Superconducting quantum computing optimization based on multi-objective deep reinforcement learning by Yangting Liu

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Noise-Aware Quantum Amplitude Estimation by Steven Herbert, Ifan Williams, Roland Guichard, Darren Ng

“…We provide results from quantum amplitude estimation run on various IBM superconducting quantum computers and on Quantinuum's H1 trapped-ion quantum computer to show that the proposed model is a good fit for real-world experimental data. …”
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Sparse Blossom: correcting a million errors per core second with minimum-weight matching by Oscar Higgott, Craig Gidney

“…For 0.1% circuit-level depolarising noise, sparse blossom processes syndrome data in both $X$ and $Z$ bases of distance-17 surface code circuits in less than one microsecond per round of syndrome extraction on a single core, which matches the rate at which syndrome data is generated by superconducting quantum computers. Our implementation is open-source, and has been released in version 2 of the PyMatching library.…”
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Comparative study of quantum error correction strategies for the heavy-hexagonal lattice by César Benito, Esperanza López, Borja Peropadre, Alejandro Bermudez

“…In this work, we make a comparative study of possible strategies to overcome this limitation for the heavy-hexagonal lattice, the architecture of current IBM superconducting quantum computers. We explore two complementary strategies: the search for an efficient embedding of the surface code into the heavy-hexagonal lattice, as well as the use of codes whose connectivity requirements are naturally tailored to this architecture, such as subsystem-type and Floquet codes. …”
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