Research Intern: Neural Network Decoders for Quantum Error Correction

About QpiAI

QpiAI is building full-stack Enterprise Quantum Computers, combining AI and Quantum Computing to solve high-impact problems across Life Sciences, Healthcare, Transportation, Finance, Industrial, and Space Technologies. QpiAI Quantum Hardware team designs and characterizes quantum processors, cryogenic control circuits, and RF control hardware, and develops Fault-Tolerant Quantum Computation (FTQC) through Quantum Error Correction.

About the Opportunity

We are seeking a highly motivated Research Intern to pioneer the design, training, and benchmarking of neural network-based decoders for quantum error correction (QEC). This prestigious role offers a unique opportunity to drive cutting-edge research. Working alongside expert QEC researchers, you will leverage state-of-the-art baselines, including MWPM, Union-Find, BP-OSD, Relay-BP, and Tesseract, to explore how deep learning can substantially enhance decoding efficiency for surface codes, color codes and qLDPC codes.

Key Responsibilities

• Architect and implement sophisticated neural network decoders (GNNs, Transformers, CNNs) tailored for stabilizer codes.
• Develop robust pipelines for generating syndrome datasets under complex noise models, including circuit-level and biased noise.
• Conduct rigorous benchmarking of decoder performance, analyzing logical error rates, threshold behaviors, and real-time latency.

Qualifications

Minimum Requirements

• Strong theoretical foundation in neural networks with practical experience building, training, and debugging deep learning models.
• Proficiency in quantum computing fundamentals, including qubits, gates, noise, and decoherence.
• Expertise in Python and modern deep learning frameworks, with a strong preference for PyTorch.
• Currently enrolled in or possessing a degree (B.Tech/BS/MS/M.Sc) in Physics, Computer Science, Engineering, or a related quantitative field.

Preferred Skills

• Previous research or project experience specifically focused on quantum error correction.
• Knowledge of the stabilizer formalism, logical qubit.
• Familiarity with advanced architectures such as Graph Neural Networks (GNNs) or Sequence Models.
• Demonstrated ability to contribute to academic publications and technical documentation.

Benefits of Joining Us

• Direct mentorship from industry-leading experts on high-impact research problems.
• Deep immersion into QEC decoding pipelines and cutting-edge industrial research methodologies.