arXiv subjects

Thomas Fösel

Publications and source records attributed to Thomas Fösel.

9 recordsLinked to original sources

arXiv2026

Contacting Josephson Junctions via Airbridges in Superconducting Circuits

Superconducting circuit devices require electrical interconnects between different circuit elements on the chip, for which conventional device architectures use a combination of two structural elements: \textit{airbridges} to connect non-adjacent elements in the base layer, and \textit{bandages} to connect the electrodes forming the Josephson junctions to the base layer. Bandages introduce unwanted parasitic material interfaces and increase the manufacturing complexity. Here, we overcome the limitations imposed by \emph{bandages} by establishing \textit{all} electrical interconnects with airbridges of varying size fabricated in a single step. The airbridges show a high yield and mechanical stability over a wide range of sizes from $0.5\,μ\mathrm{m}$ to $4\,μ\mathrm{m}$ in width and from $5\,μ\mathrm{m}$ to $40\,μ\mathrm{m}$ in length, and show low loss when integrated in coplanar waveguide resonators and transmon qubits. Measured relaxation times up to more than $250\,μ\mathrm{s}$ in standard transmon geometries show that the process achieves high coherence while substantially easing and accelerating device fabrication.

quant-ph↗

arXiv2025

Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning

Markov decision processes (MDPs) are used to model a wide variety of applications ranging from game playing over robotics to finance. Their optimal policy typically maximizes the expected sum of rewards given at each step of the decision process. However, a large class of problems does not fit straightforwardly into this framework: Non-cumulative Markov decision processes (NCMDPs), where instead of the expected sum of rewards, the expected value of an arbitrary function of the rewards is maximized. Example functions include the maximum of the rewards or their mean divided by their standard deviation. In this work, we introduce a general mapping of NCMDPs to standard MDPs. This allows all techniques developed to find optimal policies for MDPs, such as reinforcement learning or dynamic programming, to be directly applied to the larger class of NCMDPs. Focusing on reinforcement learning, we show applications in a diverse set of tasks, including classical control, portfolio optimization in finance, and discrete optimization problems. Given our approach, we can improve both final performance and training time compared to relying on standard MDPs.

cs.LG↗

arXiv2024

Fast quantum control of cavities using an improved protocol without coherent errors

The selective number-dependent arbitrary phase (SNAP) gates form a powerful class of quantum gates, imparting arbitrarily chosen phases to the Fock states of a cavity. However, for short pulses, coherent errors limit the performance. Here we demonstrate in theory and experiment that such errors can be completely suppressed, provided that the pulse times exceed a specific limit. The resulting shorter gate times also reduce incoherent errors. Our approach needs only a small number of frequency components, the resulting pulses can be interpreted easily, and it is compatible with fault-tolerant schemes.

quant-ph↗

arXiv2022

Deep Learning of Quantum Many-Body Dynamics via Random Driving

Neural networks have emerged as a powerful way to approach many practical problems in quantum physics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantum many-body system, where the training is \textit{based purely on monitoring expectation values of observables under random driving}. The trained recurrent network is able to produce accurate predictions for driving trajectories entirely different than those observed during training. As a proof of principle, here we train the network on numerical data generated from spin models, showing that it can learn the dynamics of observables of interest without needing information about the full quantum state. This allows our approach to be applied eventually to actual experimental data generated from a quantum many-body system that might be open, noisy, or disordered, without any need for a detailed understanding of the system. This scheme provides considerable speedup for rapid explorations and pulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to times longer than those it has been trained on, as well as to the infinite-system-size limit.

quant-ph↗

arXiv2022

Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system

To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itself, which is generally not available. Model-free reinforcement learning circumvents this need by discovering control strategies from scratch without relying on an accurate description of the quantum system. Furthermore, important tasks like state preparation, gate teleportation and error correction need feedback at time scales much shorter than the coherence time, which for superconducting circuits is in the microsecond range. Developing and training a deep reinforcement learning agent able to operate in this real-time feedback regime has been an open challenge. Here, we have implemented such an agent in the form of a latency-optimized deep neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit into a target state. To train the agent, we use model-free reinforcement learning that is based solely on measurement data. We study the agent's performance for strong and weak measurements, and for three-level readout, and compare with simple strategies based on thresholding. This demonstration motivates further research towards adoption of reinforcement learning for real-time feedback control of quantum devices and more generally any physical system requiring learnable low-latency feedback control.

quant-ph↗

arXiv2021

Quantum circuit optimization with deep reinforcement learning

A central aspect for operating future quantum computers is quantum circuit optimization, i.e., the search for efficient realizations of quantum algorithms given the device capabilities. In recent years, powerful approaches have been developed which focus on optimizing the high-level circuit structure. However, these approaches do not consider and thus cannot optimize for the hardware details of the quantum architecture, which is especially important for near-term devices. To address this point, we present an approach to quantum circuit optimization based on reinforcement learning. We demonstrate how an agent, realized by a deep convolutional neural network, can autonomously learn generic strategies to optimize arbitrary circuits on a specific architecture, where the optimization target can be chosen freely by the user. We demonstrate the feasibility of this approach by training agents on 12-qubit random circuits, where we find on average a depth reduction by 27% and a gate count reduction by 15%. We examine the extrapolation to larger circuits than used for training, and envision how this approach can be utilized for near-term quantum devices.

quant-ph↗

arXiv2020

Efficient cavity control with SNAP gates

Microwave cavities coupled to superconducting qubits have been demonstrated to be a promising platform for quantum information processing. A major challenge in this setup is to realize universal control over the cavity. A promising approach are selective number-dependent arbitrary phase (SNAP) gates combined with cavity displacements. It has been proven that this is a universal gate set, but a central question remained open so far: how can a given target operation be realized efficiently with a sequence of these operations. In this work, we present a practical scheme to address this problem. It involves a hierarchical strategy to insert new gates into a sequence, followed by a co-optimization of the control parameters, which generates short high-fidelity sequences. For a broad range of experimentally relevant applications, we find that they can be implemented with 3 to 4 SNAP gates, compared to up to 50 with previously known techniques.

quant-ph↗

arXiv2018

Reinforcement Learning with Neural Networks for Quantum Feedback

Machine learning with artificial neural networks is revolutionizing science. The most advanced challenges require discovering answers autonomously. This is the domain of reinforcement learning, where control strategies are improved according to a reward function. The power of neural-network-based reinforcement learning has been highlighted by spectacular recent successes, such as playing Go, but its benefits for physics are yet to be demonstrated. Here, we show how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise. These strategies require feedback adapted to measurement outcomes. Finding them from scratch, without human guidance, tailored to different hardware resources, is a formidable challenge due to the combinatorially large search space. To solve this, we develop two ideas: two-stage learning with teacher/student networks and a reward quantifying the capability to recover the quantum information stored in a multi-qubit system. Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.

quant-ph↗

arXiv2017

L lines, C points and Chern numbers: understanding band structure topology using polarization fields

Topology has appeared in different physical contexts. The most prominent application is topologically protected edge transport in condensed matter physics. The Chern number, the topological invariant of gapped Bloch Hamiltonians, is an important quantity in this field. Another example of topology, in polarization physics, are polarization singularities, called L lines and C points. By establishing a connection between these two theories, we develop a novel technique to visualize and potentially measure the Chern number: it can be expressed either as the winding of the polarization azimuth along L lines in reciprocal space, or in terms of the handedness and the index of the C points. For mechanical systems, this is directly connected to the visible motion patterns.

cond-mat.mes-hall↗