NVIDIA

Technology

QuantumErrorCorrectionResearchScientistIntern-Fall2026

$0–0k California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Entry candidates.

The Brief

“Quantum Error Correction Research Scientist Intern - Fall 2026 at NVIDIA. Skills: Quantum Error Correction, Machine Learning, Python, Automation. Develop automated quantum error correcting code discovery protocols. Develop automated quantum error correcting pipelines”

What You'll Achieve.

Achieve high-performance across encoding rate; Achieve high-performance across error suppression; Achieve high-performance across system realizability; Improve decoding accuracy; Improve decoding scalability; Improve decoding adaptability

Industry & Context.

Technology
Problems you'll solve

Problem-solving

What They're Looking For.

Must Have

Master's, or PhD in Physics, Electrical/Computer Engineering, Computer Science, or a related field, Background in quantum computing fundamentals, fundamentals of quantum error correction, stabilizer codes, coding theory, decoders, Solid proficiency in Python or similar scientific programming language experience, Good teamwork, communication skills, documentation skills

Nice to Have

Hands-on experience with tensor network, state vector, quantum error correction methods for quantum circuit simulation, Experience with neural network-based quantum error correction decoders, topological or QLDPC codes, Familiarity with qubit loss, leakage, non-Pauli noise mechanisms, methods for incorporating this information into QEC code design and decoder design, Experience with magic state preparation, distillation, factory design, decoding and performance analysis of magic state factories, GPU-accelerated simulation experience with cuQuantum, CUDA-Q, related NVIDIA quantum software

What You'll Do.

Develop automated quantum error correcting code discovery protocols

Develop automated quantum error correcting pipelines

Use AI and automation to identify novel quantum error correcting codes

Integrate classical machine learning methods

Build machine learning models

Develop machine learning-based decoders

Build methods to identify and evaluate error correcting codes

Analyze and evaluate performance of fault-tolerant circuits

How You'll Work.

Team & Collaboration

Work with product team

Communication Scope

Communication skills

Full Job Description

Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world. We are looking for a Quantum Error Correction Research Scientist Intern to develop automated, high-performance quantum error correction research pipelines spanning both code discovery and decoder design. NVIDIA is deeply invested in the future of quantum computing, building the accelerated platforms (cuQuantum, CUDA-Q, and Ising) that researchers around the world rely on to design, simulate, and scale quantum systems. On this team, you will help push frontier research at the intersection of quantum code design, neural decoders, and AI-driven automation, prototyping large-scale methods to computationally discover, evaluate, and improve quantum error correction schemes for realistic quantum systems. **What you 'll be doing:** * Developing automated quantum error correcting code discovery protocols and pipelines. * Using AI and automation to identify novel quantum error correcting codes that achieve high-performance across encoding rate, error suppression, and system realizability. * Integrating various classical machine learning methods for identifying high-performance code constructions, building bespoke machine learning models tailored to high-performance code design, and developing machine learning-based decoders for topological and/or QLDPC codes, with an emphasis on improving decoding accuracy, scalability, and adaptability to quantum logic settings. * Building out methods to identify and evaluate high-performance error correcting codes and decoders that directly incorpo

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