Maria Schuld

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 17 papers; trusts 98 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 267 citations into it; #3,526 of 35,345 by trust here, #4,101 by citations
• Atomic and Molecular Physics, and Optics — 129 citations into it; #397 of 7,135 by trust here, #1,122 by citations
• Materials Chemistry — 44 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Computational Theory and Mathematics — 40 citations into it; #6,901 of 12,182 by trust here, #4,199 by citations
• Statistical and Nonlinear Physics — 38 citations into it
• Model Architectures — 35 citations into it
• Probabilistic & Bayesian Methods — 26 citations into it

Whom Maria Schuld trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Seth Lloyd
2. Nathan Killoran
3. Edward Farhi
4. Ville Bergholm
5. Geoffrey E. Hinton
6. Josh A. Izaac
7. Patrick Rebentrost
8. Christopher K. I. Williams
9. Aram Wettroth Harrow
10. Nathan Wiebe

Who trusts Maria Schuld overall

1. M. Cerezo
2. Nathan Killoran
3. Ryan Larose
4. Josh A. Izaac
5. Tak Hur
6. Andrea Mari
7. Yudong Cao
8. Thomas R. Bromley
9. Marcello Benedetti
10. Joschka Roffe

Papers from Maria Schuld's perspective

Ordered by how strongly the researchers Maria Schuld trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Machine learning and the physical sciences (2019) — 1,895 citations
• Circuit-centric quantum classifiers (2018) — 1,137 citations
• Machine Learning with Quantum Computers (2021) — 426 citations
• Quantum machine learning models are kernel methods (2021) — 525 citations
• Quantum circuits with many photons on a programmable nanophotonic chip (2021) — 562 citations
• Quantum embeddings for machine learning (2020) — 447 citations
• Effect of data encoding on the expressive power of variational quantum-machine-learning models (2020) — 850 citations
• Transfer learning in hybrid classical-quantum neural networks (2019) — 445 citations
• PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018) — 1,710 citations
• Continuous-variable quantum neural networks (2018) — 446 citations
• Quantum Machine Learning in Feature Hilbert Spaces. (2018) — 1,749 citations
• Supervised Learning with Quantum Computers (2018) — 662 citations
• Evaluating analytic gradients on quantum hardware (2018) — 1,140 citations
• Quantum Machine Learning (2018) — 2,426 citations
• Encyclopedia of Machine Learning and Data Mining (2017) — 452 citations
• The quest for a Quantum Neural Network (2014) — 694 citations
• An introduction to quantum machine learning (2014) — 1,477 citations