Barren plateaus analyzed for quantum linear combinations of unitaries
A single-pass LCU keeps a gradient-variance lower bound, while interleaved Haar sampling can concentrate exponentially.
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Stories tagged #quantum-machine-learning
Secondary analysis finds detection trade-offs in quantum ML intrusion detection
In one comparison a quantum SVM lowers false positives from 6.875% to 3.329% while true positives dip slightly.
Quantum and classical models compared on dark-web link prediction
A four-qubit classifier stayed near chance under degree control, while VGAE reached mean ROC-AUC about 0.91.
Quantum Hamiltonian embeddings proposed for supervised learning
A dataset is encoded into one Hamiltonian whose ground state classifies; the method was demonstrated on IBM Heron processors.
Q-MALDetect study measures evasion of a quantum malware detector
The hybrid detector reached 97.2% training accuracy, but the strongest feature-space attack succeeded 78.7% of the time.
Paper links spectral queries to inductive bias in quantum circuits
Encoding a fixed spectral query changes how existing signal frequencies combine without adding sample-dependent information.
Mu-DisCoCat: A Variational Pipeline for Compositional Generalization on Quantum Processors
Mu-DisCoCat uses variational quantum circuits to improve compositional generalization on noisy near-term quantum hardware.
A Width-Matched Comparison of Hybrid Quantum-Classical Self-Supervised Learning for Fingerprint Recognition
Hybrid quantum SSL beats matched classical models on fingerprints only for contrastive objectives, not reliably for BYOL.
Quantum score matching with applications to learning thermal states
Quantum score matching learns Gibbs states optimally at high temperature and runs on IBM hardware.
Observational Indistinguishability and Integrity Blind Regions in Hybrid Quantum-Classical Workflows
Framework formalizes integrity blind regions in hybrid quantum-classical workflows, validated across 3,600 label interventions with conformal detection rules.
Certifying Adversarial Robustness of Quantum Classifiers under Known-Readout Query Access
Framework certifies adversarial robustness of quantum classifiers using only measurement statistics and finite-shot outcomes, demonstrated on IBM Quantum hardware.
Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers
Quantum IQP circuit features lift logistic-regression credit-default F1 from 0.462 to 0.517, beating Kernel PCA at an equal feature budget.