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17 papers · 2 filters
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines
Aidan Kehoe, Peter Wittek, Yanbo Xue +1
We provide a robust defence to adversarial attacks on discriminative algorithms. Neural networks are naturally vulnerable to small, tailored perturbations in the input data that le…
Natural Evolutionary Strategies for Variational Quantum Computation
Abhinav Anand, Matthias Degroote, Alán Aspuru-Guzik
Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly-initialized…
Analog quantum simulation of non-Condon effects in molecular spectroscopy
Hamza Jnane, Nicolas P. D. Sawaya, Borja Peropadre +3
In this work, we present a linear optical implementation for analog quantum simulation of molecular vibronic spectra, incorporating the non-Condon scattering operation with a quadr…
Tequila: A platform for rapid development of quantum algorithms
Jakob S. Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza +15
Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are al…
U(1) symmetric recurrent neural networks for quantum state reconstruction
Stewart Morawetz, Isaac J. S. De Vlugt, Juan Carrasquilla +1
Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experiment…
The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
Alba Cervera-Lierta, Jakob S. Kottmann, Alán Aspuru-Guzik
We present the meta-VQE, an algorithm capable to learn the ground state energy profile of a parametrized Hamiltonian. By training the meta-VQE with a few data points, it delivers a…