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Quantum supremacy using a programmable superconducting processor
Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute. Expand
PhotoOCR: Reading Text in Uncontrolled Conditions
This work describes Photo OCR, a system for text extraction from images that is capable of recognizing text in a variety of challenging imaging conditions where traditional OCR systems fail, notably in the presence of substantial blur, low resolution, low contrast, high image noise and other distortions. Expand
Bayesian Sampling Using Stochastic Gradient Thermostats
This work shows that one can leverage a small number of additional variables to stabilize momentum fluctuations induced by the unknown noise inynamics-based sampling methods. Expand
Characterizing quantum supremacy in near-term devices
A critical question for quantum computing in the near future is whether quantum devices without error correction can perform a well-defined computational task beyond the capabilities ofExpand
Large-Scale Object Classification Using Label Relation Graphs
A new model that allows encoding of flexible relations between labels is developed that can significantly improve object classification by exploiting the label relations and a probabilistic classification model based on HEX graphs is proposed. Expand
Tour the world: Building a web-scale landmark recognition engine
This paper leverages the vast amount of multimedia data on the Web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address issues of modeling and recognizing landmarks at world-scale. Expand
Scalable Quantum Simulation of Molecular Energies
We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qubits to compute theExpand
Classification with Quantum Neural Networks on Near Term Processors
A quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning, is introduced and it is shown through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. Expand
Barren plateaus in quantum neural network training landscapes
It is shown that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. Expand
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
We construct quantum circuits which exactly encode the spectra of correlated electron models up to errors from rotation synthesis. By invoking these circuits as oracles within the recently introducedExpand