1 citations · 1 across the 6 of their papers we have counts for
6 papers
Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
Kevin Shen, Susanne Pielawa, Vedran Dunjko +1
Instantaneous quantum polynomial quantum circuit Born machines (IQP-QCBMs) have been proposed as quantum generative models with a classically tractable training objective based on…
Universality and kernel-adaptive training for classically trained, quantum-deployed generative models
Andrii Kurkin, Kevin Shen, Susanne Pielawa +2
The instantaneous quantum polynomial (IQP) quantum circuit Born machine (QCBM) has been proposed as a promising quantum generative model over bitstrings. Recent works have shown th…
Note on the Universality of Parameterized IQP Circuits with Hidden Units for Generating Probability Distributions
Andrii Kurkin, Kevin Shen, Susanne Pielawa +2
In a series of recent works, an interesting quantum generative model based on parameterized instantaneous polynomial quantum (IQP) circuits has emerged as they can be trained effic…
Weighted Approximate Quantum Natural Gradient for Variational Quantum Eigensolver
Chenyu Shi, Vedran Dunjko, Hao Wang
The variational quantum eigensolver (VQE) is one of the most prominent algorithms using near-term quantum devices, designed to find the ground state of a Hamiltonian. In VQE, a cla…
Variational Quantum Generative Modeling by Sampling Expectation Values of Tunable Observables
Kevin Shen, Andrii Kurkin, Adrián Pérez-Salinas +3
Expectation Value Samplers (EVSs) are quantum generative models that can learn high-dimensional continuous distributions by measuring the expectation values of parameterized quantu…
Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search
Vicente P. Soloviev, Vedran Dunjko, Concha Bielza +2
Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the probl…