Neural-IC separates embedding inequalities from capacity bounds in query-separated computations, with one-bit RAC benchmarks and CHSH-layer stability selecting the Tsirelson threshold for quantum enhancements.
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Experimental runs on a superconducting quantum processor demonstrate that 20-qubit quantum neural networks are more resistant to adversarial attacks than classical networks, with adversarial training further improving robustness and empirical bounds closely matching theory.
Structure-aware approximate compilation for Hamiltonian dynamics on NISQ devices produces shallower circuits with higher observed fidelity than generic exact synthesis.
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.
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Neural Information Causality
Neural-IC separates embedding inequalities from capacity bounds in query-separated computations, with one-bit RAC benchmarks and CHSH-layer stability selecting the Tsirelson threshold for quantum enhancements.
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Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor
Experimental runs on a superconducting quantum processor demonstrate that 20-qubit quantum neural networks are more resistant to adversarial attacks than classical networks, with adversarial training further improving robustness and empirical bounds closely matching theory.
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Hardware-Efficient Hamiltonian Simulation via Trotter-Initialized Variational Optimization with Native Placement
Structure-aware approximate compilation for Hamiltonian dynamics on NISQ devices produces shallower circuits with higher observed fidelity than generic exact synthesis.
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Quantum computation at the edge of chaos
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.