4 citations · 5 across the 2 of their papers we have counts for
4 papers · 1 filter
Quantum Adversarial Learning in Emulation of Monte-Carlo Methods for Max-cut Approximation: QAOA is not optimal
Cem M. Unsal, Lucas T. Brady
One of the leading candidates for near-term quantum advantage is the class of Variational Quantum Algorithms, but these algorithms suffer from classical difficulty in optimizing th…
Behavior of Analog Quantum Algorithms
Lucas T. Brady, Lucas Kocia, Przemyslaw Bienias +3
Analog quantum algorithms are formulated in terms of Hamiltonians rather than unitary gates and include quantum adiabatic computing, quantum annealing, and the quantum approximate…
Optimal Protocols in Quantum Annealing and QAOA Problems
Lucas T. Brady, Christopher L. Baldwin, Aniruddha Bapat +2
Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) are two special cases of the following control problem: apply a combination of two Hamiltonians to…
Quantum Approximate Optimization of the Long-Range Ising Model with a Trapped-Ion Quantum Simulator
G. Pagano, A. Bapat, P. Becker +13
Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving perfor…