methods.…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Qubit Allocation for Error Reduction in Quantum Circuits

LeCompte,

Qi,

Yuan

et al. 2023

IEEE Trans. Quantum Eng.

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Quantum computing is a quickly growing field with great potential for future technology. Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations -qubit count and error vulnerability. Although quantum error correction (QEC) methods exist, they are not applicable to the current size of computers, requiring thousands of qubits, while current NISQ systems have hundreds at most. It is therefore imperative to improve the reliability of the circuits as much as possible to make them robust to the errors that will occur. One common approach is to adjust the compilation process of a circuit to create a final circuit with improved reliability. However, there are many decisions to be made when compiling that affect the final performance of the circuit, two of the most critical ones being the mapping of logical to physical qubits (the qubit allocation problem) and the movement of qubits to satisfy two-qubit gate adjacency requirements (the qubit routing problem). We focus on solving the qubit allocation problem and identifying initial layouts that reduce error. To identify these layouts, we combine reinforcement learning with a graph neural network (GNN)-based Q-network for analyzing both the connections and error rates of the graph-like backend of superconducting quantum computers to make mapping decisions, creating a Graph Neural Network Assisted Compilation (GNAQC) strategy. We provide both the circuit and the properties of the target backend as input to guide the decision-making process. We work with the IBM Qiskit API to compile and simulate our quantum circuits. We train the architecture using a set of four backends and six circuits and find that GNAQC generally outperforms pre-existing qubit allocation algorithms, increasing final relative output fidelity by roughly 12.7%.

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