Cs Quantum Gas Microscope
A quantum gas microscope for strongly interacting topological phases.
We have built a new Quantum Gas Microscope experiment with bosonic Caesium atoms at LMU to study topological many-body phases of matter. We will make use of the unique possibilities offered by high-resolution imaging techniques to investigate topological many-body phenomena and out-of-equilibrium dynamics in these lattices.
We are currently looking for PhD students!!
Recent publications:
Measurements of local kinetic operators
In our recent preprint arXiv:2312:13268 we demonstrated a novel technique for state-preparation and read-out based on optical superlattice potentials that enable local manipulations on the level of isolated bonds in the lattice.
Quantum gas microscopes have revolutionized quantum simulations with ultracold atoms, allowing to measure local observables and snapshots of quantum states. However, measurements so far were mostly carried out in the occupation basis. In this work, we demonstrate how all kinetic operators, such as kinetic energy or current operators, can be measured and manipulated with single bond resolution. Beyond simple expectation values of these observables, the single-shot measurements allow to access full counting statistics and complex correlation functions. Our work paves the way for the implementation of efficient quantum state tomography and hybrid quantum computing protocols for itinerant particles on a lattice. In addition, we demonstrate how site-resolved programmable potentials enable a spatially-selective, parallel readout in different bases as well as the engineering of arbitrary initial states.
Original publication:
Local readout and control of current and kinetic energy operators in optical lattices
Alexander Impertro, Simon Karch, Julian F. Wienand, SeungJung Huh, Christian Schweizer, Immanuel Bloch, Monika Aidelsburger
Preprint arXiv:2312.13268
Emergence of fluctuating hydrodynamics in chaotic quantum systems
A fundamental principle of chaotic quantum dynamics is that local subsystems eventually approach a thermal equilibrium state. Large subsystems thermalize slower: their approach to equilibrium is limited by the hydrodynamic build-up of large-scale fluctuations. For classical out-of-equilibrium systems, the framework of macroscopic fluctuation theory (MFT) was recently developed to model the hydrodynamics of fluctuations. We perform large-scale quantum simulations that monitor the full counting statistics of particle-number fluctuations in hard-core boson ladders, contrasting systems with ballistic and chaotic dynamics. We find excellent agreement between our results and MFT predictions, which allows us to accurately extract diffusion constants from fluctuation growth. Our results suggest that large-scale fluctuations of isolated quantum systems display emergent hydrodynamic behavior, expanding the applicability of MFT to the quantum regime.
Original publication:
Emergence of fluctuating hydrodynamics in chaotic quantum systems
Julian F. Wienand, Simon Karch, Alexander Impertro, Christian Schweizer, Ewan McCulloch, Romain Vasseur, Sarang Gopalakrishnan, Monika Aidelsburger, Immanuel Bloch
Preprint arXiv:2306.11457
Unsupervised machine learning
for single-site reconstruction in quantum gas microscopes
We propose a novel technique based on a convolutional autoencoder which enables us to reconstruct the lattice occupation with high fidelity even though our lattice spacing is more than two times smaller than our imaging resolution (383.5nm lattice spacing vs. 850nm Rayleigh resolution).
In quantum gas microscopy experiments, reconstructing the site-resolved lattice occupation with high fidelity is essential for the accurate extraction of physical observables. For short interatomic separations and limited signal-to-noise ratio, this task becomes increasingly challenging. Common methods rapidly decline in performance as the lattice spacing is decreased below half the imaging resolution. Here, we present a novel algorithm based on deep convolutional neural networks to reconstruct the site-resolved lattice occupation with high fidelity. The algorithm can be directly trained in an unsupervised fashion with experimental fluorescence images and allows for a fast reconstruction of large images containing several thousand lattice sites. We benchmark its performance using a quantum gas microscope with cesium atoms that utilizes short-spaced optical lattices with lattice constant 383.5nm and a typical Rayleigh resolution of 850nm. We obtain promising reconstruction fidelities ≳96% across all fillings based on a statistical analysis. We anticipate this algorithm to enable novel experiments with shorter lattice spacing, boost the readout fidelity and speed of lower-resolution imaging systems, and furthermore find application in related experiments such as trapped ions.
Original publication:
An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes
Alexander Impertro, Julian F. Wienand, Sophie Häfele, Hendrik von Raven, Scott Hubele, Till Klostermann, Cesar R. Cabrera, Immanuel Bloch, Monika Aidelsburger
Commun. Phys. 6, 166 (2023)
Team members:
- SeungJung Huh, PostDoc
- Alexander Impertro, PhD
- Simon Karch, PhD
- Julian Wienand, PhD
Former members
- Dr. Cesar Cabrera, PostDoc
- Dr. Christian Schweizer, PostDoc
- Hendrik von Raven, PhD candidate
- Till Klostermann, PhD candidate (now @MenloSystems)
- Andreas Reetz, Master student
- Jingjing Chen, Master student
- Julian Wienand, Master student
- Scott Hubele, Master student
- Sophie Häfele, Master student
- Ignacio Pérez Ramos, Master student
- Bodo Kaiser, Bachelor student
- Nicola Reiter, Bachelor student
- Emma Cussenot, Internship student
- Friederike Horn, Internship student
- Andrés Durán Hernández, Internship student