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Institute: search.filters.UBSInstitut.Institut für Architektur von AnwendungssystemenAuthor: search.filters.author.Barzen, JohannaAuthor: search.filters.author.Leymann, FrankStart date: 2020Subject: search.filters.subject.004

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Now showing 1 - 9 of 9
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    On reducing the amount of samples required for training of QNNs : constraints on the linear structure of the training data
    (2025) Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Vietz, Daniel
    Training classical neural networks generally requires a large number of training samples. Using entangled training samples, Quantum Neural Networks (QNNs) have the potential to significantly reduce the amount of training samples required in the training process. However, to minimize the number of incorrect predictions made by the resulting QNN, it is essential that the structure of the training samples meets certain requirements. On the one hand, the exact degree of entanglement must be fixed for the whole set of training samples. On the other hand, training samples must be linearly independent and non-orthogonal. However, how failing to meet these requirements affects the resulting QNN is not fully studied. To address this, we extend the proof of the Quantum No-Free-Lunch theorem to (i) provide a generalization of the theorem for varying degrees of entanglement. This generalization shows that the average degree of entanglement in the set of training samples can be used to predict the expected quality of the QNN. Furthermore, we (ii) introduce new estimates for the expected accuracy of QNNs for moderately entangled training samples that are linearly dependent or orthogonal. Our analytical results are (iii) experimentally validated by simulating QNN training and analyzing the quality of the QNN after training.
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    Automated quantum hardware selection for quantum workflows
    (2021) Weder, Benjamin; Barzen, Johanna; Leymann, Frank; Salm, Marie
    The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be very heterogeneous and they depend on the quantum hardware used to execute the quantum circuits of the algorithm. Additionally, today’s quantum computers are still restricted, which limits the size of the quantum circuits that can be executed. As the circuit size often depends on the input data of the algorithm, the selection of quantum hardware to execute a quantum circuit must be done at workflow runtime. However, modeling all possible alternative tasks would clutter the workflow model and require its adaptation whenever a new quantum computer or software tool is released. To overcome this problem, we introduce an approach to automatically select suitable quantum hardware for the execution of quantum circuits in workflows. Furthermore, it enables the dynamic adaptation of the workflows, depending on the selection at runtime based on reusable workflow fragments. We validate our approach with a prototypical implementation and a case study demonstrating the hardware selection for Simon’s algorithm.
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    The bitter truth about gate-based quantum algorithms in the NISQ era
    (2020) Leymann, Frank; Barzen, Johanna
    Implementing a gate-based quantum algorithm on an noisy intermediate scale quantum (NISQ) device has several challenges that arise from the fact that such devices are noisy and have limited quantum resources. Thus, various factors contributing to the depth and width as well as to the noise of an implementation of a gate-based algorithm must be understood in order to assess whether an implementation will execute successfully on a given NISQ device. In this contribution, we discuss these factors and their impact on algorithm implementations. Especially, we will cover state preparation, oracle expansion, connectivity, circuit rewriting, and readout: these factors are very often ignored when presenting a gate-based algorithm but they are crucial when implementing such an algorithm on near-term quantum computers. Our contribution will help developers in charge of realizing gate-based algorithms on such machines in (i) achieving an executable implementation, and (ii) assessing the success of their implementation on a given machine.
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    Configurable readout error mitigation in quantum workflows
    (2022) Beisel, Martin; Barzen, Johanna; Leymann, Frank; Truger, Felix; Weder, Benjamin; Yussupov, Vladimir
    Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, configuration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-specific configuration options. Quantum applications comprise many classical pre- and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workflow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum workflows comprising configurable readout error mitigation tasks. Based on the method configuration, these workflows can then be automatically refined into executable workflow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain.
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    Loss behavior in supervised learning with entangled states
    (2026) Mandl, Alexander; Barzen, Johanna; Bechtold, Marvin; Leymann, Frank; Stiliadou, Lavinia
    Quantum machine learning (QML) aims to leverage the principles of quantum mechanics to speed up the process of solving machine learning problems or improve the quality of solutions. In supervised learning, training samples that are entangled with an auxiliary system were shown to reduce the approximation error of the trained model. However, also the training process is affected by various properties of the supervised learning task, including the model's circuit structure, the used cost function, and noise on the quantum computer. To evaluate the applicability of entanglement in supervised learning, the effect of highly entangled training data on the trainability is investigated in this work. This study shows that for highly expressive models, i.e., models capable of expressing a large number of candidate solutions, the possible improvement of loss function values in constrained neighborhoods during optimization is severely limited when maximally entangled states are used. Furthermore, this finding is experimentally supported by simulating training with Parameterized Quantum Circuits (PQCs). As the expressivity of the PQC increases, this training process becomes more susceptible to loss concentration induced by entangled training data. Lastly, for non‐maximally entangled states, the experiments highlight the fundamental role of entanglement entropy as a predictor for the trainability.
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    Provenance-preserving analysis and rewrite of quantum workflows for hybrid quantum algorithms
    (2023) Weder, Benjamin; Barzen, Johanna; Beisel, Martin; Leymann, Frank
    Quantum applications are hybrid, i.e., they comprise quantum and classical programs, which must be orchestrated. Workflows are a proven solution for orchestrating heterogeneous programs while providing benefits, such as robustness or scalability. However, the orchestration using workflows can be inefficient for some quantum algorithms, requiring the execution of quantum and classical programs in a loop. Hybrid runtimes are offered to efficiently execute these algorithms. For this, the quantum and classical programs are combined in a single hybrid program, for which the execution is optimized. However, this leads to a conceptual gap between the modeling benefits of workflow technologies, e.g., modularization, reuse, and understandability, and the efficiency improvements when using hybrid runtimes. To close this gap, we introduce a method to model all tasks explicitly in the workflow model and analyze the workflow to detect parts of the workflow that can benefit from hybrid runtimes. Furthermore, corresponding hybrid programs are automatically generated based on the quantum and classical programs, and the workflow is rewritten to invoke them. To ease the live monitoring and later analysis of workflow executions, we integrate process views into our method and collect related provenance data. Thus, the user can visualize and monitor the workflow in the original and rewritten form within the workflow engine. The practical feasibility of our approach is validated by a prototypical implementation, a case study, and a runtime evaluation.
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    Simulating quantum state transfer between distributed devices using noisy interconnects
    (2025) Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander
    Scaling beyond individual quantum devices via distributed quantum computing relies critically on high‐fidelity quantum state transfers between devices, yet the quantum interconnects needed for this are currently unavailable or expected to be significantly noisy. These limitations can be bypassed by simulating ideal state transfer using quasiprobability decompositions (QPDs). Wire cutting, for instance, allows this even without quantum interconnects. Nevertheless, QPD methods face drawbacks, requiring sampling from multiple circuit variants and incurring substantial sampling overhead. While prior theoretical work showed that incorporating noisy interconnects within QPD protocols could reduce sampling overhead relative to interconnect quality, a practical implementation for realistic conditions is lacking. Addressing this gap, this work presents a generalized and practical QPD for state transfer simulation using noisy interconnects to reduce sampling overhead. The QPD incorporates a single tunable parameter for straightforward calibration to any utilized interconnect. To lower practical costs, the work also explores reducing the number of distinct circuit variants required by the QPD. Experimental validation on contemporary quantum devices confirms the proposed QPD's practical feasibility and expected sampling overhead reduction under realistic noise. Notably, the results show higher effective state transfer fidelity than direct transfer over the underlying noisy interconnect.
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    Continued fractions and probability estimations in Shor’s algorithm : a detailed and self-contained treatise
    (2022) Barzen, Johanna; Leymann, Frank
    Shor’s algorithm for prime factorization is a hybrid algorithm consisting of a quantum part and a classical part. The main focus of the classical part is a continued fraction analysis. The presentation of this is often short, pointing to text books on number theory. In this contribution, we present the relevant results and proofs from the theory of continued fractions in detail (even in more detail than in text books), filling the gap to allow a complete comprehension of Shor’s algorithm. Similarly, we provide a detailed computation of the estimation of the probability that convergents will provide the period required for determining a prime factor.
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    Joint wire cutting with non‐maximally entangled states
    (2025) Bechtold, Marvin; Barzen, Johanna; Leymann, Frank; Mandl, Alexander; Truger, Felix
    Distributed quantum computing leverages multiple quantum devices collectively to perform computations exceeding each device's capabilities. A currently studied technique to enable this distributed approach is wire cutting, which decomposes a quantum circuit into smaller subcircuits by cutting connecting wires. These subcircuits can be executed on distributed devices, and their results are then classically combined to reconstruct the original computation's result. However, wire cutting requires additional circuit executions to preserve result accuracy, with their number growing exponentially with each cut. Thus, minimizing this sampling overhead is crucial for reducing execution time. Employing shared non‐maximally entangled (NME) states between distributed devices reduces this overhead for single wire cuts, approaching ideal teleportation with maximally entangled states. Extending this approach to jointly cutting multiple wires using NME states remained unexplored. This study addresses this gap by investigating the use of NME states for joint wire cuts, aiming to reduce the sampling overhead further. The three main contributions include (i) determining the minimal sampling overhead for this scenario, (ii) analyzing the overhead when using composite NME states constructed from smaller NME states, and (iii) introducing a wire cutting technique that achieves the optimal sampling overhead with pure NME states, advancing toward wire cutting with arbitrary NME states.