QMANN — Origin & Ownership Statement

Title: Quantum Memory-Augmented Neural Networks (QMANN)

Primary Author & Project Lead: Bayram Eker

Affiliation: Founder & AI Architect, Neura Parse Ltd.

1. Academic and Technical Origins

The QMANN (Quantum Memory-Augmented Neural Networks) architecture builds upon two distinct research foundations in artificial intelligence and quantum computing:

1. Memory-Augmented Neural Networks (MANN)

• Originated from Neural Turing Machines (Alex Graves et al., DeepMind, 2014) and Differentiable Neural Computers (Greg Wayne, Ivo Danihelka, Felix Hill et al., DeepMind, 2016).
• These works introduced the concept of coupling neural network controllers with external differentiable memory for long-term information retention.

2. Quantum Random Access Memory (QRAM) and Quantum Memory Systems

• Pioneered by Seth Lloyd, Vittorio Giovannetti, and Lorenzo Maccone (MIT, 2008, Physical Review Letters).
• Provided the theoretical framework for quantum-accelerated memory access, leveraging superposition-based addressing and Grover’s amplitude amplification.

While these domains evolved independently in the literature, no comprehensive, application-ready integration of MANN with QRAM has been formally established or documented under the designation QMANN prior to this work.

2. Inception of the QMANN Project

The QMANN project, as defined in its current architecture and technical roadmap, was initiated, architected, and authored by Bayram Eker in 2024–2025 under the auspices of Neura Parse Ltd..

This work:

• Introduces a hybrid classical–quantum controller capable of dynamic task allocation between quantum subcircuits and classical processing layers.
• Implements a QRAM-based external memory with content-based and superposition-based addressing mechanisms.
• Defines quantum-accelerated retrieval protocols for few-shot learning, large-scale knowledge base querying, and multi-step reasoning.
• Outlines scalability, hardware integration, and commercial applicability in enterprise AI systems.

3. Intellectual Property & Authorship

• Conceptual Design: Authored exclusively by Bayram Eker.
• Technical Documentation & Roadmap: Written and published under Neura Parse Ltd.
• First Use of the Term “QMANN” as a Systematic, Implementable Architecture: Introduced by Bayram Eker.
• All diagrams, architectural models, and performance benchmarks in the QMANN technical document are original works.

Ownership Statement:

QMANN, in the form described in the “QMANN: Quantum Memory-Augmented Neural Networks — Comprehensive Technical Analysis & Future Roadmap” document, is the intellectual property of Bayram Eker and Neura Parse Ltd., and represents the first formal, applied integration of Memory-Augmented Neural Networks with Quantum Memory Systems under this designation.

4. References

• Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. DeepMind Technologies.
• Wayne, G., et al. (2016). Differentiable Neural Computers. Nature.
• Giovannetti, V., Lloyd, S., & Maccone, L. (2008). Quantum Random Access Memory. Physical Review Letters, 100(16).
• Grover, L. (1996). A fast quantum mechanical algorithm for database search. Proceedings of STOC.