Quantum Computing Research Intern

Overview

Read the full description before applying.

MUST HAVE: Hands-on experience implementing quantum computing algorithms, quantum machine learning methods, or hybrid quantum-classical workflows using frameworks such as Qiskit, PennyLane, Cirq, or equivalent.

At M31 Biomedical AI, we are developing foundation models and advanced computational approaches for understanding human health and biology across medical imaging, clinical data, and other biomedical modalities. We are expanding our research into quantum computing and quantum machine learning, with a particular interest in understanding where quantum and hybrid quantum-classical methods could meaningfully address computational problems in biomedical AI, healthcare, and scientific discovery.

We’re seeking an exceptional Quantum Computing Research Intern to work alongside our AI researchers and research collaborators to explore, implement, benchmark, and evaluate quantum approaches for clinically and scientifically meaningful problems.

This is not intended to be a purely theoretical role. We are looking for someone who can translate quantum computing concepts into working experiments, critically evaluate whether quantum approaches provide meaningful value, and build reproducible research pipelines.

What You’ll Do

•    Implement and benchmark. Build quantum and hybrid quantum–classical methods — variational quantum algorithms, quantum kernels, quantum neural networks, and emerging quantum ML approaches — and evaluate them against strong classical and deep learning baselines.

•    Run experiments. Design and run experiments on quantum simulators and, where appropriate, real quantum hardware.

•    Find the real applications. Explore where quantum computing could apply to biomedical AI — medical imaging, clinical and biological data, optimization, representation learning, and predictive modelling — working with AI scientists, ML engineers, clinicians, and external collaborators to identify problems where quantum methods may add genuine value.

•    Characterise the limits. Investigate noise, scalability, circuit depth, data encoding, computational cost, and hardware constraints.

•    Keep the work reproducible. Document and maintain reproducible workflows using Python, Git, and cloud-based research tools; review, debug, and improve research code.

•    Read, write, and explain. Conduct technical literature reviews, contribute to publications, technical reports and internal proposals, and communicate findings clearly to both technical and interdisciplinary audiences.

•    Work with AI scientists, machine learning engineers, clinicians, and external research collaborators to identify problems where quantum approaches may provide meaningful scientific or computational value

•    Contribute to research publications, technical reports, internal research proposals, and presentations

•    Clearly communicate complex quantum computing concepts and experimental findings to both technical and interdisciplinary audiences

Required Skills & Background

• Currently pursuing or recently completed an undergraduate, master’s, or PhD degree in Computer Science, Physics, Mathematics, Engineering, Quantum Information, Computational Science, or a related field
• Strong programming skills in Python
• Hands-on experience with at least one quantum computing framework such as Qiskit, PennyLane, Cirq, CUDA-Q, or equivalent
• Strong understanding of fundamental quantum computing concepts, including:

qubits and quantum states; quantum gates and circuits; measurement; entanglement

• variational quantum algorithms
• noise and limitations of current quantum hardware
• Hamiltonians and expectation values
• Understanding of machine learning fundamentals and experimental evaluation
• Ability to independently read and implement methods from technical research papers
• Strong mathematical foundation, particularly linear algebra, probability, optimization, and statistics
• Strong problem-solving, communication, and teamwork skills

Nice-to-Have

• Experience running experiments on real quantum hardware, particularly IBM Quantum environments
• Experience with quantum