22 citations · 54 across the 5 of their papers we have counts for
8 papers · 1 filter
Trainability Barriers in Low-Depth QAOA Landscapes
Joel Rajakumar, John Golden, Andreas Bärtschi +1
The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum algorithm for solving combinatorial optimization problems. Its effectiveness depends on identifyin…
Hierarchical Multigrid Ansatz for Variational Quantum Algorithms
Christo Meriwether Keller, Stephan Eidenbenz, Andreas Bärtschi +3
Quantum computing is an emerging topic in engineering that promises to enhance supercomputing using fundamental physics. In the near term, the best candidate algorithms for achievi…
Scaling Whole-Chip QAOA for Higher-Order Ising Spin Glass Models on Heavy-Hex Graphs
Elijah Pelofske, Andreas Bärtschi, Lukasz Cincio +2
We show through numerical simulation that the Quantum Approximate Optimization Algorithm (QAOA) for higher-order, random-coefficient, heavy-hex compatible spin glass Ising models h…
Provable bounds for noise-free expectation values computed from noisy samples
Samantha V. Barron, Daniel J. Egger, Elijah Pelofske +4
In this paper, we explore the impact of noise on quantum computing, particularly focusing on the challenges when sampling bit strings from noisy quantum computers as well as the im…
Probing Quantum Telecloning on Superconducting Quantum Processors
Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz +2
Quantum information can not be perfectly cloned, but approximate copies of quantum information can be generated. Quantum telecloning combines approximate quantum cloning, more typi…
Lower bounds on the number of rounds of the quantum approximate optimization algorithm required for guaranteed approximation ratios
Naphan Benchasattabuse, Andreas Bärtschi, Luis Pedro García-Pintos +3
The quantum approximate optimization algorithm, also known in its generalization as the quantum alternating operator ansatz, (QAOA) is a heuristic hybrid quantum-classical algorith…