AI summaryⓘ
The authors explore a system called QDSV that helps connect high-level problem descriptions to different types of quantum computing backends. They show how this system can run quantum-related computations without needing the original problem to be written as a quantum circuit, while still producing clear outputs that separate the problem's logic from hardware-specific details. Using EEG data classification as an example, they compare their approach with classical machine learning and quantum hardware results, focusing on how their framework keeps the problem representation consistent across different execution modes. The authors emphasize that they are not claiming quantum advantage but are demonstrating stable, understandable execution across simulators and real quantum devices.
Predicate-based computationQuantum computingQDSV frameworkQuantum circuitsSemantic executionEEG classificationVariational quantum classifierQuantum hardwareMachine learningQuantum simulation
Authors
Jaime Alexander Jimenez Lozano, Sebastian Jimenez Giraldo
Abstract
Predicate-based computation over state spaces separates a problem specification from the backend that realizes it. Building on the model introduced in arXiv:2606.15027, this paper studies QDSV as a semantic, multi-backend execution framework for quantum-oriented computation. We describe how QDSV, QIntent, and Qruba connect declarative problem intent to a structured semantic representation, realize that representation under heterogeneous backend constraints, and report execution trace outputs that separate model-level semantic outputs from backend-specific observations. The framework supports execution modes that do not require the original problem to be authored as a circuit, while still allowing circuit-compatible artifacts when required. As a case study, we evaluate EEG ictal/interictal classification using prepared signal features from the Bonn and Delhi datasets. The study compares classical machine-learning references, a circuit-first variational quantum classifier baseline, QDSV simulator executions, and controlled IBM Quantum hardware runs. The paper does not claim general quantum advantage or superiority over classical machine learning. Its contribution is a semantic execution validation showing how a problem-first representation can remain stable across simulator and hardware realizations while retaining interpretable execution trace outputs.