Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning

Abstract

The complexity of experimental quantum information processing devices is increasing rapidly, requiring new approaches to control them. In this paper, we address the problems of practically modeling and controlling an integrated optical waveguide array chip, a technology expected to have many applications in telecommunications and optical quantum information processing. This photonic circuit can be electrically reconfigured, but only the output optical signal can be monitored. As a result, the conventional control methods cannot be naively applied. Characterizing such a chip is challenging for three reasons. First, there are uncertainties associated with the Hamiltonian describing the chip. Second, we expect distortions of the control voltages caused by the chip's electrical response, which cannot be directly observed. Finally, there are imperfections in the measurements caused by losses from coupling the chip externally to optical fibers. We developed a deep neural network approach to solve these problems. The architecture is designed specifically to overcome the aforementioned challenges using a Gated Recurrent Unit (GRU)-based network as the central component. The Hamiltonian is estimated as a blackbox, while the rules of quantum mechanics such as state evolution is embedded in the structure as a whitebox. The resulting overall graybox model of the chip shows good performance both quantitatively in terms of the mean square error and qualitatively in terms of the predicted waveforms. We use this neural network to solve a classical and a quantum control problem. In the classical application we find a control sequence to approximately realize a time-dependent output power distribution. For the quantum application we obtain the control voltages to realize a target set of quantum gates. The proposed method is generic and can be applied to other systems that can only be probed indirectly.

arXiv commentAdded some references, corrected some typos, enhanced presentation

References in corpus (21)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum walks of correlated particles
• Chopped random-basis quantum optimization
• Observation of Entangled States of a Fully Controlled 20-Qubit System
• Quantum and classical correlations in waveguide lattices
• Reinforcement Learning in Different Phases of Quantum Control
• Active learning machine learns to create new quantum experiments
• Experimental Quantum Hamiltonian Learning
• Automated Search for new Quantum Experiments
• Hamiltonian Learning and Certification Using Quantum Resources
• Robust Online Hamiltonian Learning
• Gradient optimization of analytic controls: the route to high accuracy quantum optimal control
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Quantum Hamiltonian Learning Using Imperfect Quantum Resources
• Error correction in quantum cryptography based on artificial neural networks
• Efficiently measuring a quantum device using machine learning
• Quantum topology identification with deep neural networks and quantum walks
• Efficient online quantum state estimation using a matrix-exponentiated gradient method
• Approximation of quantum control correction scheme using deep neural networks

Cited by in corpus (16)

• Deep learning in nano-photonics: inverse design and beyond
• Learning Quantum Systems
• Computer-inspired Quantum Experiments
• Quantum machine learning and quantum biomimetics: A perspective
• Reconfigurable continuously-coupled 3D photonic circuit for Boson Sampling experiments
• Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering
• Artificial Intelligence for Quantum Computing
• Quantum Geometric Machine Learning for Quantum Circuits and Control
• Waveguide lattice based architecture for multichannel optical transformations
• Programmable quantum circuits in a large-scale photonic waveguide array
• On-chip quantum interference between independent lithium niobate-on-insulator photon-pair sources
• Noise Detection with Spectator Qubits and Quantum Feature Engineering
• Experimental graybox quantum system identification and control
• Quantum reinforcement learning in the presence of thermal dissipation
• Multi-Axis Control of a Qubit in the Presence of Unknown Non-Markovian Quantum Noise
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning