FT-FM: A Financial-Transaction-Specific Quantum Feature Map for Variational Quantum Classifiers in Fraud Detectio
Jean Marie Vianney Sindayigaya
Jul 2026· International Journal of Electronics and Telecommunications· 0 citations· 27 references
TL;DR
Preliminary empirical characterisation on synthetic financial-transaction data confirms FT-FM's predicted expressibility advantage over generic feature maps and exposes a kernel-concentration phenomenon that motivates a refined construction with a tunable entanglement-strength hyperparameter.
Abstract
Preliminary empirical characterisation on synthetic financial-transaction data confirms FT-FM's predicted expressibility advantage over generic feature maps (Kullback– Leibler divergence to the Haar distribution of 4.01 ± 1.16 versus 21.24 ± 0.79 for a ZZ-like baseline and 19.53 ± 0.43 for an amplitude-like baseline at matched qubit count), and exposes a kernel-concentration phenomenon (Meyer–Wallach Q = 0.948 ± 0.004) that motivates a refined construction (FT-FM-lite) with a tunable entanglement-strength hyperparameter; full empirical benchmarking on Kaggle CCF, CICIDS-2017, and UNSW-NB15 is the subject of a forthcoming paper in this series.
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.
Menachem Finkelstein, Diana Levy, Sarel Cohen et al.· Proceedings of the 19th ACM...· 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
In this study, we developed and evaluated four conditional energy-based forecasting architectures: a classical Gaussian-Bernoulli CRBM, a hybrid quantum-classical QCRBM, a full-register QQRBM, and a lag-feature QFeatureQRBM with complete derivations of their conditional distributions, Contrastive-Divergence gradients,...
Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer et al.· arXiv.org· 0 citations
This work evaluates a 10-qubit hybrid quantum-classical variational circuit for paraphrase detection across three benchmarks: MRPC, Quora Question Pairs, and adversarial PAWS to constitute the first systematic parameter-matched multi-benchmark empirical validation of hybrid variational circuits for NLP.
Farha Nausheen, K. Ahmed, Farina Riaz· 0 citations
It is shown that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring...
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
