Barren plateaus in quantum neural network training landscapes

1 code implementation • 29 Mar 2018 • Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, Hartmut Neven

Specifically, we show 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

2 code implementations • 31 Jul 2016 • Sergio Boixo, Sergei V. Isakov, Vadim N. Smelyanskiy, Ryan Babbush, Nan Ding, Zhang Jiang, Michael J. Bremner, John M. Martinis, Hartmut Neven

We study the task of sampling from the output distributions of (pseudo-)random quantum circuits, a natural task for benchmarking quantum computers.

Quantum Physics

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

2 code implementations • 30 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

A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration

1 code implementation • 12 Apr 2012 • Vadim N. Smelyanskiy, Eleanor G. Rieffel, Sergey I. Knysh, Colin P. Williams, Mark W. Johnson, Murray C. Thom, William G. Macready, Kristen L. Pudenz

We review quantum algorithms for structured learning for multi-label classification and introduce a hybrid classical/quantum approach for learning the weights.

Quantum Physics