MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
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Energy modeling and parameter optimization for cat-qubit superconducting quantum computers performing semiclassical QFT with error correction indicates an energetic advantage over classical systems for more than 26 qubits under cryogenic assumptions.
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MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
MerLin is a new open-source discovery engine for photonic and hybrid quantum machine learning that integrates circuit simulations into standard ML frameworks and reproduces 18 prior works as reusable benchmarks.
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Unveiling Energetic Advantage in Superconducting Cat-Qubits Quantum Computation
Energy modeling and parameter optimization for cat-qubit superconducting quantum computers performing semiclassical QFT with error correction indicates an energetic advantage over classical systems for more than 26 qubits under cryogenic assumptions.