arXiv subjects

Michael Krebsbach

Publications and source records attributed to Michael Krebsbach.

4 recordsLinked to original sources

arXiv2026

Support Vector Machine with a Scalable Quantum Kernel

Quantum support vector machines are classification algorithms that rely on quantum-generated kernels. The fidelity quantum kernel commonly used in quantum support vector machines suffers from exponential concentration as system size increases, preventing an efficient scaling beyond fewqubit systems. We introduce the Hamming quantum kernel, a classical post-processing method that is based on the same measurement outcomes as the fidelity quantum kernel. However, it avoids the exponential concentration problem by using the full measurement statistics rather than a single fidelity value. We evaluate the approach on both classical data (MNIST) and synthetic data generated from quantum circuits, using systems ranging from 2 to 27 qubits. Throughout the simulations, the Hamming quantum kernel outperforms the fidelity quantum kernel whenever 15 or more qubits are used. Furthermore, for synthetic quantum data, our method consistently outperforms the classical Gaussian kernel. This demonstrates that the Hamming quantum kernel improves the expressivity and robustness at larger qubit scales without requiring any additional quantum ressources.

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arXiv2026

Encoding Numerical Data for Generative Quantum Machine Learning

Generative quantum machine learning models are trained to deduce the probability distribution underlying a given dataset, and to produce new, synthetic samples from it. The majority of such models proposed in the literature, like the Quantum Circuit Born Machine (QCBM), fundamentally work on a binary level. Real-world data, however, is often numeric, requiring the models to translate between binary and continuous representations. We analyze how this transition influences the performance of quantum models and show that it requires the models to learn correlations that are solely an artifact of the way the data is encoded, and not related to the data itself. At the same time, structure of the original data, like continuity, can be obscured in the binary representation, hindering generalization. To mitigate these effects, we propose a strategy based on Gray-codes that can be implemented with essentially no overhead, conserves structures in the data, and avoids artificial correlations in situations in which the standard approach creates them. Considering datasets drawn from various low-dimensional probability distributions, we verify that, in most cases, QCBMs using the reflected Gray code learn faster and more accurately than those with standard binary code. This shows that, as complement to specifically tailored circuit ans\"atze or data pre-processing, binary encodings can be used to introduce inductive biases into generative machine learning models.

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arXiv2024

Pattern-based quantum functional testing

With the growing number of qubits of quantum information processing devices, the task of fully characterizing these processors becomes increasingly unfeasible. From a practical perspective, one wants to find possible errors in the functioning of the device as quickly as possible, or otherwise establish its correct functioning with high confidence. In response to these challenges, we propose a pattern-based approach inspired by classical memory testing algorithms to evaluate the functionality of a quantum memory, based on plausible failure mechanisms. We demonstrate the method's capability to extract pattern dependencies of important qubit characteristics, such as $T_1$ and $T_2$ times, and to identify and analyze interactions between adjacent qubits. Additionally, our approach enables the detection of different types of crosstalk effects and of signatures indicating non-Markovian dynamics in individual qubits.

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arXiv2022

Optimization of Richardson extrapolation for quantum error mitigation

Quantum error mitigation is a key concept for the development of practical applications based on current noisy intermediate scale quantum (NISQ) devices. One of the most promising methods is Richardson extrapolation to the zero noise limit. While its main idea is rather simple, the full potential of Richardson extrapolation has not been completely uncovered yet. We give an in-depth analysis of the relevant parameters of Richardson extrapolation and propose an optimized protocol for its implementation. This protocol allows for a precise control of the increase in statistical uncertainty and lays the foundation for a significant improvement of the mitigation performance achieved by increasing the number of nodes. Furthermore, we present a novel set of nodes that, on average, outperforms the linear, exponential or Chebyshev nodes frequently used for Richardson extrapolation without requiring any additional resources.

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