68 citations · 257 across the 18 of their papers we have counts for
18 papers
Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Pauline Mathiot, Elio Garnaoui, Axel-Ugo Leriche +37
We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel…
Computational complexity of the homology problem with orientable filtration: MA-completeness
Ryu Hayakawa, Casper Gyurik, Mahtab Yaghubi Rad +1
We show the existence of an MA-complete homology problem for a certain subclass of simplicial complexes. The problem is defined through a new concept of orientability of simplicial…
From quantum feature maps to quantum reservoir computing: perspectives and applications
Casper Gyurik, Filip Wudarski, Evan Philip +5
We explore the interplay between two emerging paradigms: reservoir computing and quantum computing. We observe how quantum systems featuring beyond-classical correlations and vast…
Differential equation quantum solvers: engineering measurements to reduce cost
Annie Paine, Casper Gyurik, Antonio Andrea Gentile
Quantum computers have been proposed as a solution for efficiently solving non-linear differential equations (DEs), a fundamental task across diverse technological and scientific d…
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…
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…
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…
Limitations of measure-first protocols in quantum machine learning
Casper Gyurik, Riccardo Molteni, Vedran Dunjko
In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomograph…
Exponential separations between classical and quantum learners
Casper Gyurik, Vedran Dunjko
Despite significant effort, the quantum machine learning community has only demonstrated quantum learning advantages for artificial cryptography-inspired datasets when dealing with…
Shadows of quantum machine learning
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall +2
Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a ma…
Analyzing Prospects for Quantum Advantage in Topological Data Analysis
Dominic W. Berry, Yuan Su, Casper Gyurik +7
Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, ana…
On establishing learning separations between classical and quantum machine learning with classical data
Casper Gyurik, Vedran Dunjko
Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the c…
High Dimensional Quantum Machine Learning With Small Quantum Computers
Simon C. Marshall, Casper Gyurik, Vedran Dunjko
Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation…
Structural risk minimization for quantum linear classifiers
Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko
Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing's near-term ``killer application''. However,…
Parametrized quantum policies for reinforcement learning
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall +2
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system i…
Towards quantum advantage via topological data analysis
Casper Gyurik, Chris Cade, Vedran Dunjko
Even after decades of quantum computing development, examples of generally useful quantum algorithms with exponential speedups over classical counterparts are scarce. Recent progre…