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Radiophysics and Quantum Electronics

Integrated Circuits for Quantum Machine Learning Based on Superconducting Artificial Atoms and Methods of Their Control

Aleksei Tolstobrov, Shamil Kadyrmetov, Gleb Fedorov, S. V. Sanduleanu, Viktor B. Lubsanov, Daria A. Kalacheva, Aleksey N. Bolgar, A. Yu. Dmitriev, Evgeny V. Korostylev, Konstantin S. Tikhonov, O. V. Astafiev

Published Apr 1, 2024• 4 citations
Publisher
DOI
10.1007/s11141-024-10342-7

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The design, accurate preparation and manipulation of quantum states in quantum circuits are essential operational tasks at the heart of quantum technologies. Nowadays, circuits can be designed with physical parameters that can be controlled with unprecedented accuracy and flexibility. However, the generation of well-controlled current states is still a nagging bottleneck, especially when different circuit elements are integrated together. In this work, we show how machine learning can effectively address this challenge and outperform the current existing methods. To this end, we exploit deep reinforcement learning to prepare prescribed quantum current states in circuits composed of lumped elements. To highlight our method, we show how to engineer bosonic persistent currents as they are relevant in different quantum technologies as cold atoms and superconducting circuits. We demonstrate the use of deep reinforcement learning to re-discover established protocols, as well as solve configurations that are difficult to treat with other methods. With our approach, quantum current states characterised by a single winding number or entangled currents of multiple winding numbers can be prepared in a robust manner, superseding the existing protocols.

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This work is devoted to solving the problem of processing numerical sequences using variational quantum algorithms implemented on a noisy intermediate-scale quantum computer. The quantum computing platform is an integrated circuit based on superconducting artificial atoms. The architecture of the quantum recurrent neural network is constructed using single- and two-qubit operations. At the simulation stage, we investigated the trainability of the model as a function of the number of qubits, as well as the encoded into the qubit states data amount and the encoding method. A comparison with classical architectures showed that on current quantum processors it is possible to achieve a prediction quality on the chosen machine-learning task that is comparable to that of models implemented on classical processors.
Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.
The design, accurate preparation and manipulation of quantum states in quantum circuits are essential operational tasks at the heart of quantum technologies. Nowadays, circuits can be designed with physical parameters that can be controlled with unprecedented accuracy and flexibility. However, the generation of well-controlled current states is still a nagging bottleneck, especially when different circuit elements are integrated together. In this work, we show how machine learning can effectively address this challenge and outperform the current existing methods. To this end, we exploit deep reinforcement learning to prepare prescribed quantum current states in circuits composed of lumped elements. To highlight our method, we show how to engineer bosonic persistent currents as they are relevant in different quantum technologies as cold atoms and superconducting circuits. We demonstrate the use of deep reinforcement learning to re-discover established protocols, as well as solve configurations that are difficult to treat with other methods. With our approach, quantum current states characterised by a single winding number or entangled currents of multiple winding numbers can be prepared in a robust manner, superseding the existing protocols.