Evolutionary Quantum Architecture Search for Parametrized Quantum
Circuits
- URL: http://arxiv.org/abs/2208.11167v1
- Date: Tue, 23 Aug 2022 19:47:37 GMT
- Title: Evolutionary Quantum Architecture Search for Parametrized Quantum
Circuits
- Authors: Li Ding, Lee Spector
- Abstract summary: We introduce EQAS-PQC, an evolutionary quantum architecture search framework for PQC-based models.
We show that our method can significantly improve the performance of hybrid quantum-classical models.
- Score: 7.298440208725654
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in quantum computing have shown promising computational
advantages in many problem areas. As one of those areas with increasing
attention, hybrid quantum-classical machine learning systems have demonstrated
the capability to solve various data-driven learning tasks. Recent works show
that parameterized quantum circuits (PQCs) can be used to solve challenging
reinforcement learning (RL) tasks with provable learning advantages. While
existing works yield potentials of PQC-based methods, the design choices of PQC
architectures and their influences on the learning tasks are generally
underexplored. In this work, we introduce EQAS-PQC, an evolutionary quantum
architecture search framework for PQC-based models, which uses a
population-based genetic algorithm to evolve PQC architectures by exploring the
search space of quantum operations. Experimental results show that our method
can significantly improve the performance of hybrid quantum-classical models in
solving benchmark reinforcement problems. We also model the probability
distributions of quantum operations in top-performing architectures to identify
essential design choices that are critical to the performance.
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