40 citations · 68 across the 5 of their papers we have counts for
6 papers
Exponential learning advantages with conjugate states and minimal quantum memory
Robbie King, Kianna Wan, Jarrod McClean
The ability of quantum computers to directly manipulate and analyze quantum states stored in quantum memory allows them to learn about aspects of our physical world that would othe…
An SU(2)-symmetric Semidefinite Programming Hierarchy for Quantum Max Cut
Jun Takahashi, Chaithanya Rayudu, Cunlu Zhou +3
Understanding and approximating extremal energy states of local Hamiltonians is a central problem in quantum physics and complexity theory. Recent work has focused on developing ap…
On quantum backpropagation, information reuse, and cheating measurement collapse
Amira Abbas, Robbie King, Hsin-Yuan Huang +4
The success of modern deep learning hinges on the ability to train neural networks at scale. Through clever reuse of intermediate information, backpropagation facilitates training…
Analyzing Prospects for Quantum Advantage in Topological Data Analysis
Dominic W. Berry, Yuan Su, Casper Gyurik +7
Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, ana…
An Improved Approximation Algorithm for Quantum Max-Cut
Robbie King
We give an approximation algorithm for Quantum Max-Cut which works by rounding an SDP relaxation to an entangled quantum state. The SDP is used to choose the parameters of a variat…
Stoquastic ground states are classical thermal distributions
Robbie King, Sergii Strelchuk
We study the structure of the ground states of local stoquastic Hamiltonians and show that under mild assumptions the following distributions can efficiently approximate one anothe…