Publications
Research papers, selected book-length work, and open source projects in AI, quantum computing, and machine learning by Shlomo Kashani and the QNeura team.
Featured Publications
Research papers, open source releases, and book-length work advancing AI and quantum computing.
Deep Learning Interviews
Hundreds of Fully-Solved Interview Questions
Essential reference for candidates and teams: curated questions and step-by-step solutions across ML theory, deep learning, systems, and practical engineering.
QuantumLLMInstruct
Cutting-Edge Quantum AI Research
QuantumLLMInstruct (QLMMI) is a pioneering dataset with over 500,000 curated problem-solution pairs for quantum computing, the largest of its kind. Built from over 90 seed domains and LLM-generated subdomains, it enhances instruction fine-tuning to improve LLM performance in quantum physics challenges.
Using a four-stage methodology, QLMMI covers areas like synthetic Hamiltonians, QASM code generation, and Trotter-Suzuki decompositions with advanced reasoning techniques.
metalQwen3
High-Performance LLM Inference with Metal Shaders
metalQwen3 is a GPU-accelerated LLM inference stack using CUDA, Vulkan, and Metal Shaders for high-throughput, low-latency serving with custom kernels and memory optimizations.
mlx-Quantum
Quantum Framework for Apple Silicon
Efficient quantum circuit simulation and QML on Apple's MLX, optimized for Apple Silicon with hybrid quantum-classical workflows.
QonFusion
Quantum Approaches to Gaussian Random Variables
QonFusion presents a novel strategy for generating Gaussian random variables using non-parametric quantum circuits, offering an alternative to traditional pseudorandom number generators. The research integrates Quantum Random Number Generators (QRNGs) into classical diffusion models.
This work demonstrates applications in Stable Diffusion and Brownian Motion, bridging quantum computing with classical machine learning through PyTorch integration.
Teaching Quantum Entanglement with Julia
Pedagogical Framework for Quantum Computing Education
This educational research explores the use of the Yao.jl quantum computing framework, written in Julia, for teaching quantum entanglement concepts to graduate students. The work provides a comprehensive pedagogical approach to understanding quantum entanglement.
Quantum Fourier Transform for Music
QFT-Based Note Detection Algorithm
This innovative research applies quantum computing principles to music processing by developing a note detection algorithm based on the Quantum Fourier Transform (QFT). The work demonstrates the potential of quantum algorithms in signal processing applications.
Pulmonary Nodule Detection
Ensemble Deep Learning for Medical Imaging
This medical AI research presents a sophisticated ensemble approach for detecting pulmonary nodules in medical imaging, combining the strengths of 3D SE-ResNet18 and DPN68 deep learning architectures for early lung cancer detection.