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Optimal training of variational quantum algorithms without barren plateaus
Tobias Haug, M. S. Kim
Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers. However, training VQAs often requires an extensive amount of time and suffers from the b…
NISQ Algorithm for Hamiltonian Simulation via Truncated Taylor Series
Jonathan Wei Zhong Lau, Tobias Haug, Leong Chuan Kwek +1
Simulating the dynamics of many-body quantum systems is believed to be one of the first fields that quantum computers can show a quantum advantage over classical computers. Noisy i…
Noisy intermediate scale quantum simulation of time dependent Hamiltonians
Jonathan Wei Zhong Lau, Kishor Bharti, Tobias Haug +1
Quantum computers are expected to help us to achieve accurate simulation of the dynamics of many-body quantum systems. However, the limitations of current NISQ devices prevents us…
Classifying global state preparation via deep reinforcement learning
Tobias Haug, Wai-Keong Mok, Jia-Bin You +3
Quantum information processing often requires the preparation of arbitrary quantum states, such as all the states on the Bloch sphere for two-level systems. While numerical optimiz…
Machine Learning meets Quantum Foundations: A Brief Survey
Kishor Bharti, Tobias Haug, Vlatko Vedral +1
The goal of machine learning is to facilitate a computer to execute a specific task without explicit instruction by an external party. Quantum foundations seeks to explain the conc…
How to Teach AI to Play Bell Non-Local Games: Reinforcement Learning
Kishor Bharti, Tobias Haug, Vlatko Vedral +1
Motivated by the recent success of reinforcement learning in games such as Go and Dota2, we formulate Bell non-local games as a reinforcement learning problem. Such a formulation h…