Daniel K. Park

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Atomic and Molecular Physics, and Optics — 12 citations into it

Whom Daniel K. Park trusts overall

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

1. Maria Schuld
2. Yurii E. Nesterov
3. John Preskill
4. Seth Lloyd
5. Ryan Larose
6. Daniel George
7. Ville Bergholm

Papers from Daniel K. Park's perspective

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

1. Quantum Machine Learning in Feature Hilbert Spaces. (Physical Review Letters)
2. Gradient-based learning applied to document recognition
3. Evaluating analytic gradients on quantum hardware (Physical Review A)
4. Quantum circuit learning (Physical Review A)
5. Hierarchical quantum classifiers (npj Quantum Information)
6. Quantum Computing in the NISQ era and beyond (Quantum)
7. Deep Learning
8. PennyLane: Automatic differentiation of hybrid quantum-classical computations (arXiv.org)
9. GENERATIVE ADVERSARIAL NETS
10. Quantum principal component analysis (Nature Physics)

Their papers

• Quantum convolutional neural network for classical data classification (2021) — 385 citations