Sebastian Jimenez Giraldo

arXiv2026

QDSV: A Semantic Problem Representation and Multi-Backend Execution Framework for Quantum-Oriented Computation

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.

cs.PL↗

arXiv2026

A Predicate-Based Model for Computation over State Spaces

Many mainstream programming interfaces represent computation procedurally, as sequences of instructions, control-flow constructs, and explicit execution steps. However, several important classes of problems are more naturally described declaratively: one specifies the set of candidate states and the condition that makes a state valid. This paper formalizes a predicate-based abstraction for computation over state spaces. A computational problem is represented by a state space S and a predicate C: S -> {0,1}. Solutions are the states that satisfy the predicate, while execution is delegated to a realization strategy for evaluating, sampling, searching, or otherwise characterizing this solution set. We introduce a minimal semantic-preservation contract that distinguishes the problem specification from backend-specific evaluators and establishes when composed predicates preserve their meaning across realizations. The contribution is a unifying abstraction and preservation contract, rather than a new class of constraint problems or a claim that predicate evaluation is always efficient. Procedural algorithms, solvers, probabilistic methods, and quantum oracles are treated as possible realizations of the same semantic specification. The model is related to constraint satisfaction, satisfiability, logic programming, relational query processing, model checking, high-level quantum languages, and quantum intermediate representations. Its relevance to quantum computation follows from the fact that a Boolean predicate can be materialized, when finite and efficiently representable, as a reversible or phase oracle over computational basis states. This makes the abstraction a bridge between declarative problem specification and quantum-oriented execution without requiring the problem itself to be stated as a circuit.

cs.PL↗