QTIS: A QAOA-Based Quantum Time Interval Scheduler

• URL: http://arxiv.org/abs/2511.15590v1
• Date: Wed, 19 Nov 2025 16:29:14 GMT
• Title: QTIS: A QAOA-Based Quantum Time Interval Scheduler
• Authors: José A. Tirado-Domínguez, Eladio Gutiérrez, Oscar Plata,
• Abstract summary: The proposed method integrates an ancilla-assisted quantum circuit to dynamically detect and penalize overlapping tasks.<n>Results confirm the efficiency of QTIS in scheduling tasks with fixed temporal windows while minimizing conflicts.
• Score: 0.22940141855172033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Task scheduling with constrained time intervals and limited resources remains a fundamental challenge across domains such as manufacturing, logistics, cloud computing, and healthcare. This study presents a novel variant of the Quantum Approximate Optimization Algorithm (QAOA) designed to address the task scheduling problem formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model. The proposed method, referred to as Quantum Time Interval Scheduler (QTIS), integrates an ancilla-assisted quantum circuit to dynamically detect and penalize overlapping tasks, enhancing the enforcement of scheduling constraints. Two complementary implementations are explored for overlap detection: a quantum approach based on RY rotations and CCNOT gates, and a classical alternative relying on preprocessed interval comparisons. QTIS decomposes the problem Hamiltonian, Hp, into two components, each parameterized by a distinct angle. The first component encodes the objective function, while the second captures penalty terms associated with overlapping intervals, which are controlled by the auxiliary circuit. Subsequently, three minimization strategies are evaluated: standard QAOA, T-QAOA, and HT-QAOA, showing that employing separate parameters for the different components of the problem Hamiltonian leads to lower energy values and improved solution quality. Results confirm the efficiency of QTIS in scheduling tasks with fixed temporal windows while minimizing conflicts, demonstrating its potential to advance hybrid quantum-classical optimization in complex scheduling environments.

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