Qubit Pharmaceuticals is a French-American deeptech startup, focusing on discovering novel molecules for complex targets in healthcare and materials science. We leverage proprietary molecular simulation and quantum-physics based modeling technology to develop our own discovery programs that we either co-develop with or license out to pharmaceutical and industrial partners. This enables us to design more effective and safer drug candidates, aiming to significantly reduce the time and investment needed for discovery. In just 18 months, Qubit Pharmaceuticals grew its portfolio to 7 programs in oncology, immunology & antivirals.
Our cutting-edge technology is based on over 30 years of research by our academic founders, and relies on three main components: in-depth expertise in computational science and high-performance computing (HPC), quantum chemistry and artificial intelligence algorithms, and a powerful, automated cloud platform for molecular simulation with chemical accuracy. We recently announced the launch of the world's most powerful AI foundation model for molecular simulation.
Qubit Pharmaceuticals is looking for an Intern (M/F) to implement a quantum algorithm for a NISQ device implementation, to perform combinatorial optimization tasks using QUBO and PUBO formulations applied to drug discovery problems.
The perfect fit for this job
We are looking for a detail-oriented engineer who is willing to work on developing a quantum algorithm for NISQ architectures to solve combinatorial optimization tasks with application in drug discovery, leveraging the flexibility of PUBO while managing the resource constraints imposed by current hardware.
Combinatorial optimization problems are challenging for classical computers, requiring to find the optimal value of a suitable cost function from a finite but large set of objects. These problems are NP-hard, making the use of quantum computers appealing.
Your role
- Problem Formulation and Encoding: implement and compare both the QUBO and the PUBO formulations for representative instances of a given problem.. The goal is to compare the resulting cost Hamiltonians and identify how resource requirements scale for each approach.
- Quantum Algorithm Development (QAOA): Implement a QAOA algorithm tailored to approximate the solutions for these combinatorial optimization problems.
- Comparative Resource Analysis for NISQ Devices: Perform a detailed resource estimation, focusing on the key trade-offs between the two formulations when implemented on current NISQ hardware:
- Performance Benchmarking and Utility Assessment: Collect evidence on wich approach delivers better results and eventually justify its computational cost on existing NISQ hardware. This evaluation will contribute to assessing quantum utility—the effectiveness and practicality of quantum computers for a specific application.
- Future Hardware Projection: Explore how the performances might shift if future quantum processors incorporate more advanced capabilities, as native multi-qubit gates, and their effect on the computational run time and circuit implementation.
Your qualifications and skills
- Master 2 or final year of engineering school
- Quantum computing: quantum technologies (basics) and algorithm are mandatory
- Quantum technology
- Numerical methods and optimization algorithms
- Evidence of good understanding of graph theory problems
The proposal benefits & perks
- Duration: 6 months
- Starting date: from February 2026
- Lunch vouchers worth €9 covered at 50%
- Location: Paris 14th arrondissement
