WHAT YOU WILL CONTRIBUTE:

• Lead the design and benchmarking of FTQC algorithms, focusing on their feasibility.
• Identify and model the mathematical primitives in LLMs and neural networks most likely to benefit from quantum acceleration.
• Use Entropica’s Loom compiler and software stack to perform rigorous QEC resource estimations and speedup analyses.
• Compare QEC codes for different quantum algorithms to outline possible architectures for specialised QPUs based on the implementation requirements.
• Explore and evaluate quantum-native AI model architectures with academic and industry partners.
• Translate research findings into actionable recommendations for compiler optimisation and FTQC orchestration.
• Publish and present research internally and externally to position Entropica as a leader in fault-tolerant quantum error correction.Maintain close collaboration with the compiler and orchestration teams to ensure alignment between theory and system requirements.

JOB REQUIREMENTS:

• Strong background in quantum mechanics, quantum algorithms and computational physics.
• Experience with resource estimation for quantum algorithms and familiarity with QEC concepts.
• Proficiency in Python and experience with scientific computing libraries.
• Familiarity with quantum programming frameworks (Qiskit, Cirq, PennyLane, or similar).
• Strong analytical skills and ability to translate theoretical models into implementable algorithms.
• Excellent communication skills for technical and non-technical audiences.
• PhD in Physics, Computer Science, or a related quantitative field.

This role focuses on leading research and development at the intersection of fault-tolerant quantum computing (FTQC) and large-scale algorithms for AI and simulation. You will use Loom, Entropica’s quantum error correction compiler, to analyse the resource requirements and performance of fault-tolerant quantum algorithms, particularly those relevant to foundational mathematical functions in large language models (LLMs) and neural networks.
As Quantum Algorithms Lead, you will work hands-on with algorithm implementation, resource estimation, and performance benchmarking, while also exploring collaborations with academic and industry partners to test novel quantum neural network designs and AI models. Success requires a strong background in quantum mechanics, algorithms, and computational physics, plus the creativity to bridge theoretical insight with practical, architecture-aware FTQC implementation.