Measurement-based quantum computation from Clifford quantum cellular automata

1 code implementation • 20 Dec 2023 • Hendrik Poulsen Nautrup, Hans J. Briegel

Measurement-based quantum computation (MBQC) is a paradigm for quantum computation where computation is driven by local measurements on a suitably entangled resource state.

Variational measurement-based quantum computation for generative modeling

no code implementations • 20 Oct 2023 • Arunava Majumder, Marius Krumm, Tina Radkohl, Hendrik Poulsen Nautrup, Sofiene Jerbi, Hans J. Briegel

Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms.

Automated Gadget Discovery in Science

2 code implementations • 24 Dec 2022 • Lea M. Trenkwalder, Andrea López Incera, Hendrik Poulsen Nautrup, Fulvio Flamini, Hans J. Briegel

This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm.

Clustering Reinforcement Learning (RL) +1

Quantum machine learning beyond kernel methods

1 code implementation • 25 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

Operationally meaningful representations of physical systems in neural networks

2 code implementations • 2 Jan 2020 • Hendrik Poulsen Nautrup, Tony Metger, Raban Iten, Sofiene Jerbi, Lea M. Trenkwalder, Henrik Wilming, Hans J. Briegel, Renato Renner

To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems.

Reinforcement Learning Representation Learning

Quantum enhancements for deep reinforcement learning in large spaces

1 code implementation • 28 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 +4

Photonic architecture for reinforcement learning

no code implementations • 17 Jul 2019 • Fulvio Flamini, Arne Hamann, Sofiène Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Hans J. Briegel

The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices.

Active Learning Q-Learning +3

Optimizing Quantum Error Correction Codes with Reinforcement Learning

no code implementations • 20 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 +2

Active learning machine learns to create new quantum experiments

no code implementations • 2 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