462 citations · 4.5k across the 108 of their papers we have counts for
105 papers · 1 filter
Cautious optimism for deep parameterized quantum circuits
Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performa…
Provable learning separation for predicting time-evolution of quantum many-body systems
Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert +2
Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantu…
Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits
Onur Danaci, Yash J. Patel, Riccardo Molteni +3
Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and unde…
Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models
Andrii Kurkin, Ulysse Chabaud, Zoltán Kolarovszki +3
Recent work on the instantaneous quantum polynomial-time (IQP) quantum-circuit Born machine (QCBM) highlights a promising paradigm for generative modeling: train classically, deplo…
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…
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…
Quantum generative modeling for financial time series with temporal correlations
David Dechant, Eliot Schwander, Lucas van Drooge +4
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks.…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
Alice Barthe, Mahtab Yaghubi Rad, Michele Grossi +1
One of the key challenges in quantum machine learning is finding relevant machine learning tasks with a provable quantum advantage. A natural candidate for this is learning unknown…
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…
Detecting underdetermination in parameterized quantum circuits
Marie Kempkes, Jakob Spiegelberg, Evert van Nieuwenburg +1
A central question in machine learning is how reliable the predictions of a trained model are. Reliability includes the identification of instances for which a model is likely not…
Quantum machine learning advantages beyond hardness of evaluation
Riccardo Molteni, Simon C. Marshall, Vedran Dunjko
The most general examples of quantum learning advantages involve data labeled by cryptographic or intrinsically quantum functions, where classical learners are limited by the infea…
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…
Double descent in quantum kernel methods
Marie Kempkes, Aroosa Ijaz, Elies Gil-Fuster +4
The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen…
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…