Search Results for author: Roberto Bondesan

Found 15 papers, 8 papers with code

Accurate Learning of Equivariant Quantum Systems from a Single Ground State

1 code implementation20 May 2024 Štěpán Šmíd, Roberto Bondesan

Predicting properties across system parameters is an important task in quantum physics, with applications ranging from molecular dynamics to variational quantum algorithms.

Efficient Learning of Long-Range and Equivariant Quantum Systems

1 code implementation28 Dec 2023 Štěpán Šmíd, Roberto Bondesan

For short-range gapped Hamiltonians, a sample complexity that is logarithmic in the number of qubits and quasipolynomial in the error was obtained.

The END: An Equivariant Neural Decoder for Quantum Error Correction

no code implementations14 Apr 2023 Evgenii Egorov, Roberto Bondesan, Max Welling

Quantum error correction is a critical component for scaling up quantum computing.


Robust Scheduling with GFlowNets

2 code implementations17 Jan 2023 David W. Zhang, Corrado Rainone, Markus Peschl, Roberto Bondesan

Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization.

Compiler Optimization Diversity +1

Neural Topological Ordering for Computation Graphs

no code implementations13 Jul 2022 Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Herke van Hoof, Weiliang Will Zeng, Piero Zappi, Christopher Lott, Roberto Bondesan

Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.

2k BIG-bench Machine Learning +3

Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

1 code implementation1 Jul 2022 Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, Miranda C. N. Cheng

We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem.

BIG-bench Machine Learning

Neural Simulated Annealing

1 code implementation4 Mar 2022 Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan

Simulated annealing (SA) is a stochastic global optimisation technique applicable to a wide range of discrete and continuous variable problems.

Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

no code implementations6 Oct 2021 Pim de Haan, Corrado Rainone, Miranda C. N. Cheng, Roberto Bondesan

We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics.

BIG-bench Machine Learning

Deterministic Gibbs Sampling via Ordinary Differential Equations

1 code implementation18 Jun 2021 Kirill Neklyudov, Roberto Bondesan, Max Welling

Deterministic dynamics is an essential part of many MCMC algorithms, e. g.

The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning

no code implementations8 Mar 2021 Roberto Bondesan, Max Welling

In this work we develop a quantum field theory formalism for deep learning, where input signals are encoded in Gaussian states, a generalization of Gaussian processes which encode the agent's uncertainty about the input signal.

Gaussian Processes

Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel

1 code implementation26 Feb 2021 Changyong Oh, Roberto Bondesan, Efstratios Gavves, Max Welling

In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive-to-evaluate objectives.

Bayesian Optimization Point Processes +1

Probabilistic Numeric Convolutional Neural Networks

1 code implementation ICLR 2021 Marc Finzi, Roberto Bondesan, Max Welling

Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods.

Gaussian Processes Time Series +1

Quantum Deformed Neural Networks

no code implementations21 Oct 2020 Roberto Bondesan, Max Welling

We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states.

Learning Symmetries of Classical Integrable Systems

no code implementations11 Jun 2019 Roberto Bondesan, Austen Lamacraft

The solution of problems in physics is often facilitated by a change of variables.

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