co9olguy
commented
3 years ago Thanks for your Power Up Submission @wongwsvincent !
To help us keep track of final submissions, we will be closing all of the [Power Up] issues. We ask you to open a new issue for your final submission. Please use this pre-formatted [Entry] Issue template. Note that for the final submission, the Resource Estimate requirement is replaced by a Presentation item.
Team Name:
Giglampshare
Project Description:
There are various proposals to apply quantum computing to reinforcement learning. This experiment serves as a proof-of-concept demonstration, testing the power of quantum circuits in decision-making by interacting with the environment. Two aspects will be studied: (1) To compare the performance between classical and quantum machine learning in reinforcement learning tasks, a Quantum Variational Circuit (QVC) is tested against classical machine learning methods (SARSA, Q-learning, etc.). (2) To investigate the potential improvement that could be gain from an ensemble of quantum learners.
The experiment will start by testing with a simple (8x8) maze-solving task, aiming to show that a bunch of weak quantum maze runners could increase the chance of surviving the maze. Possible extensions to the project would be implementing quantum energy-based models, or comparing quantum and classical models in various reinforcement learning environments (like gym AI, etc.).
Source code:
https://github.com/wongwsvincent/Pennylane_quantum-variational-circuit_RL
Resource Estimate:
We intend to use the power-up prize to further investigate the algorithms and test the scalability of the algorithms.
The initial testing will be carried out with local simulators on Amazon Braket. Therefore, we only need to spend on the final test that investigates the impact of noise error from quantum devices. IonQ Q11 is chosen here for the final testing.
IonQ Q11 costs: $0.30 per task + $0.01 per shot And we will request O(100) shots per task, and ~2 tasks on average are needed to perform one epoch of learning, which gives us ~$2 per epoch. Each run takes a maximum of 50 epochs, so each run costs ~$100. A total of 20 runs will be run for the study, which gives us ~$2000.