Variational Entanglement-Assisted Quantum Process Tomography with Arbitrary Ancillary Qubits

Xue, Shichuan; Wang, Yizhi; Zhan, Junwei; Wang, Yaxuan; Zeng, Ru; Ding, Jiangfang; Shi, Weixu; Liu, Yong; Liu, Yingwen; Huang, Anqi; Huang, Guang-Yao; Yu, Chunlin; Wang, Dongyang; Fu, Xiang; Qiang, Xiaogang; Xu, Ping; Deng, Mingtang; Yang, Xuejun; Wu, Junjie

doi:10.1103/physrevlett.129.133601

Cited by 4 publications

(12 citation statements)

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“…We use a reconfigurable silicon photonic chip that has been reported in Refs. [1,2] to perform experimental quantum generative adversarial learning demonstrations. As shown in Fig.…”

Section: (E)mentioning

confidence: 99%

“…Introduction.-The generation of quantum states plays a fundamental role in quantum computing [1]. Using the power of machine learning techniques [2], quantum generative adversarial networks (QGANs) can generate high-quality quantum states without requiring knowledge of the explicit structure of the states [3,4].…”

mentioning

confidence: 99%

“…Experimental methods.-We use a programmable silicon quantum photonic chip [26,27] to implement the QGAN model and investigate QGAN's robustness against noise and defects. The schematic of the chip is presented in Fig.…”

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confidence: 99%

“…1(d), which can prepare arbitrary two-ququart maximally entangled states and perform two-ququart projective measurements. Firstly, on-chip signal-idler photon pairs are produced [26,28]. Post-selecting the cases in which the signal photons exit at the top circuit and the idler photons exit at the bottom yields the twophoton maximally entangled state |𝜓 0 ⟩ = 1 2 4…”

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confidence: 99%

“…According to the Schmidt decomposition [1], Eq. (3) can describe arbitrary two-ququart maximally entangled states.…”

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confidence: 99%

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Quantum generative adversarial learning in photonics

Wang,

Xue,

Wang

et al. 2023

Opt. Lett.

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Quantum generative adversarial networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of noisy intermediate-scale quantum (NISQ) computing, it is essential to investigate whether QGANs can perform learning tasks on near-term quantum devices usually affected by noise and even defects. In this Letter, using a programmable silicon quantum photonic chip, we experimentally demonstrate the QGAN model in photonics for the first time to our knowledge and investigate the effects of noise and defects on its performance. Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90%, even under conditions where up to half of the generator’s phase shifters are damaged, or all of the generator and discriminator’s phase shifters are subjected to phase noise up to 0.04π. Our work sheds light on the feasibility of implementing QGANs on the NISQ-era quantum hardware.

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“…We use a reconfigurable silicon photonic chip that has been reported in Refs. [1,2] to perform experimental quantum generative adversarial learning demonstrations. As shown in Fig.…”

Section: (E)mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…According to the Schmidt decomposition [1], Eq. (3) can describe arbitrary two-ququart maximally entangled states.…”

mentioning

confidence: 99%

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Quantum generative adversarial learning in photonics

Wang,

Xue,

Wang

et al. 2023

Opt. Lett.

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A noise-robust quantum dynamics learning protocol based on Choi–Jamiolkowski isomorphism: theory and experiment

Chen,

Gao,

Qiu

et al. 2024

New J. Phys.

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With the rapid development of quantum technology, the growing manipulated Hilbert space makes learning the dynamics of the quantum system a significant challenge. Machine learning technique has brought apparent advantages in some learning strategies, therefore, we introduce it to indirect learning in this paper. Based on Choi-Jamiolkowski isomorphism, we propose a protocol that learns the dynamics of an inaccessible quantum system using a quantum device at hand. For an n-qubit system, the learning task can be done iteratively, with operational complexity O(poly(n,L)/ε2) in each iteration, where L is the circuit depth and ε is the measurement error. Then we theoretically prove its noise resilience to global depolarization, state preparation and measurement noise, and unitary noise in gates implementation, where we find the learned dynamics stay invariant. Finally, we investigate the protocol experimentally on a nitrogen-vacancy center system with a natural noise source. The results show that the behavior of a relatively intractable nuclear spin can be learned through an easily accessible electron spin under different noise models, demonstrating the protocol’s feasibility.

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