Search Results for author: Vedran Dunjko

Found 29 papers, 6 papers with code

Shadows of quantum machine learning

no code implementations31 May 2023 Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Riccardo Molteni, Vedran Dunjko

Quantum machine learning is often highlighted as one of the most promising uses for a quantum computer to solve practical problems.

Quantum policy gradient algorithms

no code implementations19 Dec 2022 Sofiene Jerbi, Arjan Cornelissen, Māris Ozols, Vedran Dunjko

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.

reinforcement-learning Reinforcement Learning (RL)

On establishing learning separations between classical and quantum machine learning with classical data

no code implementations12 Aug 2022 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 case of classical data.

Learning Theory Quantum Machine Learning

Reinforcement Learning Assisted Recursive QAOA

no code implementations13 Jul 2022 Yash J. Patel, Sofiene Jerbi, Thomas Bäck, Vedran Dunjko

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 to tackle hard combinatorial optimization problems.

Combinatorial Optimization reinforcement-learning +1

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets

no code implementations20 Jun 2022 Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko

In this domain, one of the more investigated approaches is the use of a special type of quantum circuit - a so-called quantum neural network -- to serve as a basis for a machine learning model.

BIG-bench Machine Learning Model Selection +1

Equivariant quantum circuits for learning on weighted graphs

2 code implementations12 May 2022 Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, Vedran Dunjko

When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm.

Combinatorial Optimization Quantum Machine Learning

High Dimensional Quantum Machine Learning With Small Quantum Computers

no code implementations25 Mar 2022 Simon C. Marshall, Casper Gyurik, Vedran Dunjko

In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires.

BIG-bench Machine Learning Quantum Machine Learning +1

Quantum machine learning beyond kernel methods

1 code implementation25 Oct 2021 Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Jonas M. Kübler, Hans J. Briegel, Vedran Dunjko

In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models.

BIG-bench Machine Learning Quantum Machine Learning

Structural risk minimization for quantum linear classifiers

no code implementations12 May 2021 Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko

Firstly, using relationships to well understood classical models, we prove that two model parameters -- i. e., the dimension of the sum of the images and the Frobenius norm of the observables used by the model -- closely control the models' complexity and therefore its generalization performance.

BIG-bench Machine Learning Quantum Machine Learning

Reinforcement learning for optimization of variational quantum circuit architectures

no code implementations NeurIPS 2021 Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, Vedran Dunjko

The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices.

reinforcement-learning Reinforcement Learning (RL)

Parametrized quantum policies for reinforcement learning

no code implementations NeurIPS 2021 Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko

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 is gaining increasing traction.

Benchmarking reinforcement-learning +1

Certificates of quantum many-body properties assisted by machine learning

1 code implementation5 Mar 2021 Borja Requena, Gorka Muñoz-Gil, Maciej Lewenstein, Vedran Dunjko, Jordi Tura

A number of standard methods are used to tackle such problems: variational approaches focus on parameterizing a subclass of solutions within the feasible set; in contrast, relaxation techniques have been proposed to approximate it from outside, thus complementing the variational approach by providing ultimate bounds to the global optimal solution.

Transfer Learning Quantum Physics

Towards quantum advantage via topological data analysis

no code implementations6 May 2020 Casper Gyurik, Chris Cade, Vedran Dunjko

Our results provide a number of useful applications for full-blown, and restricted quantum computers with a guaranteed exponential speedup over classical methods, recovering some of the potential for linear-algebraic QML to become one of quantum computing's killer applications.

Quantum Machine Learning Topological Data Analysis

Quantum enhancements for deep reinforcement learning in large spaces

1 code implementation28 Oct 2019 Sofiene Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Hans J. Briegel, Vedran Dunjko

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 algorithmic methods.

BIG-bench Machine Learning Decision Making +3

On the convergence of projective-simulation-based reinforcement learning in Markov decision processes

no code implementations25 Oct 2019 Walter L. Boyajian, Jens Clausen, Lea M. Trenkwalder, Vedran Dunjko, Hans J. Briegel

Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes.

reinforcement-learning Reinforcement Learning (RL)

Optimizing Quantum Error Correction Codes with Reinforcement Learning

no code implementations20 Dec 2018 Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, Nicolai Friis

Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest.

reinforcement-learning Reinforcement Learning (RL) +1

Computational speedups using small quantum devices

no code implementations24 Jul 2018 Vedran Dunjko, Yimin Ge, J. Ignacio Cirac

Suppose we have a small quantum computer with only M qubits.

Neural Network Operations and Susuki-Trotter evolution of Neural Network States

1 code implementation6 Mar 2018 Nahuel Freitas, Giovanna Morigi, Vedran Dunjko

We show that this parametrization contains a set of universal quantum gates, from which it follows that the state prepared by any quantum circuit can be expressed as a Neural Network State with a number of hidden nodes that grows linearly with the number of elementary operations in the circuit.

Quantum Physics

Exponential improvements for quantum-accessible reinforcement learning

no code implementations30 Oct 2017 Vedran Dunjko, Yi-Kai Liu, Xingyao Wu, Jacob M. Taylor

Quantum computers can offer dramatic improvements over classical devices for data analysis tasks such as prediction and classification.

reinforcement-learning Reinforcement Learning (RL)

Machine learning \& artificial intelligence in the quantum domain

no code implementations8 Sep 2017 Vedran Dunjko, Hans J. Briegel

For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world.

BIG-bench Machine Learning

Speeding-up the decision making of a learning agent using an ion trap quantum processor

no code implementations5 Sep 2017 Theeraphot Sriarunothai, Sabine Wölk, Gouri Shankar Giri, Nicolai Friis, Vedran Dunjko, Hans J. Briegel, Christof Wunderlich

We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions.

BIG-bench Machine Learning Decision Making

Active learning machine learns to create new quantum experiments

no code implementations2 Jun 2017 Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel

We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.

Active Learning

Quantum-enhanced machine learning

no code implementations26 Oct 2016 Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel

Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements.

BIG-bench Machine Learning Quantum Machine Learning +2

Meta-learning within Projective Simulation

no code implementations25 Feb 2016 Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel

The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.

Meta-Learning reinforcement-learning +1

Framework for learning agents in quantum environments

no code implementations30 Jul 2015 Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel

In this paper we provide a broad framework for describing learning agents in general quantum environments.

Projective simulation with generalization

no code implementations9 Apr 2015 Alexey A. Melnikov, Adi Makmal, Vedran Dunjko, Hans J. Briegel

Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.

Faster quantum mixing for slowly evolving sequences of Markov chains

no code implementations4 Mar 2015 Davide Orsucci, Hans J. Briegel, Vedran Dunjko

Markov chain methods are remarkably successful in computational physics, machine learning, and combinatorial optimization.

BIG-bench Machine Learning Combinatorial Optimization

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