<p>The intersection of novel computing paradigms and Quantum Computing (QC) promises to unlock unprecedented computational capabilities, yet a significant challenge remains in developing learning models that truly align with quantum principles. Many current approaches involve adapting classical frameworks for quantum computation. However, this translation is often complex, requiring routines that do not map intuitively onto a QC’s native operations. In this work, we introduce a fundamentally new paradigm: Quantum Hyperdimensional Computing (QHDC). We demonstrate that the core operations of its classical counterpart, Hyperdimensional Computing (HDC), a brain-inspired model, map with remarkable elegance and direct correspondence onto the native operations of a QC. We present the first-ever implementation of this framework that we validated through two distinct experiments: a symbolic analogical reasoning task and a data-driven supervised classification challenge. The viability of QHDC is rigorously assessed via a comparative analysis of results from classical computation, ideal quantum simulation, and execution on a state-of-the-art 156-qubit IBM Heron r3 quantum processor. Our results validate the proposed mappings and demonstrate the framework’s versatility, establishing QHDC as a physically realizable technology. This work lays the foundation for a new class of quantum neuromorphic algorithms designed to run natively on quantum hardware.</p>