181 citations · 203 across the 10 of their papers we have counts for
10 papers
Quantum Data Learning of Topological-to-Ferromagnetic Phase Transitions in the 2+1D Toric Code Loop Gas Model
Shamminuj Aktar, Rishabh Bhardwaj, Andreas Bärtschi +2
Quantum data learning (QDL) provides a framework for extracting physical insights directly from quantum states, bypassing the need for any identification of the classical observabl…
Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms
Elijah Pelofske, Marek Rams, Andreas Bärtschi +4
The emergent practical applicability of the Quantum Approximate Optimization Algorithm (QAOA) for approximate combinatorial optimization is a subject of considerable interest. One…
Variational Quantum Simulations of a Two-Dimensional Frustrated Transverse-Field Ising Model on a Trapped-Ion Quantum Computer
Ammar Kirmani, Elijah Pelofske, Andreas Bärtschi +2
Quantum computers are an ideal platform to study the ground state properties of strongly correlated systems due to the limitation of classical computing techniques particularly for…
Snapshot-QAOA: Extending QAOA to Quantum Hamiltonian Simulation
Reuben Tate, Quinn Langfitt, Elijah Pelofske +4
We present Snapshot-QAOA, a variation of the Quantum Approximate Optimization Algorithm (QAOA) that finds approximate minimum energy eigenstates of a large set of quantum Hamiltoni…
Quantum Approximate Optimization: A Computational Intelligence Perspective
Christo Meriwether Keller, Satyajayant Misra, Andreas Bärtschi +1
Quantum computing is an emerging field on the multidisciplinary interface between physics, engineering, and computer science with the potential to make a large impact on computatio…
Potential Applications of Quantum Computing at Los Alamos National Laboratory
Andreas Bärtschi, Francesco Caravelli, Carleton Coffrin +16
The emergence of quantum computing technology over the last decade indicates the potential for a transformational impact in the study of quantum mechanical systems. It is natural t…
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Shamminuj Aktar, Andreas Bärtschi, Diane Oyen +2
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs…
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…
Simulating Heavy-Hex Transverse Field Ising Model Magnetization Dynamics Using Programmable Quantum Annealers
Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz
Recently, a Hamiltonian dynamics simulation was performed on a kicked ferromagnetic 2D transverse field Ising model with a connectivity graph native to the 127 qubit heavy-hex IBM…
Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing
Zachary Morrell, Marc Vuffray, Andrey Lokhov +3
We propose a quantum annealing protocol that more effectively probes the dynamics of a single qubit on D-Wave's quantum annealing hardware. This protocol uses D-Wave's h-gain sched…