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Quantum Deep Learning
Nathan Wiebe, Ashish Kapoor, Krysta M. Svore
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to effi…
Chemical Basis of Trotter-Suzuki Errors in Quantum Chemistry Simulation
Ryan Babbush, Jarrod McClean, Dave Wecker +2
Although the simulation of quantum chemistry is one of the most anticipated applications of quantum computing, the scaling of known upper bounds on the complexity of these algorith…
Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning
Nathan Wiebe, Christopher Granade, David G. Cory
Recent work has shown that quantum simulation is a valuable tool for learning empirical models for quantum systems. We build upon these results by showing that a small quantum simu…
The Trotter Step Size Required for Accurate Quantum Simulation of Quantum Chemistry
David Poulin, M. B. Hastings, Dave Wecker +3
The simulation of molecules is a widely anticipated application of quantum computers. However, recent studies \cite{WBCH13a,HWBT14a} have cast a shadow on this hope by revealing th…
Quantum arithmetic and numerical analysis using Repeat-Until-Success circuits
Nathan Wiebe, Martin Roetteler
We develop a method for approximate synthesis of single--qubit rotations of the form that is based on the Repeat-Until-Success (RUS) framework for quant…
On The Power Of Coherently Controlled Quantum Adiabatic Evolutions
Maria Kieferova, Nathan Wiebe
A major challenge facing adiabatic quantum computing is that algorithm design and error correction can be difficult for adiabatic quantum computing. Recent work has considered addr…