Experimental quantum computational chemistry with optimized unitary coupled cluster ansatz

Guo, Shaojun; Sun, Jinzhao; Qian, Haoran; Gong, Ming; Zhang, Yukun; Chen, Fusheng; Ye, Yangsen; Wu, Yulin; Cao, Sirui; Liu, Kun; Zha, Chen; Ying, Chong; Zhu, Qingling; Huang, He-Liang; Zhao, Youwei; Li, Shaowei; Wang, Shiyu; Yu, Jiale; Fan, Daojin; Wu, Dachao; Su, Hong; Deng, Hui; Rong, Hao; Li, Yuan; Zhang, Kaili; Chung, Tung-Hsun; Liang, Futian; Lin, Jin; Xu, Yu; Sun, Lihua; Guo, Cheng; Li, Na; Huo, Yong-Heng; Peng, Cheng-Zhi; Lu, Chao-Yang; Yuan, Xiao; Zhu, Xiaobo; Pan, Jian-Wei

doi:10.1038/s41567-024-02530-z

Cited by 7 publications

References 45 publications

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Non-hemolytic peptide classification using a quantum support vector machine

Zhuang,

Tanner,

et al. 2024

Quantum Inf Process

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Quantum machine learning (QML) is one of the most promising applications of quantum computation. Despite the theoretical advantages, it is still unclear exactly what kind of problems QML techniques can be used for, given the current limitation of noisy intermediate-scale quantum devices. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets, we apply and contrast the performance of the QSVM with a number of popular classical SVMs, out of which the QSVM performs best overall. The contributions of this work include: (i) the first application of the QSVM to this specific peptide classification task and (ii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification task. This foundational work provides insight into possible applications of QML in computational biology and may facilitate safer therapeutic developments by improving our ability to identify hemolytic properties in peptides.

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Non-hemolytic peptide classification using a quantum support vector machine

Zhuang,

Tanner,

et al. 2024

Quantum Inf Process

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In situ mixer calibration for superconducting quantum circuits

Wu,

Lin,

Xie

et al. 2024

Applied Physics Letters

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Mixers play a crucial role in superconducting quantum computing, primarily by facilitating frequency conversion of signals to enable precise control and readout of quantum states. However, imperfections, particularly local oscillator leakage and unwanted sideband signal, can significantly compromise control fidelity. To mitigate these defects, regular and precise mixer calibrations are indispensable, yet they pose a formidable challenge in large-scale quantum control. Here, we introduce an in situ and scalable mixer calibration scheme using superconducting qubits. Our method leverages the qubit's response to imperfect signals, allowing for calibration without modifying the wiring configuration. We experimentally validate the efficacy of this technique by benchmarking single-qubit gate error and qubit coherence time.

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Exploring electron-phonon coupling using quantum computing methods

Zhou,

Shang

2024

Phys. Scr.

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Quantum computing in the noisy intermediate-scale quantum (NISQ) era has foregrounded the importance of Variational Quantum Algorithms (VQAs). These algorithms are crucial for addressing complex quantum mechanical problems that challenge classical computers. One such problem is the electron-phonon (e–ph) interaction, which is essential for determining the zero-point renormalization (ZPR) of electronic structure properties. The calculation of ZPR of fundamental gap relies on the accurate computation of ionization potential (IP) and electron affinity (EA) energy levels in molecular systems, where the VQAs offer the promising solutions. Despite the critical importance of IP, EA energies and ZPR in quantum chemistry calculations, research into the application of quantum algorithms for these calculations remains limited. To address these challenges, we propose two quantum algorithms for ZPR of fundamental gap calculation using Variational Quantum Deflation (VQD) and Quantum Equation of Motion (QEOM) algorithm for several molecular systems. This work opens up new possibilities for the accurate and efficient study of e-ph interaction in electronic structure calculations, even with NISQ-era hardware.

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Experimental quantum computational chemistry with optimized unitary coupled cluster ansatz

Cited by 7 publications

References 45 publications

Non-hemolytic peptide classification using a quantum support vector machine

Non-hemolytic peptide classification using a quantum support vector machine

In situ mixer calibration for superconducting quantum circuits

Exploring electron-phonon coupling using quantum computing methods

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