Hi, I'm Ashitesh Singh 👋

Quantum Computing · Machine Learning · Cybersecurity

👋 About Me

🧑‍🔬 Role
Quantum Computing Researcher

🤖 Right now
ML Engineer & Research Fellow at AnthologyResearch (Polymath AI Labs)

🎓 Research Focus
Combining Machine Learning and Quantum Computing (QML) to engineer high-performance distributed simulators and optimize QML trainability.

🏆 Open Source Contributor to TensorFlow (18+ PRs) & OpenXLA (1 PR)
Authored and merged 19 contributions spanning distributed training (tf.distribute.AutoStrategy), core variable slice assignment, quantization layers (QuantizedDense), float64 autodiff numerical stability (tanh and softmax VJPs, logdet), C++ CPU/MKL kernels (Digamma, segment reductions), XLA HLO compiler lowering, and Python runtime safety.

🏫 Education
Grade 12 at Little Flower School, managing tech alongside academics.

🛡️ Cybersecurity Work

During independent security research and Android reverse engineering, I discovered a vulnerability chain in a school ERP platform involving insecure credential storage, excessive cloud permissions, and weaknesses in access controls.

If exploited, these vulnerabilities could have led to a data breach affecting an estimated 5 million+ students, parents, and teachers across India. This represents the estimated potential platform-wide impact.

I coordinated responsible disclosure through CERT-In, documenting the findings to support verification and remediation. My published write-up covers the investigation, disclosure timeline, vendor remediation, and CERT-In acknowledgment.

📖 Read the responsible disclosure write-up →

🛠️ Tech Stack & Language Toolbox

🤖 Tools & LLMs I Use

AI tools and assistants in my workflow

Codex

Kiro

Antigravity

DeepSeek

Gemini

🏆 Featured Quantum & AI Projects

🚀 TPU-Accelerated Quantum Simulation in JAX

Tpu-Accelerated-Quantum-JAX

High-performance, differentiable quantum state-vector & tensor network simulator in 100% pure JAX, with no classical framework overhead.

Accelerated on NVIDIA GPUs and Google Cloud TPU v6e-64/v5e VM clusters, scaling up to 37 qubits. Supported by Google's TPU Research Cloud (TRC) program.