Quandela stands as a global leader in quantum computing, driven by groundbreaking technology and a strategic vision for scaling quantum solutions. The company’s unique ability to offer both hardware and software solutions, along with its commitment to build energy efficient datacenters and scalability, positions it to play a key role in the next wave of innovation, and in many strategic and sovereign industrial sectors.
Join us at the forefront of quantum computing innovation 🚀
Description of the Project / Team
We are seeking a highly motivated and innovative intern to join our cutting-edge research team focused on advancing the field of optimal control in photonic quantum computing. The selected candidate will work closely with our lead researcher to enhance the efficiency and precision of machine learning techniques applied to calibrate and control photonic integrated circuits. Our current methods have set a new benchmark in optimal control (see here), achieving unprecedented fidelity values. With the new generation of devices, featuring up to 600 controllable components, we are facing exciting new challenges in our quest for ultimate precision. This internship offers a unique opportunity to contribute to ground-breaking research, while working on some of the most recent integrated photonics hardware in the world.
Your key responsibilities
- Collaborate with the research team to understand and analyze existing machine learning models used for calibrating photonic chips.
- Develop and implement novel techniques to significantly reduce the number of required data samples, making the machine learning process more efficient.
- Optimize the computational aspects of machine learning training to accelerate the learning of the behavior of photonic devices.
- Design and develop strategies to mitigate imperfections learned by machine learning models, aiming for more precise photonic quantum computers.
- Collaborate on potential experimental work related to the research, depending on the intern's interests and skill set.
- Regularly communicate progress and findings. Participate in team discussions.
- You are enrolled in a university program that includes an internship period. This should be a Master's Degree (first or second year) or equivalent (not a PhD) program in physics, engineering or related fields.
- You are available full-time for 5 to 6 months.
- You are proficient in Python programming and physics.
- You have good communication skills.
- You are curious, energetic and love solving problems.
- Knowledge of optics, quantum mechanics and machine learning is a plus.
- Swile Card (meal vouchers) 🍴🛒
- 50% participation in transportation costs 🚆
- Possibility of remote work 💻
- Internship Allowance between €1,200 and €1,400 per month 💰
- 1,5 days off per month, cumulative 🧳
What we also offer
A challenging and innovative work environment at the heart of quantum computing.
A diverse and collaborative company culture.
Opportunities for professional growth and skill development.
At Quandela, we believe that the strength of our team is the plurality of experiences, perspectives, and journeys. We are committed to building a respectful, inclusive, and welcoming work environment. All applications are welcome.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The convergence of machine learning and photonic integrated circuits represents a structural shift in the quantum hardware value chain, moving from manual heuristic tuning toward automated algorithmic control. This role type is essential for managing the high-dimensional parameter spaces inherent in scaling photonic quantum computers, where classical optimization often fails to maintain high-fidelity state preparation and gate operations. By applying machine learning to the calibration of integrated optics, organizations can mitigate the impacts of fabrication variances and thermal instability, which are primary bottlenecks in transition from laboratory prototypes to utility-scale systems. Market indicators suggest that the integration of artificial intelligence into the quantum control stack is a prerequisite for achieving the operational stability required for fault-tolerant computing. Consequently, this function serves as a critical bridge between theoretical photonic research and the engineering of robust, scalable quantum processors.
The photonic quantum computing sector is currently navigating a pivotal transition phase characterized by the maturation of integrated photonic platforms and the increasing complexity of multi-qubit systems. As hardware developers move toward large-scale monolithic and modular architectures, the traditional methods of manual device calibration and control are proving insufficient. This evolution has created a demand for a specialized research tier capable of embedding advanced machine learning protocols directly into the hardware lifecycle. Within this ecosystem, the role of machine learning research for photonic circuits is positioned at the intersection of optical engineering and computational physics, addressing the systemic challenge of drift and noise in integrated components.
Macro-level analysis of the global quantum workforce highlights a significant talent gap in the application of neural networks and reinforcement learning to physical quantum systems. National quantum initiatives and private sector investments are increasingly prioritizing the development of self-calibrating hardware to reduce the heavy reliance on specialized doctoral-level operators. By automating the identification of optimal control pulses and phase shifts, this role type facilitates the industrialization of photonic chips, ensuring that quantum processors can maintain peak performance in dynamic environmental conditions. This structural enablement is vital for the long-term viability of photonics as a dominant modality for universal quantum computation.
Furthermore, the industry is seeing a shift toward co-design strategies where hardware constraints inform the development of machine learning models. These models must operate within the strict latency and throughput requirements of real-time quantum control stacks. The successful deployment of such techniques not only improves individual gate fidelities but also accelerates the benchmarking of new photonic integrated circuit designs. As the ecosystem matures, the ability to rapidly iterate on hardware configurations through algorithmic feedback will be a primary determinant of competitive advantage in the emerging quantum economy.
The capability architecture for this role type centers on the integration of deep learning and optimization frameworks with the physical principles of quantum optics. At the foundational layer, a deep understanding of light-matter interaction and the operational characteristics of components such as waveguides, beam splitters, and phase shifters is required. This physical intuition is coupled with expertise in designing machine learning architectures—such as generative models or Bayesian optimization—that can effectively navigate the non-linear landscapes of photonic circuit responses. These capabilities are critical for ensuring the reproducibility of quantum states and the precision of measurement-induced nonlinearities, which are essential for gate-based photonic computing.
Beyond individual device control, the technical focus extends to the system-level orchestration of feedback loops between the classical control layer and the quantum processing unit. This requires proficiency in data-driven modeling of experimental imperfections and the development of noise-mitigation strategies that can be implemented at the firmware level. By standardizing these calibration subroutines, researchers enable a higher level of interoperability across different photonic hardware generations. This technical coupling ensures that the software stack can rely on a stable hardware abstraction layer, thereby accelerating the deployment of hybrid quantum-classical algorithms in production environments.
Accelerates the automation of high-fidelity calibration for large-scale photonic integrated circuits
Mitigates systemic performance degradation caused by fabrication variances in integrated optical components
Facilitates the transition from heuristic-based control to data-driven optimal control in quantum systems
Reduces the operational overhead required for maintaining stable quantum gate operations over time
Strengthens the reliability of state preparation and readout in photonic quantum processing units
Optimizes the throughput of hardware benchmarking cycles through automated parameter identification
Supports the scaling of photonic quantum computers by addressing high-dimensional control challenges
Enhances the precision of real-time noise mitigation within the quantum control stack
Shortens the development timeline for fault-tolerant quantum architectures through algorithmic co-design
Improves the energy efficiency of quantum datacenters by optimizing photonic device tuning protocols
Protects capital investments in photonic hardware by ensuring long-term operational stability and accuracy
Enables the integration of advanced artificial intelligence techniques into the foundational quantum hardware layer
Industry Tags: Photonic Quantum Computing, Integrated Photonics, Machine Learning, Optimal Control, Quantum Calibration, Silicon Photonics, Algorithmic Engineering, Quantum Hardware Scaling, NISQ Systems, Computational Physics
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