Quantum Neural Interface Integration Framework

Maria Perera

B Dinesh, M. Thangamani, S. Tamizharasu +3 more

This paper introduces a computer science framework for large-scale brain–quantum interface computing by integrating bivariate bicycle code quantum low-density parity-check (qLDPC) codes with a neural spike encoding pipeline. The proposed system achieves 200 logical qubits using sparse circulant constructions, maintaining fault tolerance and constant encoding rate under realistic noise models. We present a mathematical mapping from real-time neural spike streams to logical registers, implement a hardware-aware belief-propagation decoder, and optimize quantum circuit placement for minimal routing overhead. Real-time emulation using live neural surrogate data and calibrated quantum noise confirms a logical error probability below 10−8per cycle and end-to-end information retention exceeding 99.7%. These results demonstrate the feasibility and scalability of robust, low-latency neural signal processing on quantum hardware, paving the way for dependable quantum brain–computer interfaces.

Subhash C. Kak

mohamed kamal arafa el-rakhawi

The *El-Rakhawi Doctrine of Hybrid Quantum-Silicon Neural Interfaces* (2026) by Dr. Mohamed Kamal Arafa El-Rakhawi integrates silicon spin qubits, silicon photonics, and neuromorphic computing for adaptive neural interfaces. It resolves the thermal contradiction of implantable quantum systems through a Hybrid Implantable-External Architecture: bio-compatible neural electrodes and photonic telemetry are implanted at 310K, while data is transmitted transcutaneously via optical links to an External Cryo-Processing Unit housing 1K silicon spin qubits. It corrects the quantum data-loading bottleneck using Variational Quantum Circuits (VQCs) coupled with Neuromorphic Pre-compression, providing Probabilistic Representation Efficiency rather than absolute exponential speedup. The doctrine provides a realistic 12-15 year clinical pathway and establishes a rigorous IP framework with provisional patent filings, clearly delineating trade secrets from open-source components. Released under CC BY 4.0. DOI: 10.5281/zenodo.23214359.

Michail A. Zak, Colin P. Williams

Poornima Kumaresan, Shwetha Singaravelu, Lakshmi Rajendran +1 more

Quantum machine learning (QML) stands at the intersection of quantum computing and artificial intelligence, offering the potential to solve problems that remain intractable for classical methods. However, the current landscape of QML software frameworks suffers from severe fragmentation: models developed in TensorFlow Quantum cannot execute on PennyLane backends, circuits authored in Qiskit Machine Learning cannot be deployed to Amazon Braket hardware, and researchers who invest in one ecosystem face prohibitive switching costs when migrating to another. This vendor lock-in impedes reproducibility, limits hardware access, and slows the pace of scientific discovery. In this paper, we present a framework-agnostic quantum neural network (QNN) architecture that abstracts away vendor-specific interfaces through a unified computational graph, a hardware abstraction layer (HAL), and a multi-framework export pipeline. The core architecture supports simultaneous integration with TensorFlow, PyTorch, and JAX as classical co-processors, while the HAL provides transparent access to IBM Quantum, Amazon Braket, Azure Quantum, IonQ, and Rigetti backends through a single application programming interface (API). We introduce three pluggable data encoding strategies (amplitude, angle, and instantaneous quantum polynomial encoding) that are compatible with all supported backends. An export module leveraging Open Neural Network Exchange (ONNX) metadata enables lossless circuit translation across Qiskit, Cirq, PennyLane, and Braket representations. We benchmark our framework on the Iris, Wine, and MNIST-4 classification tasks, demonstrating training time parity (within 8\% overhead) compared to native framework implementations, while achieving identical classification accuracy.