22 citations · 54 across the 5 of their papers we have counts for
5 papers
Classical Combinatorial Optimization Scaling for Random Ising Models on 2D Heavy-Hex Graphs
Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz
Motivated by near term quantum computing hardware limitations, combinatorial optimization problems that can be addressed by current quantum algorithms and noisy hardware with littl…
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…