Tuesday, March 5, 2019
8:00AM - 8:12AM
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E27.00001: Improved Training of Quantum Boltzmann Machines
Eric Anschuetz, Yudong Cao
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Tuesday, March 5, 2019
8:12AM - 8:24AM
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E27.00002: Measurement-based adaptation protocol with quantum reinforcement learning
Lucas Lamata, Francisco Albarrán-Arriagada, Juan Carlos Retamal, Enrique Solano
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Tuesday, March 5, 2019
8:24AM - 8:36AM
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E27.00003: Improving training of Boltzmann machines with error corrected quantum annealing
Richard Li, Daniel A Lidar
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Tuesday, March 5, 2019
8:36AM - 9:12AM
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E27.00004: Opportunities and Challenges in Quantum-Assisted Machine Learning
Invited Speaker:
Alejandro Perdomo
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Tuesday, March 5, 2019
9:12AM - 9:24AM
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E27.00005: Quantum generative adversarial learning in a superconducting quantum circuit
Hu Ling, shuhao wu, Weizhou Cai, Yuwei Ma, Xianghao Mu, Yuan Xu, Haiyan Wang, Yipu Song, Dong-Ling Deng, Chang-Ling Zou, Luyan Sun
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Tuesday, March 5, 2019
9:24AM - 9:36AM
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E27.00006: Noncommutative Boltzmann Machines
Mark Novotny
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Tuesday, March 5, 2019
9:36AM - 9:48AM
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E27.00007: Hybrid quantum-classical schemes for generative adversarial learning: HQGANs
Jhonathan Romero, Alan Aspuru-Guzik
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Tuesday, March 5, 2019
9:48AM - 10:00AM
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E27.00008: Variational circuits for machine learning with near-term devices
Maria Schuld
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Tuesday, March 5, 2019
10:00AM - 10:12AM
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E27.00009: Quantum Manifold Learning Algorithms for Dimensionality Reduction
Xi He, Li Sun, Xiaokai Hou, Xiaoting Wang
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Tuesday, March 5, 2019
10:12AM - 10:24AM
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E27.00010: Differentiable Quantum Circuits and Generative Modeling
JinGuo Liu, Lei Wang
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Tuesday, March 5, 2019
10:24AM - 10:36AM
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E27.00011: Machine-learned QCVV for distinguishing single-qubit noise
Travis Scholten, Yi-Kai Liu, Kevin Young, Robin Blume-Kohout
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Tuesday, March 5, 2019
10:36AM - 10:48AM
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E27.00012: Quantum optical neural networks for next generation quantum information processing
Gregory R Steinbrecher, Jonathan Olson, Dirk R. Englund, Jacques Carolan
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