Free Energybased Reinforcement Learning Using A Quantum Processor
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**arXiv ID:** 1706.00074 **Authors:** Anna Levit, Daniel Crawford, Navid Ghadermarzy, Jaspreet S. Oberoi, Ehsan Zahedinejad, Pooya Ronagh **Published:** 2017-05-29T18:57:42Z **Abstract:** Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum ...
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# Free energy-based reinforcement learning using a quantum processor
**arXiv ID:** 1706.00074
**Authors:** Anna Levit, Daniel Crawford, Navid Ghadermarzy, Jaspreet S. Oberoi, Ehsan Zahedinejad, Pooya Ronagh
**Published:** 2017-05-29T18:57:42Z
**Abstract:**
Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum annealer's measured qubit spin configurations in approximating the free energy of a quantum Boltzmann machine (QBM). We then apply this method to perform reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum annealer. The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
## Skill Description
This skill is generated from the arXiv paper: Free energy-based reinforcement learning using a quantum processor (1706.00074).
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## References
- [arXiv:1706.00074](http://arxiv.org/abs/1706.00074v1)
