1.1k citations · 3.9k across the 27 of their papers we have counts for
28 papers
Towards unsupervised representation learning for quantum data: quantum models with inference and generation
Robin Lorenz, Eric Brunner, Marcello Benedetti
With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rather than as classical measurement…
Sparsity-dependent Complexity Lower Bound of Quantum Linear System Solvers
Hitomi Mori, Yuta Kikuchi, Marcello Benedetti +1
Quantum linear system (QLS) solvers are a fundamental class of quantum algorithms used in many potential quantum computing applications, including machine learning and solving diff…
Unconditional and exponentially large violation of classicality
Marcello Benedetti, Gabriel Marin-Sanchez, Jordi Weggemans +2
Testing the predictions of quantum mechanics has been one of the main experimental endeavors for decades. Recent advancements in technology led to a number of demonstrations which…
Probabilistic quantum algorithm for Lyapunov equations and matrix inversion
Marcello Benedetti, Ansis Rosmanis, Matthias Rosenkranz
We present a probabilistic quantum algorithm for preparing mixed states which, in expectation, are proportional to the solutions of Lyapunov equations -- linear matrix equations ub…
Provable and Verifiable Quantum Advantage in Sample Complexity
Marcello Benedetti, Harry Buhrman, Jordi Weggemans
Consider a fixed universe of elements and the uniform distribution over elements of some subset of size . Given samples from this distribution, the task of complement sa…
Measuring Correlation and Entanglement between Molecular Orbitals on a Trapped-Ion Quantum Computer
Gabriel Greene-Diniz, Chris N. Self, Michal Krompiec +4
Quantifying correlation and entanglement between molecular orbitals can elucidate the role of quantum effects in strongly correlated reaction processes. However, accurately storing…
Quantum state preparation for multivariate functions
Matthias Rosenkranz, Eric Brunner, Gabriel Marin-Sanchez +5
A fundamental step of any quantum algorithm is the preparation of qubit registers in a suitable initial state. Often qubit registers represent a discretization of continuous variab…
On the Sample Complexity of Quantum Boltzmann Machine Learning
Luuk Coopmans, Marcello Benedetti
Quantum Boltzmann machines (QBMs) are machine-learning models for both classical and quantum data. We give an operational definition of QBM learning in terms of the difference in e…
Training Quantum Boltzmann Machines with the -Variational Quantum Eigensolver
Onno Huijgen, Luuk Coopmans, Peyman Najafi +2
The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of minimizing the relative entropy…
Realization of quantum signal processing on a noisy quantum computer
Yuta Kikuchi, Conor Mc Keever, Luuk Coopmans +2
Quantum signal processing (QSP) is a powerful toolbox for the design of quantum algorithms and can lead to asymptotically optimal computational costs. Its realization on noisy quan…
Protecting Expressive Circuits with a Quantum Error Detection Code
Chris N. Self, Marcello Benedetti, David Amaro
A successful quantum error correction protocol would allow quantum computers to run algorithms without suffering from the effects of noise. However, fully fault-tolerant quantum er…
Bayesian Learning of Parameterised Quantum Circuits
Samuel Duffield, Marcello Benedetti, Matthias Rosenkranz
Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits. As such, variational quantum algorithms that utilise a classi…
Predicting Gibbs-State Expectation Values with Pure Thermal Shadows
Luuk Coopmans, Yuta Kikuchi, Marcello Benedetti
The preparation and computation of many properties of quantum Gibbs states is essential for algorithms such as quantum semidefinite programming and quantum Boltzmann machines. We p…
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Chiara Leadbeater, Louis Sharrock, Brian Coyle +1
Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum c…
Filtering variational quantum algorithms for combinatorial optimization
David Amaro, Carlo Modica, Matthias Rosenkranz +3
Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more…
Variational inference with a quantum computer
Marcello Benedetti, Brian Coyle, Mattia Fiorentini +2
Inference is the task of drawing conclusions about unobserved variables given observations of related variables. Applications range from identifying diseases from symptoms to class…
Hardware-efficient variational quantum algorithms for time evolution
Marcello Benedetti, Mattia Fiorentini, Michael Lubasch
Parameterized quantum circuits are a promising technology for achieving a quantum advantage. An important application is the variational simulation of time evolution of quantum sys…
Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack +1
Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regard…
Structure optimization for parameterized quantum circuits
Mateusz Ostaszewski, Edward Grant, Marcello Benedetti
We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits…
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski +1
Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of…
Training of Quantum Circuits on a Hybrid Quantum Computer
D. Zhu, N. M. Linke, M. Benedetti +10
Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to tak…
Adversarial quantum circuit learning for pure state approximation
Marcello Benedetti, Edward Grant, Leonard Wossnig +1
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun…
Hierarchical quantum classifiers
Edward Grant, Marcello Benedetti, Shuxiang Cao +5
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circu…
A generative modeling approach for benchmarking and training shallow quantum circuits
Marcello Benedetti, Delfina Garcia-Pintos, Oscar Perdomo +3
Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propos…