Search Results - "Zoufal, Christa"

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  1. 1

    Quantum Generative Adversarial Networks for learning and loading random distributions by Zoufal, Christa, Lucchi, Aurélien, Woerner, Stefan

    Published in npj quantum information (22-11-2019)
    “…Quantum algorithms have the potential to outperform their classical counterparts in a variety of tasks. The realization of the advantage often requires the…”
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    Journal Article
  2. 2

    Iterative quantum amplitude estimation by Grinko, Dmitry, Gacon, Julien, Zoufal, Christa, Woerner, Stefan

    Published in npj quantum information (19-03-2021)
    “…We introduce a variant of Quantum Amplitude Estimation (QAE) , called Iterative QAE (IQAE), which does not rely on Quantum Phase Estimation (QPE) but is only…”
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    Journal Article
  3. 3

    Variational quantum Boltzmann machines by Zoufal, Christa, Lucchi, Aurélien, Woerner, Stefan

    Published in Quantum machine intelligence (01-06-2021)
    “…This work presents a novel realization approach to quantum Boltzmann machines (QBMs). The preparation of the required Gibbs states, as well as the evaluation…”
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    Journal Article
  4. 4

    Option Pricing using Quantum Computers by Stamatopoulos, Nikitas, Egger, Daniel J., Sun, Yue, Zoufal, Christa, Iten, Raban, Shen, Ning, Woerner, Stefan

    Published in Quantum (Vienna, Austria) (06-07-2020)
    “…We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a…”
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    Journal Article
  5. 5

    Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information by Gacon, Julien, Zoufal, Christa, Carleo, Giuseppe, Woerner, Stefan

    Published in Quantum (Vienna, Austria) (20-10-2021)
    “…The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum…”
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    Journal Article
  6. 6

    Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits by Letcher, Alistair, Woerner, Stefan, Zoufal, Christa

    Published in Quantum (Vienna, Austria) (25-09-2024)
    “…The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients are a central…”
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    Journal Article
  7. 7

    A variational quantum algorithm for the Feynman-Kac formula by Alghassi, Hedayat, Deshmukh, Amol, Ibrahim, Noelle, Robles, Nicolas, Woerner, Stefan, Zoufal, Christa

    Published in Quantum (Vienna, Austria) (07-06-2022)
    “…We propose an algorithm based on variational quantum imaginary time evolution for solving the Feynman-Kac partial differential equation resulting from a…”
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    Journal Article
  8. 8

    Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection by Zoufal, Christa, Mishmash, Ryan V., Sharma, Nitin, Kumar, Niraj, Sheshadri, Aashish, Deshmukh, Amol, Ibrahim, Noelle, Gacon, Julien, Woerner, Stefan

    Published in Quantum (Vienna, Austria) (2023)
    “…We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is…”
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    Journal Article
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    The power of quantum neural networks by Abbas, Amira, Sutter, David, Zoufal, Christa, Lucchi, Aurelien, Figalli, Alessio, Woerner, Stefan

    Published in Nature Computational Science (01-06-2021)
    “…It is unknown whether near-term quantum computers are advantageous for machine learning tasks. In this work we address this question by trying to understand…”
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    Journal Article
  12. 12

    Quantum circuits for discrete graphical models by Piatkowski, Nico, Zoufal, Christa

    Published in Quantum machine intelligence (01-12-2024)
    “…Graphical models are useful tools for describing structured high-dimensional probability distributions. The development of efficient algorithms for generating…”
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    Journal Article
  13. 13

    Generative Quantum Machine Learning by Zoufal, Christa

    Published 24-11-2021
    “…The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to…”
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    Journal Article
  14. 14
  15. 15

    On Quantum Circuits for Discrete Graphical Models by Piatkowski, Nico, Zoufal, Christa

    Published 01-06-2022
    “…Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for generating…”
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    Journal Article
  16. 16

    Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits by Letcher, Alistair, Woerner, Stefan, Zoufal, Christa

    Published 19-09-2024
    “…Quantum 8, 1484 (2024) The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing…”
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    Journal Article
  17. 17

    Error Bounds for Variational Quantum Time Evolution by Zoufal, Christa, Sutter, David, Woerner, Stefan

    Published 27-06-2023
    “…Physical Review Applied, 2023 Variational quantum time evolution allows us to simulate the time dynamics of quantum systems with near-term compatible quantum…”
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    Journal Article
  18. 18

    Quantum-Enhanced Simulation-Based Optimization by Gacon, Julien, Zoufal, Christa, Woerner, Stefan

    “…In this paper, we introduce a quantum-enhanced algorithm for simulation-based optimization. Simulation-based optimization seeks to optimize an objective…”
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    Conference Proceeding
  19. 19

    Stochastic Approximation of Variational Quantum Imaginary Time Evolution by Gacon, Julien, Zoufal, Christa, Carleo, Giuseppe, Woerner, Stefan

    “…The imaginary-time evolution of quantum states is integral to various fields, ranging from natural sciences to classical optimization or machine learning…”
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    Conference Proceeding
  20. 20

    Quantum Kernel Alignment with Stochastic Gradient Descent by Gentinetta, Gian, Sutter, David, Zoufal, Christa, Fuller, Bryce, Woerner, Stefan

    “…Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so…”
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    Conference Proceeding