σ-VQE uses low-depth circuits and an energy-selective cost function to preferentially prepare quantum many-body scar states on NISQ devices.
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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.
Quantum kernel ridge regression shows double descent in test risk, with the interpolation peak suppressible by regularization, via random matrix theory asymptotics in the high-dimensional limit.
Analog quantum kernels with operational noise outperform noiseless versions in benchmarking and non-Markovianity estimation due to increased expressivity and model complexity.
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.
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$\sigma$-VQE: Excited-state preparation of quantum many-body scars with shallow circuits
σ-VQE uses low-depth circuits and an energy-selective cost function to preferentially prepare quantum many-body scar states on NISQ devices.
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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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Double Descent in Quantum Kernel Ridge Regression
Quantum kernel ridge regression shows double descent in test risk, with the interpolation peak suppressible by regularization, via random matrix theory asymptotics in the high-dimensional limit.
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Noise-enhanced quantum kernels on analog quantum computers
Analog quantum kernels with operational noise outperform noiseless versions in benchmarking and non-Markovianity estimation due to increased expressivity and model complexity.
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Compton Form Factor Extraction using Quantum Deep Neural Networks
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.