Schedule

Fri Dec 11, 06:00 AM -- 05:55 PM (PST)



Time

Event

6:00 a.m. - 6:05 a.m.

Opening Remarks

6:05 a.m. - 6:35 a.m.

Invited talk 1: Jens Eisert
Tensor Networks as a Data Structure in Probabilistic Modeling and for Learning Dynamical Laws from Data

6:35 a.m. - 6:45 a.m.

Q&A

6:45 a.m. - 7:17 a.m.

Invited talk 2: Nadav Cohen
Expressiveness in Deep Learning via Tensor Networks and Quantum Entanglement

7:17 a.m. - 7:25 a.m.

Q&A

7:25 a.m. - 7:55 a.m.

Invited talk 3: Frank Verstraete
Tensor Networks and Counting Problems on the Lattice

7:55 a.m. - 8:05 a.m.

Q&A

8:05 a.m. - 8:50 a.m.

Invited talk 4: Ivan Oseledets
Quantum in ML and ML in Quantum

8:50 a.m. - 9:00 a.m.

Q&A

9:00 a.m. - 9:40 a.m.

Invited talk 5: Animashree Anandkumar
Live Presentation By Prof. Anima

9:40 a.m. - 10:07 a.m.

Invited talk 6: Tomotoshi Nishino
A Century of the Tensor Network Formulation from the Ising Model

10:07 a.m. - 10:15 a.m

Q&A

10:15 a.m. - 10:18 a.m.

Poster 1: Yao Lei Xu
Multi-Graph Tensor Networks

10:18 a.m. - 10:21 a.m.

Poster 2: Hong Hao
High Performance Single-Site Finite DMRG on GPUs

10:21 a.m. - 10:24 a.m.

Poster 3: Yunpu Ma
Variational Quantum Circuit Model for Knowledge Graph Embeddings

10:24 a.m. - 10:27 a.m.

Poster 4: Yen-Chi Chen
Hybrid quantum-classical classifier based on tensor network and variational quantum circuit

10:27 a.m. - 10:30 a.m.

Poster 5: Hui Gao
A Neural Matching Model based on Quantum Interference and Quantum Many-body System

10:30 a.m. - 10:40 a.m.

Contributed talk 1: Alex GoeƟmann
Paper 3: Tensor network approaches for data-driven identification of non-linear dynamical laws

10:40 a.m. - 10:50 a.m.

Contributed talk 2: Jensen Wang
Paper 6: Anomaly Detections with Tensor Networks

10:50 a.m. - 11:00 a.m.

Contributed talk 3: Chao Li
Paper 32: High-order Learning Model via Fractional Tensor Network Decomposition

11:00 a.m. - 11:45 a.m.

Panel Discussion 1: Jacob Biamonte, Ivan Oseledets, Glen Evenbly, Jens Eisert, Nadav Cohen, Xiao-Yang Liu
Theoretical, Algorithmic and Physical

11:45 a.m. - 12:00 p.m.

Break

12:00 p.m. - 12:45 p.m.

Panel Discussion 2: Glen Evenbly, Martin Ganahl, Paul Springer, Xiao-Yang Liu
Software and High Performance Implementation

12:45 p.m. - 1:00 p.m.

Break

1:00 p.m. - 1:28 p.m.

Invited talk 7: Paul Springer
cuTensor: High-Performance CUDA Tensor Primitives

1:28 p.m. - 1:35 p.m.

Q&A

1:35 p.m. - 2:05 p.m.

Invited talk 8: Martin Ganahl
TensorNetwork: A Python Package for Tensor Network Computations

2:05 p.m. - 2:15 p.m.

Q&A

2:15 p.m. - 2:51 p.m.

Invited talk 9: Guillaume Rabusseau
Tensor Network Models for Structured Data

2:51 p.m. - 3:00 p.m.

Q&A

3:00 p.m. - 3:30 p.m.

Invited talk 10: Glen Evenbly
Getting Started with Tensor Networks

3:30 p.m. - 3:40 p.m.

Q&A

3:40 p.m. - 3:50 p.m.

Contributed talk 4: Khadijeh Najafi
Paper 27: Limitations of gradient-based Born Machine over tensornetworks on learning quantum nonlocality

3:50 p.m. - 4:00 p.m.

Contributed talk 5: Philip Blagoveschensky
Paper 19: Deep convolutional tensor network

4:00 p.m. - 4:04 p.m.

Poster 6: Yiming Fang
Paper 16: Quantum Tensor Networks for Variational Reinforcement Learning

4:04 p.m. - 4:07 p.m.

Poster 7: (Poster talk)
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata

4:07 p.m. - 4:10 p.m.

Poster 8: Yitong Yao
Paper 24: Modeling Natural Language via Quantum Many-body Wave Function and Tensor Network

4:10 p.m. - 4:32 p.m.

Invited talk 11: Rose Yu
Tensor Methods for Efficient and Interpretable Spatiotemporal Learning

4:32 p.m. - 4:40 p.m.

Q&A

4:40 p.m. - 5:10 p.m.

Invited talk 12: Giacomo Torlai
Learning Quantum Channels with Tensor Networks

5:10 p.m. - 5:20 p.m.

Q&A

5:20 p.m. - 5:50 p.m.

Invited talk 13: Anwar Walid
(Live Presentation): High Performance Computation for Tensor Networks Learning

5:50 p.m. - 5:55 p.m.

Closing Remarks