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.
Barren plateaus in quantum neural network training landscapes
- J. McClean, S. Boixo, V. Smelyanskiy, R. Babbush, H. Neven
- Computer Science, PhysicsNature Communications
- 29 March 2018
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.
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 of…
Bayesian Sampling Using Stochastic Gradient Thermostats
- Nan Ding, Youhan Fang, R. Babbush, Changyou Chen, R. Skeel, H. Neven
- Computer Science, MathematicsNIPS
- 8 December 2014
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.
PhotoOCR: Reading Text in Uncontrolled Conditions
- A. Bissacco, M. Cummins, Yuval Netzer, H. Neven
- Computer ScienceIEEE International Conference on Computer Vision
- 1 December 2013
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.
Large-Scale Object Classification Using Label Relation Graphs
- Jia Deng, Nan Ding, Hartwig Adam
- Computer ScienceEuropean Conference on Computer Vision
- 6 September 2014
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.
Tour the world: Building a web-scale landmark recognition engine
- Yantao Zheng, Ming Zhao, H. Neven
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 1 June 2009
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.
Classification with Quantum Neural Networks on Near Term Processors
This work introduces a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning, and shows through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets.
What is the Computational Value of Finite Range Tunneling
It is demonstrated how finite range tunneling can provide considerable computational advantage over classical processors for a crafted problem designed to have tall and narrow energy barriers separating local minima, the D-Wave 2X quantum annealer achieves significant runtime advantages relative to Simulated Annealing.
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
Compiling to surface code fault-tolerant gates and assuming per gate error rates of one part in a thousand reveals that one can error correct phase estimation on interesting instances of these problems beyond the current capabilities of classical methods.