Genetic Algorithms for Digital Quantum Simulations

Abstract

We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols, while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors, but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.

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• Artificial Intelligence and Machine Learning for Quantum Technologies
• Differentiable Quantum Architecture Search
• Digital-Analog Quantum Computation
• Digital-Analog Quantum Simulations with Superconducting Circuits
• Neural Predictor based Quantum Architecture Search
• Quantum Artificial Life in an IBM Quantum Computer
• Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
• Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
• Calibration of quantum sensors by neural networks
• Quantum autoencoders via quantum adders with genetic algorithms
• Digital quantum simulation of many-body non-Markovian dynamics
• Quantum machine learning and quantum biomimetics: A perspective
• Quantum Architecture Search with Meta-learning
• Experimental Implementation of a Quantum Autoencoder via Quantum Adders
• Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
• Enhanced Quantum Synchronization via Quantum Machine Learning
• Approximation of quantum control correction scheme using deep neural networks
• Solving the Schrodinger equation with genetic algorithms: a practical approach
• Multi-objective evolutionary algorithms for quantum circuit discovery
• Practical quantum simulation of small-scale non-Hermitian dynamics
• Time-Optimal Quantum Driving by Variational Circuit Learning
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography
• Batched Line Search Strategy for Navigating through Barren Plateaus in Quantum Circuit Training
• Resilient superconducting-element design with genetic algorithms
• Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms