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Lorenz, Jeanette Miriam

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  • Publication
    A Comparative Study on Solving Optimization Problems with Exponentially Fewer Qubits
    ( 2024)
    Winderl, David
    ;
    Franco, Nicola
    ;
    Lorenz, Jeanette Miriam orcid-logo
    Variational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on the VQE, which uses exponentially fewer qubits compared to the QAOA. We highlight the numerical instabilities generated by encoding the problem into the variational ansatz and propose a classical optimization procedure to find the ground state of the ansatz in fewer iterations with a better or similar objective. In addition, we propose a method to embed the linear interpolation of the MaxCut problem on a quantum device. Furthermore, we compare classical optimizers for this variational ansatz on quadratic unconstrained binary optimization and graph partitioning problems.
  • Publication
    Benchmarking the Variational Quantum Eigensolver using different quantum hardware
    ( 2023)
    Bentellis, Amine
    ;
    Matic-Flierl, Andrea
    ;
    Mendl, Christian B.
    ;
    Lorenz, Jeanette Miriam orcid-logo
    The Variational Quantum Eigensolver (VQE) is a promising quantum algorithm for applications in chemistry within the Noisy Intermediate-Scale Quantum (NISQ) era. The ability for a quantum computer to simulate electronic structures with high accuracy would have a profound impact on material and biochemical science with potential applications e.g., to the development of new drugs. However, considering the variety of quantum hardware architectures, it is still uncertain which hardware concept is most suited to execute the VQE for e.g., the simulation of molecules. Aspects to consider here are the required connectivity of the quantum circuit used, the size and the depth and thus the susceptibility to noise effects. Besides theo-retical considerations, empirical studies using available quantum hardware may help to clarify the question of which hardware technology might be better suited for a certain given application and algorithm. Going one step into this direction, within this work, we present results using the VQE for the simulation of the hydrogen molecule, comparing superconducting and ion trap quantum computers. The experiments are carried out with a standardized setup of ansatz and optimizer, selected to reduce the number of required iterations. The findings are analyzed considering different quantum processor types, calibration data as well as the depth and gate counts of the circuits required for the different hardware concepts after transpilation.
  • Publication
    QuaST - Quantum enabling Services and Tools for industrial applications
    ( 2023)
    Lorenz, Jeanette Miriam orcid-logo
    This talk presents progress on the QuaST project with respect to developing an additional abstraction layer for solving industrial optimization problems via quantum computing. Additionally, a quantum-assisted solution of a Capacitated Vehicle Routing Problem (CVRP) is presented.
  • Publication
    Machine learning and simulation on NISQ devices
    ( 2022)
    Sakhnenko, Alona
    ;
    Lorenz, Jeanette Miriam orcid-logo
    Our team is investigating many fields, which hold a potential for a practical quantum advantage. In this presentation, we show-case some of our recent achievements within the realm of quantum simulation and quantum machine learning. Specifically, we presented some of our results within Munich Quantum Valley project, such as a recent publication in Noise impact investigation for VQE algorithm, results in Quantum reinforcement learning and current state-of-the-art in Quantum kernel methods. A recent publication in Quantum Convolutional NN was presented as well.
  • Publication
    A Comparative Study on Solving Optimization Problems with Exponentially Fewer Qubits
    ( 2022)
    Winderl, David
    ;
    Franco, Nicola
    ;
    Lorenz, Jeanette Miriam orcid-logo
    Variational Quantum optimization algorithms, such as the Variational Quantum Eigensolver (VQE) or the Quantum Approximate Optimization Algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on VQE, which uses exponentially fewer qubits compared to the QAOA. We highlight the numerical instabilities generated by encoding the problem into the variational ansatz and propose a classical optimization procedure to find the ground-state of the ansatz in less iterations with a better or similar objective. Furthermore, we compare classical optimizers for this variational ansatz on quadratic unconstrained binary optimization and graph partitioning problems.
  • Publication
    The Effect of Noise on the Performance of the Variational Quantum Eigensolver
    ( 2022)
    Oliv, Marita
    ;
    Matic, Andrea
    ;
    Messerer, Thomas
    ;
    Lorenz, Jeanette Miriam orcid-logo
    Quantum computers are expected to be highly beneficial for chemistry simulations, promising significant improvements in accuracy and speed. The most prominent algorithm for chemistry simulations on NISQ devices is the Variational Quantum Eigensolver (VQE). It is a hybrid quantum-classical algorithm which calculates the ground state energy of a Hamiltonian based on parametrized quantum circuits, while a classical optimizer is used to find optimal parameter values. However, quantum hardware is affected by noise, and it needs to be understood to which extent it can degrade the performance of the VQE algorithm. In this paper, we study the impact of noise on the example of the hydrogen molecule. First, we compare the VQE performance for a set of various optimizers, from which we find NFT to be the most suitable one. Next, we quantify the effect of different noise sources by systematically increasing their strength. The noise intensity is varied around values common to superconducting devices of IBM Q, and curve fitting is used to model the relationship between the obtained energy values and the noise magnitude. Since the amount of noise in a circuit highly depends on its architecture, we perform our studies for different ansatzes, including both hardware-efficient and chemistry-inspired ones.
  • Publication
    The impact of noise on the Variational Quantum Eigensolver
    ( 2022)
    Oliv, Marita
    ;
    Matic-Flierl, Andrea
    ;
    Lorenz, Jeanette Miriam orcid-logo
    This talk at BQCX event at LRZ is about the results of a project on the impact of noise on the Variational Quantum Eigensolver. The results can partly be found as preprints at https://www.researchsquare.com/article/rs-2640456/v1 and https://arxiv.org/abs/2209.12803 as well.