b'Vedran Dunjko'

research

∙ 06/28/2023

Exponential separations between classical and quantum learners

Despite significant effort, the quantum machine learning community has o...

0 Casper Gyurik, et al. ∙

research

∙ 06/19/2023

Enhancing variational quantum state diagonalization using reinforcement learning techniques

The development of variational quantum algorithms is crucial for the app...

0 Akash Kundu, et al. ∙

research

∙ 05/31/2023

Shadows of quantum machine learning

Quantum machine learning is often highlighted as one of the most promisi...

0 Sofiène Jerbi, et al. ∙

research

∙ 12/19/2022

Quantum policy gradient algorithms

Understanding the power and limitations of quantum access to data in mac...

0 Sofiène Jerbi, et al. ∙

research

∙ 08/12/2022

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

Despite years of effort, the quantum machine learning community has only...

0 Casper Gyurik, et al. ∙

research

∙ 07/13/2022

Reinforcement Learning Assisted Recursive QAOA

Variational quantum algorithms such as the Quantum Approximation Optimiz...

0 Yash J. Patel, et al. ∙

research

∙ 06/20/2022

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets

As restricted quantum computers are slowly becoming a reality, the searc...

0 Charles Moussa, et al. ∙

research

∙ 05/12/2022

Equivariant quantum circuits for learning on weighted graphs

Variational quantum algorithms are the leading candidate for near-term a...

8 Andrea Skolik, et al. ∙

research

∙ 03/25/2022

High Dimensional Quantum Learning With Small Quantum Computers

Quantum computers hold great promise to enhance machine learning, but th...

0 Simon C. Marshall, et al. ∙

research

∙ 10/25/2021

Quantum machine learning beyond kernel methods

With noisy intermediate-scale quantum computers showing great promise fo...

0 Sofiène Jerbi, et al. ∙

research

∙ 08/02/2021

LIMDD A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States

Efficient methods for the representation of relevant quantum states and ...

0 Lieuwe Vinkhuijzen, et al. ∙

research

∙ 05/12/2021

Structural risk minimization for quantum linear classifiers

Quantum machine learning (QML) stands out as one of the typically highli...

0 Casper Gyurik, et al. ∙

research

∙ 03/30/2021

Reinforcement learning for optimization of variational quantum circuit architectures

The study of Variational Quantum Eigensolvers (VQEs) has been in the spo...

0 Mateusz Ostaszewski, et al. ∙

research

∙ 03/09/2021

Variational quantum policies for reinforcement learning

Variational quantum circuits have recently gained popularity as quantum ...

15 Sofiène Jerbi, et al. ∙

research

∙ 07/14/2020

Hybrid divide-and-conquer approach for tree search algorithms

As we are entering the era of real-world small quantum computers, findin...

0 Mathys Rennela, et al. ∙

research

∙ 05/06/2020

Towards quantum advantage for topological data analysis

A particularly promising line of quantum machine leaning (QML) algorithm...

0 Casper Gyurik, et al. ∙

research

∙ 10/28/2019

A framework for deep energy-based reinforcement learning with quantum speed-up

In the past decade, deep learning methods have seen tremendous success i...

0 Sofiène Jerbi, et al. ∙

research

∙ 10/25/2019

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

In recent years, the interest in leveraging quantum effects for enhancin...

0 Jens Clausen, et al. ∙

research

∙ 07/02/2019

A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles

Recent works have shown that quantum computers can polynomially speed up...

0 Yimin Ge, et al. ∙

research

∙ 12/20/2018

Optimizing Quantum Error Correction Codes with Reinforcement Learning

Quantum error correction is widely thought to be the key to fault-tolera...

0 Hendrik Poulsen Nautrup, et al. ∙

research

∙ 07/24/2018

Computational speedups using small quantum devices

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

0 Vedran Dunjko, et al. ∙

research

∙ 04/01/2018

A note on state preparation for quantum machine learning

The intersection between the fields of machine learning and quantum info...

0 Zhikuan Zhao, et al. ∙

research

∙ 10/30/2017

Super-polynomial and exponential improvements for quantum-enhanced reinforcement learning

Recent work on quantum machine learning has demonstrated that quantum co...

0 Vedran Dunjko, et al. ∙

research

∙ 09/08/2017

Machine learning & artificial intelligence in the quantum domain

Quantum information technologies, and intelligent learning systems, are ...

0 Vedran Dunjko, et al. ∙

research

∙ 09/05/2017

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

We report a proof-of-principle experimental demonstration of the quantum...

0 Theeraphot Sriarunothai, et al. ∙

research

∙ 06/02/2017

Active learning machine learns to create new quantum experiments

How useful can machine learning be in a quantum laboratory? Here we rais...

0 Alexey A. Melnikov, et al. ∙

research

∙ 10/26/2016

Quantum-enhanced machine learning

The emerging field of quantum machine learning has the potential to subs...

0 Vedran Dunjko, et al. ∙

research

∙ 02/25/2016

Meta-learning within Projective Simulation

Learning models of artificial intelligence can nowadays perform very wel...

0 Adi Makmal, et al. ∙

research

∙ 07/30/2015

Framework for learning agents in quantum environments

In this paper we provide a broad framework for describing learning agent...

0 Vedran Dunjko, et al. ∙

research

∙ 04/09/2015

Projective simulation with generalization

The ability to generalize is an important feature of any intelligent age...

0 Alexey A. Melnikov, et al. ∙

research

∙ 03/04/2015

Sequential quantum mixing for slowly evolving sequences of Markov chains

In this work we consider the problem of preparation of the stationary di...

0 Vedran Dunjko, et al. ∙

Vedran Dunjko

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