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Found 14 papers, 6 papers with code
Efficient Learning of Long-Range and Equivariant Quantum Systems
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
Quantum error correction is a critical component for scaling up quantum computing.
Robust Scheduling with GFlowNets
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization.
Bayesian Optimization for Macro Placement
Macro placement is the problem of placing memory blocks on a chip canvas.
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
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.
Neural Simulated Annealing
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
We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics.
Deterministic Gibbs Sampling via Ordinary Differential Equations
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
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.
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel
In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive-to-evaluate objectives.
Quantum Deformed Neural Networks
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.
Probabilistic Numeric Convolutional Neural Networks
Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods.
Learning Symmetries of Classical Integrable Systems
The solution of problems in physics is often facilitated by a change of variables.
