- 🧑🔬 Role: Quantum Computing Researcher
- 🎓 Research Focus: Combining Machine Learning and Quantum Computing (QML) to engineer high-performance distributed simulators and optimize QML trainability.
- 🔬 Current Focus: Deeply researching variational algorithms (VQE, QAOA), open quantum systems (Monte Carlo Trajectories), and Matrix Product State (MPS) tensor network approximations. Currently working on Grover's and Shor's algorithms, as well as foundational quantum mechanics.
- 💻 Core Expertise: High-speed simulation in 100% pure JAX (XLA), reverse engineering with x86_64 Assembly, and building advanced ML Pipelines.
- 🏆 Open Source: Contributor to TensorFlow and JAX, and developer of the 37-qubit distributed TPU simulator.
-
High-performance, differentiable quantum state-vector & tensor network simulator in 100% pure JAX (no classical framework overhead). Accelerated on NVIDIA GPUs and Google Cloud TPU v6e-64/v5e VM clusters up to 37 qubits! Supported by Google's TPU Research Cloud (TRC) program.
-
🌐 Perplexity-gui-copy: Next.js + TypeScript clone of Perplexity AI.
-
🔍 Neural-Search-Engine1: AI-powered search tool.
-
🏡 House-price-prediction: Advanced hybrid ML pipeline with Transformer DNN.