Abstract A transformative hybrid approach is proposed, combining Quantum Machine Learning (QML) with both traditional and meta-heuristic feature selection algorithms to overcome the complexities and limitations of conventional credit card fraud detection methods. In this study, advanced data balancing techniques such as SMOTE-ENN (Synthetic Minority Over-sampling Technique–Edited Nearest Neighbors) and Random Under Sampler (RUS) are employed on the imbalanced European Cardholder Dataset to address class imbalance and enhance model resilience. Core feature selection algorithms—both traditional methods like K-Best and meta-heuristic techniques including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Atom Search Optimization (ASO)—are systematically evaluated, each paired with the Variational Quantum Classifier (VQC) for classification. Remarkably, the PSO+VQC combination achieves an accuracy rate of 94.54%, underscoring the efficacy of integrating meta-heuristic algorithms with VQC to manage complex, high-dimensional data in fraud detection. These findings highlight QML and meta-heuristic algorithms’ potential to surpass conventional methods, delivering superior accuracy and efficiency in critical, data-intensive applications such as financial fraud detection.
THE EL-RAKHAWI PHOTONIC NEURO-QUANTUM MEMORY ARRAY (PNQMA) by Dr. M.K.A. El-Rakhawi (DOI: 10.5281/zenodo.23542213) shatters the quantum error correction latency bottleneck through a hybrid architecture achieving real-time syndrome decoding at the speed of light. **Core Innovation: Hybrid Photonic-Quantum Interface**The PNQMA integrates three scientifically validated pillars:1. **Non-Volatile TFLN Ferroelectric Memristors**: Stores neuromorphic decoding weights via optically induced ferroelectric domain switching (not transient Kerr effect), eliminating O-E-O conversion and static power consumption. 2. **In-Memory Photonic Decoding**: TFLN Mach-Zehnder interferometer meshes perform instantaneous optical matrix multiplication on syndrome signals, executing quantum error decoding in picoseconds. 3. **Cryogenic Electro-Optic Coupling**: Interfaces photonic decoder with superconducting bosonic cat qubits via Pockels effect transducers, generating precise corrective microwave pulses. **Critical Engineering Refinements:**- **Multi-Stage Cryogenic Topology**: TFLN chip mounted at intermediate stage (1K-4K) balances ferroelectric non-volatility with minimal thermal load on 10-20 mK quantum processor.- **Cryogenic Microwave-to-Optical Transducers**: Piezo-optomechanical crystals preserve phase integrity during signal up-conversion.- **Sub-5 Nanosecond Latency**: 1000x improvement over CMOS-based decoding, enabling phase-flip correction within coherence window.- **Error Correction Overhead Reduction**: Cat qubit intrinsic protection reduces physical-to-logical qubit ratio from 1000:1 to 10:1. IP protection excludes natural phenomena per Alice/Myriad precedents, safeguarding only concrete implementations (ferroelectric domain switching architectures, cryogenic electro-optic transducers, multi-stage thermal topology), ensuring robust patent eligibility for this quantum-classical interface breakthrough.
The evolution of relational DBMS is considered under the conditions of exhausting the scalability of classical architecture, which necessitates a transition to quantum computing. Methods of quantum data storage, QCOS and QROS, are proposed, along with a hybrid λcontract QCONTRAST for managing probabilistic query results. Native quantum data types and operations are introduced as an extension to SQL. Compatibility of ACID with the quantum nature is analyzed. It is argued that quantum algorithms (Grover's, QUBO, QAOA, etc.) continue the evolutionary trend of indatabase machine learning. Requirements for the SQL:2029 standard in the context of the quantum transition are formulated.