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Found 8 papers, 3 papers with code
A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction
Neural decoders for quantum error correction (QEC) rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to protect logical information against errors.
On the Computational Cost of Stochastic Security
We investigate whether long-run persistent chain Monte Carlo simulation of Langevin dynamics improves the quality of the representations achieved by energy-based models (EBM).
Neural Error Mitigation of Near-Term Quantum Simulations
Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science.
Quantum Algorithms for Structured Prediction
We introduce two quantum algorithms for solving structured prediction problems.
Deep neural decoders for near term fault-tolerant experiments
Our methods require no knowledge of the underlying noise model afflicting the quantum device making them appealing for real-world experiments.
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines
The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
Free energy-based reinforcement learning using a quantum processor
The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
Reinforcement Learning Using Quantum Boltzmann Machines
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks.
