Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
arXiv:2208.11167 · doi:10.3390/e25010093
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.
References in corpus (30)
- TensorFlow: A system for large-scale machine learning
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Supervised learning with quantum enhanced feature spaces
- pymoo: Multi-objective Optimization in Python
- Introduction to Quantum Noise, Measurement and Amplification
- Neural Architecture Search: A Survey
- Quantum machine learning in feature Hilbert spaces
- Parameterized quantum circuits as machine learning models
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Circuit-centric quantum classifiers
- Benchmarking Deep Reinforcement Learning for Continuous Control
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Data re-uploading for a universal quantum classifier
- OpenAI Gym
- qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor
- Robust data encodings for quantum classifiers
- Differentiable Learning of Quantum Circuit Born Machine
- Experimental Quantum Generative Adversarial Networks for Image Generation
- Training of Quantum Circuits on a Hybrid Quantum Computer
- Structure optimization for parameterized quantum circuits
- Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
- Differentiable Quantum Architecture Search
- Optimized Entanglement Purification
- NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
- Genetic Algorithms for Digital Quantum Simulations
- Markovian Quantum Neuroevolution for Machine Learning
- Quantum Architecture Search via Deep Reinforcement Learning
- Optimizing Neural Networks with Gradient Lexicase Selection