Search Results for author: Rupak Biswas

Found 6 papers, 3 papers with code

Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation

1 code implementation1 May 2019 Benjamin Villalonga, Dmitry Lyakh, Sergio Boixo, Hartmut Neven, Travis S. Humble, Rupak Biswas, Eleanor G. Rieffel, Alan Ho, Salvatore Mandrà

Noisy Intermediate-Scale Quantum (NISQ) computers aim to perform computational tasks beyond the capabilities of the most powerful classical computers, thereby achieving "Quantum Supremacy", a major milestone in quantum computing.

Quantum Physics Computational Complexity Computational Physics

From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz

no code implementations11 Sep 2017 Stuart Hadfield, Zhihui Wang, Bryan O'Gorman, Eleanor G. Rieffel, Davide Venturelli, Rupak Biswas

The essence of this extension, the Quantum Alternating Operator Ansatz, is the consideration of general parametrized families of unitaries rather than only those corresponding to the time-evolution under a fixed local Hamiltonian for a time specified by the parameter.

Quantum Physics

Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

no code implementations31 Aug 2017 Alejandro Perdomo-Ortiz, Marcello Benedetti, John Realpe-Gómez, Rupak Biswas

We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques.

Quantum Physics Emerging Technologies

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

no code implementations8 Sep 2016 Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, Alejandro Perdomo-Ortiz

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.

Benchmarking BIG-bench Machine Learning +1

A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems

2 code implementations30 Jun 2014 Alejandro Perdomo-Ortiz, Joseph Fluegemann, Sriram Narasimhan, Rupak Biswas, Vadim N. Smelyanskiy

Diagnosing the minimal set of faults capable of explaining a set of given observations, e. g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques.

Quantum Physics

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