Search Results - "Benedetti, Marcello"

An initialization strategy for addressing barren plateaus in parametrized quantum circuits by Grant, Edward, Wossnig, Leonard, Ostaszewski, Mateusz, Benedetti, Marcello

“…Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially…”
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Adversarial quantum circuit learning for pure state approximation by Benedetti, Marcello, Grant, Edward, Wossnig, Leonard, Severini, Simone

“…Adversarial learning is one of the most successful approaches to modeling high-dimensional probability distributions from data. The quantum computing community…”
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A generative modeling approach for benchmarking and training shallow quantum circuits by Benedetti, Marcello, Garcia-Pintos, Delfina, Perdomo, Oscar, Leyton-Ortega, Vicente, Nam, Yunseong, Perdomo-Ortiz, Alejandro

“…Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such…”
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Structure optimization for parameterized quantum circuits by Ostaszewski, Mateusz, Grant, Edward, Benedetti, Marcello

“…We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational…”
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Hardware-efficient variational quantum algorithms for time evolution by Benedetti, Marcello, Fiorentini, Mattia, Lubasch, Michael

“…Parameterized quantum circuits are a promising technology for achieving a quantum advantage. An important application is the variational simulation of time…”
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Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models by Benedetti, Marcello, Realpe-Gómez, John, Biswas, Rupak, Perdomo-Ortiz, Alejandro

“…Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability…”
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On the sample complexity of quantum Boltzmann machine learning by Coopmans, Luuk, Benedetti, Marcello

“…Quantum Boltzmann machines (QBMs) are machine-learning models for both classical and quantum data. We give an operational definition of QBM learning in terms…”
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F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits by Leadbeater, Chiara, Sharrock, Louis, Coyle, Brian, Benedetti, Marcello

“…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…”
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Predicting Gibbs-State Expectation Values with Pure Thermal Shadows by Coopmans, Luuk, Kikuchi, Yuta, Benedetti, Marcello

“…The preparation and computation of many properties of quantum Gibbs states is essential for algorithms such as quantum semidefinite programming and quantum…”
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Bayesian learning of parameterised quantum circuits by Duffield, Samuel, Benedetti, Marcello, Rosenkranz, Matthias

“…Abstract Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits. As such, variational quantum…”
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Training quantum Boltzmann machines with the β-variational quantum eigensolver by Huijgen, Onno, Coopmans, Luuk, Najafi, Peyman, Benedetti, Marcello, Kappen, Hilbert J

“…Abstract The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of…”
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Realization of quantum signal processing on a noisy quantum computer by Kikuchi, Yuta, Mc Keever, Conor, Coopmans, Luuk, Lubasch, Michael, Benedetti, Marcello

“…Quantum signal processing (QSP) is a powerful toolbox for the design of quantum algorithms and can lead to asymptotically optimal computational costs. Its…”
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Protecting expressive circuits with a quantum error detection code by Self, Chris N., Benedetti, Marcello, Amaro, David

“…A successful quantum error correction protocol would allow quantum computers to run algorithms without suffering from the effects of noise. However, fully…”
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Hierarchical quantum classifiers by Grant, Edward, Benedetti, Marcello, Cao, Shuxiang, Hallam, Andrew, Lockhart, Joshua, Stojevic, Vid, Green, Andrew G., Severini, Simone

“…Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that…”
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Quantum-Classical Generative Models for Machine Learning by Benedetti, Marcello

“…The combination of quantum and classical computational resources towards more effective algorithms is one of the most promising research directions in computer…”
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On the Sample Complexity of Quantum Boltzmann Machine Learning by Coopmans, Luuk, Benedetti, Marcello

“…Communications Physics 7, 274 (2024) Quantum Boltzmann machines (QBMs) are machine-learning models for both classical and quantum data. We give an operational…”
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Protecting Expressive Circuits with a Quantum Error Detection Code by Self, Chris N, Benedetti, Marcello, Amaro, David

“…Nat. Phys. 20, 219-224 (2024) A successful quantum error correction protocol would allow quantum computers to run algorithms without suffering from the effects…”
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Predicting Gibbs-State Expectation Values with Pure Thermal Shadows by Coopmans, Luuk, Kikuchi, Yuta, Benedetti, Marcello

“…PRX Quantum 4, 010305 (2023) The preparation and computation of many properties of quantum Gibbs states is essential for algorithms such as quantum…”
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Bayesian Learning of Parameterised Quantum Circuits by Duffield, Samuel, Benedetti, Marcello, Rosenkranz, Matthias

“…Mach. Learn.: Sci. Technol. 4, 025007 (2023) Currently available quantum computers suffer from constraints including hardware noise and a limited number of…”
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Hardware-efficient variational quantum algorithms for time evolution by Benedetti, Marcello, Fiorentini, Mattia, Lubasch, Michael

“…Phys. Rev. Research 3, 033083 (2021) Parameterized quantum circuits are a promising technology for achieving a quantum advantage. An important application is…”
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