1.1k citations · 1.5k across the 6 of their papers we have counts for
6 papers
Solving non-native combinatorial optimization problems using hybrid quantum-classical algorithms
Jonathan Wurtz, Stefan Sack, Sheng-Tao Wang
Combinatorial optimization is a challenging problem applicable in a wide range of fields from logistics to finance. Recently, quantum computing has been used to attempt to solve th…
Large-scale quantum approximate optimization on non-planar graphs with machine learning noise mitigation
Stefan H. Sack, Daniel J. Egger
Quantum computers are increasing in size and quality, but are still very noisy. Error mitigation extends the size of the quantum circuits that noisy devices can meaningfully execut…
Recursive greedy initialization of the quantum approximate optimization algorithm with guaranteed improvement
Stefan H. Sack, Raimel A. Medina, Richard Kueng +1
The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm, where a quantum computer implements a variational ansatz consisting of layers of alter…
Avoiding barren plateaus using classical shadows
Stefan H. Sack, Raimel A. Medina, Alexios A. Michailidis +2
Variational quantum algorithms are promising algorithms for achieving quantum advantage on near-term devices. The quantum hardware is used to implement a variational wave function…
Quantum annealing initialization of the quantum approximate optimization algorithm
Stefan H. Sack, Maksym Serbyn
The quantum approximate optimization algorithm (QAOA) is a prospective near-term quantum algorithm due to its modest circuit depth and promising benchmarks. However, an external pa…
Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack +1
Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regard…