Variational Quantum Pulse Learning

Liang, Zhiding; Wang, Hanrui; Cheng, Jinglei; Ding, Yongshan; Ren, Hang; Gao, Zhengqi; Hu, Zhirui; Boning, Duane S.; Qian, Xuehai; Han, Song; Jiang, Weiwen; Shi, Yiyu

doi:10.1109/qce53715.2022.00078

Cited by 21 publications

(9 citation statements)

References 29 publications

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“…More recently, VQA optimization through Quantum Optimal Control (QOC) has gained more attraction [20], [21], [25], [86]. For hardware-efficient gate-based PQCs, Liang et al [23] proposed a pulse optimization framework that manipulates the PQC gate amplitudes as part of the VQA optimization routine. In contrast to their approach, we choose to preconfigure our CR pulse parameters and not attach them to the VQA optimization procedure, as it was proven in [6] through numerical simulations that accurate optimizations can be obtained for fixed-phase two-qubit gates.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Parameterized Quantum Circuits With Cross-Resonance Pulse-Driven Entanglers

Ibrahim

Mohammadbagherpoor

Rios

et al. 2022

IEEE Trans. Quantum Eng.

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Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices. Therefore, investigating their design has become key in quantum computing research. Previous works have shown that choosing an effective parameterized quantum circuit (PQC) or ansatz for a VQA is crucial to its overall performance, especially on near-term devices. In this paper, we utilize pulse-level access to quantum machines, our understanding of their twoqubit interactions, and, more importantly, our knowledge of VQAs, to customize the design of two-qubit entanglers. Our analysis shows that utilizing customized pulse gates for ansatze reduces state preparation times by more than half, maintains expressibility relative to standard ansatze, and produces PQCs that are more trainable through local cost function analysis. Our algorithm performance results show that in three cases, our PQC configuration outperforms the base implementation. Experiments using IBM Quantum hardware demonstrate that our pulse-based PQC configurations are more capable of solving MaxCut and Chemistry problems compared to a standard configuration. INDEX TERMSQuantum computing, variational quantum algorithms (VQAs), parameterized quantum circuits (PQCs), pulse level control, hamiltonian tomography, barren-plateaus VOLUME 4, 2016 1 This article has been accepted for publication in IEEE Transactions on Quantum Engineering.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Parameterized Quantum Circuits With Cross-Resonance Pulse-Driven Entanglers

Ibrahim

Mohammadbagherpoor

Rios

et al. 2022

IEEE Trans. Quantum Eng.

View full text Add to dashboard Cite

show abstract

“…More recently, VQA optimization through Quantum Optimal Control (QOC) has gained more attraction [20], [21], [25], [87]. For hardware-efficient gate-based PQCs, Liang et al [23] proposed a pulse optimization framework that manipulates the PQC gate amplitudes as part of the VQA optimization routine. In contrast to their approach, we choose to preconfigure our CR pulse parameters and not attach them to the VQA optimization procedure, as it was proven in [6] through numerical simulations that accurate optimizations can be obtained for fixed-phase two-qubit gates.…”

Section: Related Workmentioning

confidence: 99%

Pulse-Level Optimization of Parameterized Quantum Circuits for Variational Quantum Algorithms

Ibrahim¹,

Mohammadbagherpoor²,

Rios³

et al. 2022

Preprint

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Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices. Therefore, investigating their design has become key in quantum computing research. Previous works have shown that choosing an effective parameterized quantum circuit (PQC) or ansatz for VQAs is crucial to their overall performance, especially on near-term devices. In this paper, we utilize pulse-level access to quantum machines and our understanding of their two-qubit interactions to optimize the design of two-qubit entanglers in a manner suitable for VQAs. Our analysis results show that pulse-optimized ansatze reduce state preparation times by more than half, maintain expressibility relative to standard PQCs, and are more trainable through local cost function analysis. Our algorithm performance results show that in three cases, our PQC configuration outperforms the base implementation. Our algorithm performance results, executed on IBM Quantum hardware, demonstrate that our pulse-optimized PQC configurations are more capable of solving MaxCut and Chemistry problems compared to a standard configuration.

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“…The core idea is exploiting PQCs to delegate classically difficult computation and variationally updating the parameters in PQCs with a powerful classical optimizer [28,29,30,31,32]. With error mitigation techniques [33,34,35,36,37,38,39,40], VQAs have been applied to various fields and the corresponding specific algorithms have been developed, including variational quantum eigensolver (VQE) [41,42,43,44,45], quantum approximate optimization algorithm [46,47,48,15,49], variational quantum simulator [50,51,52,53], quantum neural network (QNN) [54,55,56,57,58,59,60,61,62,63], and others [64,65,66,67,68,69,70,71,72,...…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization

Wang¹,

Qi²,

Ferrie³

et al. 2023

Preprint

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Parameterized quantum circuits (PQCs) have been widely used as a machine learning model to explore the potential of achieving quantum advantages for various tasks. However, the training of PQCs is notoriously challenging owing to the phenomenon of plateaus and/or the existence of (exponentially) many spurious local minima. In this work, we propose an efficient parameter initialization strategy with theoretical guarantees. We prove that if the initial domain of each parameter is reduced inversely proportional to the square root of circuit depth, then the magnitude of the cost gradient decays at most polynomially as a function of the depth. Our theoretical results are verified by numerical simulations of variational quantum eigensolver tasks. Moreover, we demonstrate that the reduced-domain initialization strategy can protect specific quantum neural networks from exponentially many spurious local minima. Our results highlight the significance of an appropriate parameter initialization strategy and can be used to enhance the trainability of PQCs in variational quantum algorithms.

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Variational Quantum Pulse Learning

Cited by 21 publications

References 29 publications

Evaluation of Parameterized Quantum Circuits With Cross-Resonance Pulse-Driven Entanglers

Evaluation of Parameterized Quantum Circuits With Cross-Resonance Pulse-Driven Entanglers

Pulse-Level Optimization of Parameterized Quantum Circuits for Variational Quantum Algorithms

Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization

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