Quantum Generative Adversarial Networks (QGANs) have emerged as representative generative models in the Noisy Intermediate-Scale Quantum (NISQ) era and have attracted increasing attention in quantum machine learning. However, most existing QGAN methods rely on patch-based decomposition strategies, which weaken the glob...
The real classical shadows protocol of West et al. replaces the unitary (Clifford) ensemble of the Huang--Kueng--Preskill scheme by the orthogonal (real Clifford) ensemble, and for symmetric observables achieves strictly smaller estimator variances: a factor approaching two for global evolution and an exponential facto...
Classical shadows are an influential framework for compressing copies of a given quantum state $\rho$ into classical data $S$, enabling many properties of $\rho$ to be predicted from relatively few copies. In this work, we study two natural questions involving shadows: (1) Given $S$, when can one efficiently verify tha...
Georgios Karaiskos, Asad Raza, D. Rudolph et al.· 0 citations
This review examines how error mitigation techniques can be adapted to encoded and logical-qubit settings so that they can operate alongside quantum error correction to suppress residual logical errors and improve the accuracy of computation in the early fault-tolerant regime.
Han-Ze Li, Mengjie Yang, Xian-Quan Yan et al.· 2 citations
Simulated experiments show that this reformulate quantum image generation as coordinate-conditioned implicit function learning outperforms FRQI-based generation and PQWGAN in visual and quantitative quality while using fewer qubits, and also achieves better generation quality than the corresponding classical baseline.
Xue Yang, Rigui Zhou, Shi-Zheng Jia et al.· 0 citations
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