activity
20052021
most citedWhat is the Computational Value of Finite Range Tunneling?

267 citations · 705 across the 11 of their papers we have counts for

collaborators
Showing quant-phShow all

7 papers · 1 filter

quant-ph2018★ 10 cited

Efficient population transfer via non-ergodic extended states in quantum spin glass

Kostyantyn Kechedzhi, Vadim Smelyanskiy, Jarrod R. McClean +6

We analyze a new computational role of coherent multi-qubit quantum tunneling that gives rise to bands of non-ergodic extended (NEE) quantum states each formed by a superposition o…

quant-ph2016★ 267 cited

What is the Computational Value of Finite Range Tunneling?

Vasil S. Denchev, Sergio Boixo, Sergei V. Isakov +5

Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable…

quant-ph2015★ 236 cited

Computational Role of Multiqubit Tunneling in a Quantum Annealer

Sergio Boixo, Vadim N. Smelyanskiy, Alireza Shabani +7

Quantum tunneling, a phenomenon in which a quantum state traverses energy barriers above the energy of the state itself, has been hypothesized as an advantageous physical resource…

quant-ph2014★ 23 cited

Computational Role of Collective Tunneling in a Quantum Annealer

Sergio Boixo, Vadim N. Smelyanskiy, Alireza Shabani +7

Quantum tunneling is a phenomenon in which a quantum state traverses energy barriers above the energy of the state itself. Tunneling has been hypothesized as an advantageous physic…

quant-ph2012★ 24 cited

Robust Classification with Adiabatic Quantum Optimization

Vasil S. Denchev, Nan Ding, S. V. N. Vishwanathan +1

We propose a non-convex training objective for robust binary classification of data sets in which label noise is present. The design is guided by the intention of solving the resul…

quant-ph2009★ 59 cited

Training a Large Scale Classifier with the Quantum Adiabatic Algorithm

Hartmut Neven, Vasil S. Denchev, Geordie Rose +1

In a previous publication we proposed discrete global optimization as a method to train a strong binary classifier constructed as a thresholded sum over weak classifiers. Our motiv…