arXiv subjects

Matthias G. Krauss

Publications and source records attributed to Matthias G. Krauss.

7 recordsLinked to original sources

arXiv2026

Mitigating Dynamic Crosstalk with Optimal Control

The prevalence of quantum crosstalk is an important barrier to scaling frequency-addressable qubit architectures, with dynamic crosstalk being particularly difficult to detect and suppress. This form of crosstalk refers to unintended interactions driven by the gate control fields themselves. Here, we minimize dynamic crosstalk using quantum optimal control based on the perfect entangler spectrum, where spectral peaks signal unwanted entanglement with spectator qubits. Focusing on parametric gates in tunable coupler systems, we derive pulse shapes that eliminate dynamic crosstalk. Remarkably, only minimal pulse modifications are required to mitigate the form of crosstalk that is otherwise most difficult to predict. The ability to suppress dynamic crosstalk via the perfect entangler spectrum establishes a generalizable control principle for eliminating unwanted interactions in quantum hardware.

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arXiv2025

Achieving fast and robust perfect entangling gates via reinforcement learning

Noisy intermediate-scale quantum computers hold the promise of tackling complex and otherwise intractable computational challenges through the massive parallelism offered by qubits. Central to realizing the potential of quantum computing are perfect entangling (PE) two-qubit gates, which serve as a critical building block for universal quantum computation. In the context of quantum optimal control, shaping electromagnetic pulses to drive quantum gates is crucial for pushing gate performance toward theoretical limits. In this work, we leverage reinforcement learning (RL) techniques to discover near-optimal pulse shapes that yield PE gates. A collection of RL agents is trained within robust simulation environments, enabling the identification of effective control strategies even under noisy conditions. Selected agents are then validated on higher-fidelity simulations, illustrating how RL-based methods can reduce calibration overhead when compared to quantum optimal control techniques. Furthermore, the RL approach is hardware agnostic with the potential for broad applicability across various quantum computing platforms.

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arXiv2025

The perfect entangler spectrum as a tool to analyze crosstalk

Crosstalk is a key obstacle to scaling up quantum computers. It may arise from persistent qubit-qubit couplings or dynamically during gate operation, with the latter being particularly difficult to detect. Here, we introduce the perfect entangler spectrum as a means to identify dynamic crosstalk leading to undesired entanglement. It leverages the geometric classification of two-qubit gates in terms of perfect entanglers. We exemplify application of the spectroscopy for fixed-frequency transmons and parametrically driven gates: When scanning the frequency of a spectator qubit, peaks in the perfect entangler spectrum signal dynamic crosstalk, and analysis of the peaks reveals the mechanisms causing the crosstalk. We discuss the experimental implementation of the crosstalk spectroscopy which requires two two-qubit gate tomographies.

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arXiv2024

Quantum Optimal Control of Squeezing in Cavity Optomechanics

Squeezing is a non-classical feature of quantum states that is a useful resource, for example in quantum sensing of mechanical forces. Here, we show how to use optimal control theory to maximize squeezing in an optomechanical setup with two external drives and determine how fast the mechanical mode can be squeezed. For the autonomous drives considered here, we find the inverse cavity decay to lower-bound the protocol duration. At and above this limit, we identify a family of protocols leveraging a two-stage control strategy, where the mechanical mode is cooled before it is squeezed. Identification of the control strategy allows for two important insights - to determine the factors that limit squeezing and to simplify the time-dependence of the external drives, making our protocol readily applicable in experiments.

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arXiv2023

Optimizing for an arbitrary Schrödinger cat state. II. Application in the presence of dissipation

We extend here the optimization functional targeting arbitrary cat states, derived in the companion paper, to open quantum system dynamics. Applying it to a Jaynes-Cummings model with decay on the oscillator, we find, for strong dissipation and large cat radii, a change in the control strategy for preparing an entangled cat state. Our results illustrate the versatility of the quantum optimal control toolbox for practical applications in the quantum technologies.

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arXiv2023

Optimizing for an arbitrary Schrödinger cat state

We derive a set of functionals for optimization towards an arbitrary cat state and demonstrate their application by optimizing the dynamics of a Kerr-nonlinear Hamiltonian with two-photon driving. The versatility of our framework allows us to adapt our functional towards optimization of maximally entangled cat states, applying it to a Jaynes-Cummings model. We identify the strategy of the obtained control fields and determine the quantum speed limit as a function of the cat state's excitation. Finally, we extend our optimization functionals to open quantum system dynamics and apply it to the Jaynes-Cummings model with decay on the oscillator. For strong dissipation and large cat radii, we find a change in the control strategy compared to the case without dissipation. Our results highlight the power of optimal control with functionals specifically crafted for complex physical tasks and the versatility of the quantum optimal control toolbox for practical applications in the quantum technologies.

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arXiv2019

Krotov: A Python implementation of Krotov's method for quantum optimal control

We present a new open-source Python package, krotov, implementing the quantum optimal control method of that name. It allows to determine time-dependent external fields for a wide range of quantum control problems, including state-to-state transfer, quantum gate implementation and optimization towards an arbitrary perfect entangler. Krotov's method compares to other gradient-based optimization methods such as gradient-ascent and guarantees monotonic convergence for approximately time-continuous control fields. The user-friendly interface allows for combination with other Python packages, and thus high-level customization. The package is being developed at https://github.com/qucontrol/krotov

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