143 citations · 807 across the 14 of their papers we have counts for
14 papers
Quantum federated learning through blind quantum computing
Weikang Li, Sirui Lu, Dong-Ling Deng
Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the secur…
Tensor networks and efficient descriptions of classical data
Sirui Lu, Márton Kanász-Nagy, Ivan Kukuljan +1
We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets. For that, we study how the mutual information between a su…
Algorithms for quantum simulation at finite energies
Sirui Lu, Mari Carmen Bañuls, J. Ignacio Cirac
We introduce two kinds of quantum algorithms to explore microcanonical and canonical properties of many-body systems. The first one is a hybrid quantum algorithm that, given an eff…
Quantum Adversarial Machine Learning
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordin…
Adversarial Machine Learning Phases of Matter
Si Jiang, Sirui Lu, Dong-Ling Deng
We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on…
Optimizing QAOA: Success Probability and Runtime Dependence on Circuit Depth
Murphy Yuezhen Niu, Sirui Lu, Isaac L. Chuang
The quantum approximate optimization algorithm~(QAOA) first proposed by Farhi et al. promises near-term applications based on its simplicity, universality, and provable optimality.…