1 code implementation • 28 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.
no code implementations • 14 Apr 2023 • Evgenii Egorov, Roberto Bondesan, Max Welling
Quantum error correction is a critical component for scaling up quantum computing.
2 code implementations • 17 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.
no code implementations • 18 Jul 2022 • Changyong Oh, Roberto Bondesan, Dana Kianfar, Rehan Ahmed, Rishubh Khurana, Payal Agarwal, Romain Lepert, Mysore Sriram, Max Welling
Macro placement is the problem of placing memory blocks on a chip canvas.
no code implementations • 13 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.
1 code implementation • 1 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.
no code implementations • 4 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.
no code implementations • 6 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.
1 code implementation • 18 Jun 2021 • Kirill Neklyudov, Roberto Bondesan, Max Welling
Deterministic dynamics is an essential part of many MCMC algorithms, e. g.
no code implementations • 8 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.
1 code implementation • 26 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.
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.
no code implementations • 21 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.
no code implementations • 11 Jun 2019 • Roberto Bondesan, Austen Lamacraft
The solution of problems in physics is often facilitated by a change of variables.