Speaker
Description
Quantum simulators based on semiconductor quantum dots (QDs) [1] are promising candidates for practical quantum technologies in the present NISQ (Noisy Intermediate-Scale Quantum) era. However, a fundamental challenge is the characterization and control of already fabricated devices. This task becomes increasingly complex in arrays of many interacting QDs, where the number of tunable parameters grows rapidly. The primary experimental tool for device characterization are transport measurements, where systems are tuned by searching for characteristic structures in conductance maps as functions of control parameters such as gate voltages applied to electrostatically defined QDs [2]. Interpreting such high-dimensional data and inferring the underlying Hamiltonian parameters is a difficult inverse problem.
Machine learning (ML) methods have proven useful for quantum technologies in recent years [3,4]. In this talk we present recent results on ML-assisted analysis and control of QD-based quantum simulators. First, we introduce a physics-informed neural architecture capable of learning effective Hamiltonians directly from conductance maps [5]. Second, we demonstrate an AI-enhanced autotuning protocol that can steer a QD chain toward regimes hosting Majorana zero modes by iteratively updating experimentally accessible parameters [6]. Together, these approaches provide a route toward automated characterization and control of complex quantum devices, enabling scalable operation of quantum-dot simulators.
References:
[1] F. Borsoi et al., Nat. Nanotechnol. 19, 21 (2024).
[2] J Pawłowski et al., Nanotechnology 36, 195001 (2025).
[3] J Pawłowski, M Krawczyk, Phys. Rev. Applied 22, 014068 (2024).
[4] M Krawczyk, J Pawłowski, MM Maśka, K Roszak, Phys. Rev. A 109, 022405 (2024).
[5] J. Pawłowski, M. Krawczyk, arXiv: 2603.02889 (2026).
[6] M. Krawczyk, J. Pawłowski, arXiv: 2601.02149 (2026).