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MACHINE LEARNING DISCOVERY OF NEW PHASES IN PROGRAMMABLE RYDBERG QUANTUM SIMULATOR SNAPSHOTS
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Classroom Contents
Machine Learning for Quantum Simulation - IPAM at UCLA
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- 1 Intro
- 2 TUNNELING DENSITY OF STATES, IN 1962
- 3 X-ray diffraction in 1913
- 4 Projective Measurements in 1922
- 5 Machine Learning Quantum Emergence
- 6 Train a neural network to recognize best hypothesis?
- 7 DETERMINED BY WEIGHTS AND BIASES
- 8 TRAINING THROUGH FEEDBACK
- 9 CORRELATOR CONVOLUTIONAL NEURAL NETWORKS: AN INTERPRETABLE ARCHITECTURE FOR IMAGE-LIKE
- 10 Convolutional Neural Networks (CNN)
- 11 The validity of CCNN's learning?
- 12 MACHINE LEARNING DISCOVERY OF NEW PHASES IN PROGRAMMABLE RYDBERG QUANTUM SIMULATOR SNAPSHOTS
- 13 SQUARE-LATTICE RYDBERG PHASES
- 14 Unsupervised Pass at the Phase Diagram
- 15 Supervised Learning
- 16 Supervised Phase Diagram
- 17 Entanglement in Striated Phase
- 18 New Phase l: Edge-ordering
- 19 New Phase II: Rhombic Phase
- 20 Machine Learning for Quantum Simulation Supervised ML: Learn characteristic Correlations NEW INSIGHT into COMPLEX DATA
