462 citations · 4.5k across the 108 of their papers we have counts for
13 papers · 1 filter
Testing the presence of balanced and bipartite components in a sparse graph is QMA1-hard
Massimiliano Incudini, Casper Gyurik, Riccardo Molteni +1
Determining whether an abstract simplicial complex, a discrete object often approximating a manifold, contains multi-dimensional holes is a task deeply connected to quantum mechani…
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…
Error and Resource Estimates of Variational Quantum Algorithms for Solving Differential Equations Based on Runge-Kutta Methods
David Dechant, Liubov Markovich, Vedran Dunjko +1
A focus of recent research in quantum computing has been on developing quantum algorithms for differential equations solving using variational methods on near-term quantum devices.…
Universal approximation of continuous functions with minimal quantum circuits
Adrián Pérez-Salinas, Mahtab Yaghubi Rad, Alice Barthe +1
The conventional paradigm of quantum computing is discrete: it utilizes discrete sets of gates to realize bitstring-to-bitstring mappings, some of them arguably intractable for cla…
Multiple-basis representation of quantum states
Adrián Pérez-Salinas, Patrick Emonts, Jordi Tura +1
Classical simulation of quantum physics is a central approach to investigating physical phenomena. Quantum computers enhance computational capabilities beyond those of classical re…
Improved separation between quantum and classical computers for sampling and functional tasks
Simon C. Marshall, Scott Aaronson, Vedran Dunjko
This paper furthers existing evidence that quantum computers are capable of computations beyond classical computers. Specifically, we strengthen the collapse of the polynomial hier…
Provable quantum speedups for computing persistence in topological data analysis
Casper Gyurik, Alexander Schmidhuber, Robbie King +2
Topological data analysis (TDA) aims to extract noise-robust features from a data set by examining the number and persistence of holes in its topology. We provide an efficient quan…
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…
On the relation between trainability and dequantization of variational quantum learning models
Elies Gil-Fuster, Casper Gyurik, Adrián Pérez-Salinas +1
The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classi…
On Bounded Advice Classes
Simon Marshall, Casper Gyurik, Vedran Dunjko
Advice classes in computational complexity have frequently been used to model real-world scenarios encountered in cryptography, quantum computing and machine learning, where some c…
Exponential quantum advantages in learning quantum observables from classical data
Riccardo Molteni, Casper Gyurik, Vedran Dunjko
Quantum computers are believed to bring computational advantages in simulating quantum many body systems. However, recent works have shown that classical machine learning algorithm…
Curriculum reinforcement learning for quantum architecture search under hardware errors
Yash J. Patel, Akash Kundu, Mateusz Ostaszewski +3
The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a p…
Parameterized quantum circuits as universal generative models for continuous multivariate distributions
Alice Barthe, Michele Grossi, Sofia Vallecorsa +2
Parameterized quantum circuits have been extensively used as the basis for machine learning models in regression, classification, and generative tasks. For supervised learning, the…