AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network
- URL: http://arxiv.org/abs/2210.02766v1
- Date: Thu, 6 Oct 2022 09:05:42 GMT
- Title: AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network
- Authors: Kentaro Murakami, Jianjun Zhao
- Abstract summary: AutoQC is an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs.
We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing with a neural network at each step.
- Score: 1.7704011486040847
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While the ability to build quantum computers is improving dramatically,
developing quantum algorithms is limited and relies on human insight and
ingenuity. Although a number of quantum programming languages have been
developed, it is challenging for software developers who are not familiar with
quantum computing to learn and use these languages. It is, therefore, necessary
to develop tools to support developing new quantum algorithms and programs
automatically. This paper proposes AutoQC, an approach to automatically
synthesizing quantum circuits using the neural network from input and output
pairs. We consider a quantum circuit a sequence of quantum gates and synthesize
a quantum circuit probabilistically by prioritizing with a neural network at
each step. The experimental results highlight the ability of AutoQC to
synthesize some essential quantum circuits at a lower cost.
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