212 citations · 712 across the 23 of their papers we have counts for
25 papers
Efficient learning of Clifford disentanglers and typical -doped unitaries with exponentially more gates
Gerard Aguilar, Sofiene Jerbi, Jens Eisert +1
Highly entangled and highly non-stabilizer quantum states need not be hard to learn. We give efficient algorithms for testing and recovering hidden tensor-product structure in unkn…
Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
Pablo Rodriguez-Grasa, Sofiene Jerbi, Mikel Sanz +1
In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement learning, this approach has led to…
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…
Optimal algorithmic complexity of inference in quantum kernel methods
Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi +2
Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained mode…
The computational two-way quantum capacity
Johannes Jakob Meyer, Jacopo Rizzo, Asad Raza +3
Quantum channel capacities are fundamental to quantum information theory. Their definition, however, does not limit the computational resources of sender and receiver. In this work…
Computational relative entropy
Johannes Jakob Meyer, Asad Raza, Jacopo Rizzo +3
Our capacity to process information depends on the computational power at our disposal. Information theory captures our ability to distinguish states or communicate messages when i…
Entanglement theory with limited computational resources
Lorenzo Leone, Jacopo Rizzo, Jens Eisert +1
The precise quantification of the ultimate efficiency in manipulating quantum resources lies at the core of quantum information theory. However, purely information-theoretic measur…
A quantum-classical reinforcement learning model to play Atari games
Dominik Freinberger, Julian Lemmel, Radu Grosu +1
Recent advances in reinforcement learning have demonstrated the potential of quantum learning models based on parametrized quantum circuits as an alternative to deep learning model…
Efficient distributed inner product estimation via Pauli sampling
Marcel Hinsche, Marios Ioannou, Sofiene Jerbi +3
Cross-platform verification is the task of comparing the output states produced by different physical platforms using solely local quantum operations and classical communication. W…
Variational measurement-based quantum computation for generative modeling
Arunava Majumder, Marius Krumm, Tina Radkohl +4
Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms. Indeed, due to the inherent randomness of quantum measurements, th…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…
Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
Francesco Preti, Michael Schilling, Sofiene Jerbi +4
Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such c…
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…
The power and limitations of learning quantum dynamics incoherently
Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph +4
Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share qu…
Quantum policy gradient algorithms
Sofiene Jerbi, Arjan Cornelissen, Māris Ozols +1
Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. P…
Reinforcement Learning Assisted Recursive QAOA
Yash J. Patel, Sofiene Jerbi, Thomas Bäck +1
Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices…
Near-Optimal Quantum Algorithms for Multivariate Mean Estimation
Arjan Cornelissen, Yassine Hamoudi, Sofiene Jerbi
We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the mean of a vector-valued random variable with finite mean and covariance. Our result aims at…
Quantum machine learning beyond kernel methods
Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup +3
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of q…
Quantum algorithms for multivariate Monte Carlo estimation
Arjan Cornelissen, Sofiene Jerbi
We consider the problem of estimating the expected outcomes of Monte Carlo processes whose outputs are described by multidimensional random variables. We tightly characterize the q…
Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
Andrea Skolik, Sofiene Jerbi, Vedran Dunjko
Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry. R…
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…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Michael Broughton, Guillaume Verdon, Trevor McCourt +26
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…
Operationally meaningful representations of physical systems in neural networks
Hendrik Poulsen Nautrup, Tony Metger, Raban Iten +5
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most…
Quantum enhancements for deep reinforcement learning in large spaces
Sofiene Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup +2
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorit…