Constrained quantum CNOT circuit re-synthesis using deep reinforcement learning
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2019-08-27
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en
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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.
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Faculteit der Sociale Wetenschappen