4.7k citations · 16.5k across the 115 of their papers we have counts for
9 papers · 2 filters
Tomography and Generative Data Modeling via Quantum Boltzmann Training
Maria Kieferova, Nathan Wiebe
The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quant…
Design Automation and Design Space Exploration for Quantum Computers
Mathias Soeken, Martin Roetteler, Nathan Wiebe +1
A major hurdle to the deployment of quantum linear systems algorithms and recent quantum simulation algorithms lies in the difficulty to find inexpensive reversible circuits for ar…
Structured Filtering
Christopher Granade, Nathan Wiebe
A major challenge facing existing sequential Monte-Carlo methods for parameter estimation in physics stems from the inability of existing approaches to robustly deal with experimen…
Quantum Machine Learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti +3
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce co…
Witnessing eigenstates for quantum simulation of Hamiltonian spectra
R Santagati, J Wang, A A Gentile +11
The efficient calculation of Hamiltonian spectra, a problem often intractable on classical machines, can find application in many fields, from physics to chemistry. Here, we introd…
Bounding the costs of quantum simulation of many-body physics in real space
Ian D. Kivlichan, Nathan Wiebe, Ryan Babbush +1
We present a quantum algorithm for simulating the dynamics of a first-quantized Hamiltonian in real space based on the truncated Taylor series algorithm. We avoid the possibility o…