Sep 2026· Proceedings of the 19th ACM International Systems and Storage Conference· pp. 167-167· 0 citations· 3 references
TL;DR
This work adds the missing non-linearity to UCI Default of Credit Card Clients data with a quantum-inspired feature map, specific to linear models: Random Forest, SVM, XGBoost, and k-NN, already non-linear, do not benefit.
Abstract
Banks predict credit default with Logistic Regression because regulators can read its coefficients—but it cannot express interactions. We add the missing non-linearity with a quantum-inspired feature map: 8 of the data's 23 columns become rotation angles of 8 qubits in an IQP circuit simulated in PennyLane, and 2 × 8 = 16 outputs are appended to the 23. On the UCI Default of Credit Card Clients data (30,000 clients, 5-fold CV) this lifts it from F1 = 0.462 to 0.517, while Kernel PCA, the strongest classical alternative at the same budget, reaches only 0.493; the gap survives FDR correction over 12 tests (p = 0.00007). The gain is specific to linear models: Random Forest, SVM, XGBoost, and k-NN, already non-linear, do not benefit. What caps n is the simulator, holding 2n amplitudes; on hardware the depth is independent of n.
It is shown that how the 8 input features are chosen matters: Random Forest importance-guided selection reaches F1 = 0.523, while encoding maximally uncorrelated features drops it to 0.496, demonstrating that the circuit amplifies informative structure rather than creating it from scratch.
Menachem Finkelstein, Diana Levy, Z. Yakhini et al.· 0 citations
Reliable attribution of artificial intelligence (AI)-generated text to a specific large language model (LLM) matters increasingly as LLMs proliferate, yet where quantum machine learning actually stands on this task has, to our knowledge, never been measured systematically. We benchmark the quantum support vector machin...
Kalin Kopanov, Tatiana V. Atanasova· Information· 0 citations
A quantum kernel fusing amplitude encoding of l2-normalized delay windows with a grouped single-qubit rotation layer that assigns contiguous temporal blocks to each qubit, making the circuit delay-window-aware and the first mechanistic localisation of quantum-kernel advantage to a specific dynamical regime of a classic...
Zhi-Hui Wang, Sujit Roy, Ata Akbari et al.· 0 citations
A layout-based marginalisation fix is implemented, merged into the GitHub codebase as Pull Request \#1041, that makes \texttt{SamplerQNN} postprocessing forward-compatible with current and upcoming hardware.
Soraya V. Panambalom, Edoardo Altamura, Nicholas Chancellor et al.· 0 citations
Selecting a data encoding is a central and poorly tooled decision in quantum machine learning. The feature map fixes the geometry of the Hilbert space, the expressibility of quantum kernels, and whether the circuit can run on near-term hardware. This paper presents Quantum Encoding Agents, an open-source system that tu...
It is explained that the efficiently preparable states, device-generated distributions, variationally learned loading, and amortized preparation are required to get advantage from quantum machine learning and close with a checklist for evaluating input-dependent advantage claims.
M. Faryad· 1 citation
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