Papers
Live trends in quantum computing research, updated daily from arXiv.
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13,007 papers in 12 months (-3% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Machine learning of frustrated classical spin models. I. Principal component analysis
Ce Wang, H. Zhai·Jun 24, 2017
This work aims at the goal whether the artificial intelligence can recognize phase transition without the prior human knowledge. If this becomes successful, it can be applied to, for instance, analyze data from quantum simulation of unsolved physical...
Machine-learning-assisted correction of correlated qubit errors in a topological code
P. Baireuther, T. O’Brien, B. Tarasinski +1 more·May 22, 2017
A fault-tolerant quantum computation requires an efficient means to detect and correct errors that accumulate in encoded quantum information. In the context of machine learning, neural networks are a promising new approach to quantum error correction...
Modeling observers as physical systems representing the world from within: Quantum theory as a physical and self-referential theory of inference
John Realpe-G'omez·May 11, 2017
In 1929 Szilard pointed out that the physics of the observer may play a role in the analysis of experiments. The same year, Bohr pointed out that complementarity appears to arise naturally in psychology where both the objects of perception and the pe...
Decoding small surface codes with feedforward neural networks
Savvas Varsamopoulos, B. Criger, K. Bertels·May 2, 2017
Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implementations. To decrease the decoding time, we reduce the decoding problem ...
Robustness of quantum neural calculation increases with system size
N. Nguyen, E. Behrman, J. Steck·Dec 22, 2016
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quant...
Quantum Neural Machine Learning - Backpropagation and Dynamics
C. Gonçalves·Sep 22, 2016
The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks' processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and t...
Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
Moritz August, Xiaotong Ni·Apr 1, 2016
We utilize machine learning models which are based on recurrent neural networks to optimize dynamical decoupling (DD) sequences. DD is a relatively simple technique for suppressing the errors in quantum memory for certain noise models. In numerical s...
Harnessing disordered quantum dynamics for machine learning
K. Fujii, K. Nakajima·Feb 26, 2016
Quantum computer has an amazing potential of fast information processing. However, realisation of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a novel pla...