AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A quantum machine-learning model implemented on a programmable superconducting quantum annealer that processes temporal data at large scale using up to 4,500 qubits is reported-the largest quantum machine-learning experiment performed to date.
This work identifies a class of cyclic stabilizer codes realizable through global iSWAP and single-qubit gates, yielding QEC thresholds nearly seven orders of magnitude larger than previous estimates for globally-controlled arrays.
We show that global optimal control can drastically suppress the impact of decoherence in globally driven superconducting quantum computing architectures, taking as a prototype a recently proposed quasi-two-dimensional ladder geometry. Using a tensor-network-based approach, we quantify how amplitude-damping and dephasi...
Group Reservoir Computing is introduced, an efficient machine-learning paradigm for learning temporal dynamics whose training reduces to a single linear regression, to reduce the resources required.
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