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Neural Predictor based Quantum Architecture Search
- Publication Year :
- 2021
- Publisher :
- arXiv, 2021.
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Abstract
- Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum-classical hybrid computational paradigm in the near term. Both theoretical and practical developments of VQAs share many similarities with those of deep learning. For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning. Partly inspired by the recent success of AutoML and neural architecture search (NAS), quantum architecture search (QAS) is a collection of methods devised to engineer an optimal task-specific PQC. It has been proven that QAS-designed VQAs can outperform expert-crafted VQAs under various scenarios. In this work, we propose to use a neural network based predictor as the evaluation policy for QAS. We demonstrate a neural predictor guided QAS can discover powerful PQCs, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning. Notably, neural predictor guided QAS provides a better solution than that by the random-search baseline while using an order of magnitude less of circuit evaluations. Moreover, the predictor for QAS as well as the optimal ansatz found by QAS can both be transferred and generalized to address similar problems.<br />Comment: 10.2 pages + supplemental materials, 10 figures
- Subjects :
- Quantum Physics
Quantum machine learning
Artificial neural network
Computer science
business.industry
Deep learning
Quantum simulator
Parameterized complexity
FOS: Physical sciences
Human-Computer Interaction
Quantum circuit
Artificial Intelligence
Quantum algorithm
Artificial intelligence
business
Quantum Physics (quant-ph)
Quantum
Software
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....d31295ee087456b8df0b33b1ed93c43b
- Full Text :
- https://doi.org/10.48550/arxiv.2103.06524