About the team
The Quantum material department at Pasqal develop hybrid quantum classical algorithms with applications in material science and quantum many-body physics and that can be run on Pasqal neutral atom quantum processing units.
We are offering an internship position to work on a project involving the application of machine learning (ML) techniques to datasets generated by Rydberg quantum simulators. The goal is to develop hybrid quantum-classical approaches that combine classical ML methods with data from quantum simulators to help overcome current challenges in quantum simulations. Examples of concrete applications include finding ground states of many-body quantum Hamiltonians describing realistic magnetic materials or simulating their quantum dynamics.
Mission
Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on QPU-generated datasets.
Benchmark this approach against established numerical methods (e.g., exact diagonalization, standard VMC, tensor networks) and against raw QPU data.
Apply NQS to represent observables and many-body wave functions of magnetic Hamiltonians.
Contribute to internal tools and publications.
What we offer
Hands-on experience with Pasqal’s analog QPU and emulator stack used to model such devices.
The opportunity to learn important aspects of Pasqal’s quantum hardware.
Mentorship from a multidisciplinary team (quantum many-body physics, machine learning, materials science).
Required Qualifications
Hard Skills
Master or PhD student in quantum many-body physics.
Proficiency in one or more programming languages such as Python or Julia.
Demonstrated experience with machine learning methods applied to quantum many-body systems (e.g., neural quantum states, supervised and unsupervised ML, kernel methods)
Nice to Have
Experience with numerical methods for quantum spin systems (e.g., exact diagonalization and variational Monte Carlo)
Familiarity with scientific computing frameworks (e.g., JAX, PyTorch, TensorFlow)
Experience working with high-performance computing (HPC) environments.
Soft Skills
Logistics
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The convergence of machine learning and quantum simulation represents a vital structural pivot in the transition toward practical quantum utility for complex material science. This role type exists to bridge the gap between raw data generated by neutral atom processors and the high-fidelity predictive models required by industrial research pipelines. By applying classical machine learning to quantum many-body systems, this function addresses the critical bottleneck of characterizing large-scale quantum states that remain beyond the reach of exact classical computation. Market signals from the pharmaceutical and materials sectors suggest that this hybrid technical interface is essential for validating the accuracy of near-term quantum simulators. Consequently, these researchers serve as the primary conduits for translating abstract hardware capabilities into the reproducible, benchmarked outputs necessary for commercial-grade discovery workflows.
The quantum computing ecosystem is currently experiencing a maturation phase where the focus has shifted from hardware-centric demonstration to the development of reliable software stacks and algorithmic benchmarking. Within the application enablement layer of the value chain, neutral atom architectures—specifically Rydberg-based simulators—have emerged as high-potential candidates for solving many-body physics problems. However, a significant Technology Readiness Level mismatch persists between current hardware noise levels and the precision required for realistic material discovery. The integration of hybrid classical-quantum workflows, particularly those utilizing machine learning for state reconstruction and observable estimation, is the primary strategy for mitigating these hardware limitations.
Sector-level analysis indicates that the demand for talent capable of navigating both quantum many-body physics and deep learning architectures is outpacing current academic output. This workforce scarcity creates a dependency on specialized internship and doctoral-level research to maintain the structural throughput of development pipelines. Furthermore, the industry is moving away from monolithic software solutions toward modular, interoperable toolchains that can interface with diverse hardware backends. This transition favors the development of neural quantum state frameworks that can be pretrained on quantum processing unit datasets, thereby reducing the computational overhead of traditional variational methods.
Public investment cycles and national quantum strategies in Europe and North America have increasingly prioritized the deployment of quantum-classical hybrid systems. These initiatives aim to establish high-performance computing centers where quantum simulators act as specialized accelerators for material screening and drug candidate optimization. The success of these centers depends on the ecosystem's ability to standardize benchmarking protocols, ensuring that quantum-enhanced results are systematically compared against state-of-the-art classical numerical methods like tensor networks or Monte Carlo simulations.
The technical architecture of this domain is anchored in the synergy between variational quantum algorithms and neural network representations of many-body wave functions. Expertise in this area facilitates the development of scalable data pipelines that process high-dimensional datasets from quantum hardware, enabling the identification of ground states in complex magnetic Hamiltonians. Mastery of frameworks such as JAX or PyTorch, coupled with a deep understanding of quantum spin systems, is essential for ensuring the stability and convergence of hybrid models. These capabilities matter for the broader ecosystem because they provide a pathway for error mitigation and verification in the Noisy Intermediate-Scale Quantum era. By representing observables through neural quantum states, researchers reduce the sampling requirements on physical hardware, effectively increasing the throughput of the entire quantum stack. This technical coupling between algorithmic research and hardware performance is critical for building the interoperable software layers required for fault-tolerant computing.
Accelerates the deterministic progression of material discovery through hybrid quantum-classical benchmarking
Mitigates systemic risks in hardware scaling by providing high-fidelity state reconstruction via machine learning
Facilitates the transition from laboratory prototypes to standardized industrial simulation toolchains
Reduces iteration friction in the development of near-term algorithms for Rydberg-based architectures
Strengthens the long-term competitive positioning of the materials sector through quantum-ready workforce development
Harmonizes abstract quantum physics research with the scalable requirements of modern deep learning frameworks
Optimizes the lifecycle of quantum datasets by utilizing pretraining techniques on neural quantum states
Supports the scaling of quantum adoption by identifying high-impact use cases in many-body physics
Shortens the time-to-market for quantum-enhanced discovery services through rigorous numerical validation
Improves the reliability of multi-disciplinary research teams by bridging the gap between physics and data science
Protects capital-intensive investments in quantum hardware by demonstrating practical utility in material science
Enables the strategic orchestration of hybrid workflows across high-performance computing environments
Industry Tags: Quantum Simulation, Rydberg Atoms, Neural Quantum States, Many-Body Physics, Hybrid Quantum-Classical Computing, Machine Learning, Material Science, Variational Monte Carlo
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