Constrained quantum CNOT circuit re-synthesis using deep reinforcement learning
| dc.contributor.advisor | Kissinger, Aleks | |
| dc.contributor.advisor | Kwisthout, Johan | |
| dc.contributor.author | Griend, van de Arianne | |
| dc.date.issued | 2019-08-27 | |
| dc.description.abstract | In this master thesis, we describe a novel approach to constrained CNOT circuit resynthesis as a first step towards neural constrained quantum circuit re-synthesis. We train a neural network to do constrained Gaussian elimination from a parity matrix using deep reinforcement learning. The CNOT circuit is transformed into a parity matrix from which an equivalent CNOT circuit is synthesized such that all CNOT gates adhere to the connectivity constraints provided by the quantum computer architecture. For our n-step deep Q learning approach, we have used an asynchronous dueling neural network with three different action selection policies: ϵ-greedy, softmax and a novel oracle selection policy. To train this neural network, we have proposed a novel phased training procedure that guides the training process from trivial problems to arbitrary ones while simulating. Although we were only able to successfully train an agent for trivial quantum computer connectivity constraints, the 2 and 3 qubit coupling graphs. We did show that those agents were able to perform similar to the genetic Steiner baseline and could even improve on them. We also investigated the effect of coupling graph sizes and connectivity on network performance and training time. Lastly, we show that transfer learning can result in an improved network, but it takes longer to train. This is a very promising start of a new research field that could result in a universal quantum circuit optimization and mapping algorithm that is robust to both expected and unexpected future changes in quantum computer architectures. | en_US |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/10713 | |
| dc.language.iso | en | en_US |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
| dc.thesis.specialisation | Master Artificial Intelligence | en_US |
| dc.thesis.studyprogramme | Artificial Intelligence | en_US |
| dc.thesis.type | Master | en_US |
| dc.title | Constrained quantum CNOT circuit re-synthesis using deep reinforcement learning | en_US |
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