Efficient Cat Qubit Simulation Intern M/F
Alice & Bob is hiring an Efficient Cat Qubit Simulation Intern M/F in Paris, France. Level rates it ; you can apply on Alice & Bob.
Level's read on this job
- On Level's scale, this role is Little AI ●○○○: aI is not part of the work.
- Level classifies it as an internship.
- Level lists 45 other open roles at Alice & Bob.
- The role was posted in the last few days.
AI in this role
PURPOSE OF THE ROLE In a context where bit flips in cat qubits are exponentially rare, and where the Hilbert space grows quickly as soon as several modes are involved, estimating bit-flip probabilities in multi-mode operations lies beyond the reach of both brute-force simulation and exact master-equation methods. The Research Intern — Efficient Cat Qubit Simulation addresses this gap by making such estimates computationally tractable, so that error rates of multi-qubit operations can be quantified and better understood. Reporting to the Device Theorist, you will work closely with the Simulation Model team to explore how tensor-network representations, quantum trajectories and efficient simulation techniques can be combined to estimate bit-flip probabilities in many-mode settings, building on efficient simulation methods already developed for a single cat qubit.
Responsibilities:
- 1. Develop simulation methods for many-mode cat-qubit systems
- Combine tensor-network representations with quantum trajectory unravelings of the Lindblad master equation to simulate multi-mode operations
- Verify the numerical robustness of the simulations (Hilbert-space and bond-dimension truncation, solver stability and precision)
- Extend efficient simulation methods developed for a single cat qubit to multi-mode systems
- Extract statistically reliable estimates of bit-flip probabilities and rates from the simulated trajectories 3. Validate and benchmark the approach
- Compare estimates against exact references where they exist (master-equation results, single-qubit simulation results) and against theoretical predictions
- Quantify the computational cost and scaling of the method relative to standard Monte Carlo sampling
- Analyse simulated bit-flip trajectories to characterise the underlying error mechanisms
- Identify and explain discrepancies between simulated behaviour and theoretical expectations 5. Document and communicate results
- Document methods, code and results for reuse by the Simulation Model team
- Present progress in regular team meetings and formulate recommendations for extending these simulation methods to further multi-qubit operations
2. Develop efficient simulation strategies for many-mode bit flips
Requirements:
- You are a Master's or final-year engineering student with a strong background in quantum physics and numerical methods. You are motivated by research at the interface of theory and computation, and are able to drive a technical project with a high degree of independence while seeking feedback from more experienced colleagues.
- LANGUAGES: Proficient in English
- Good understanding of quantum mechanics and open quantum systems (density matrices, Lindblad master equations)
- Programming experience in Python or Julia for scientific computing
- Autonomy and comfort with open-ended research questions that have no guaranteed answer
- Clear technical communication and a collaborative attitude within a multidisciplinary team
- EXPERIENCE: No prior professional experience required
Nice to have:
- Familiarity with quantum trajectories and stochastic unraveling
- Exposure to tensor-network methods (e.g. matrix product states)
- Knowledge of Monte Carlo methods and statistical estimation
- Experience with Julia, numerical ODE/SDE solvers, or GPU-accelerated and high-performance computing
- Habit of rigorous numerical practice (convergence checks, truncation control, precision and solver choice)
- Experience reading and applying current research literature
- A previous research, project or internship in quantum physics or scientific computing is an asset
How we rate this
Efficient Cat Qubit Simulation Intern M/F at Alice & Bob rates 14 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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