7 papers · 1 filter
Learning Cut Distributions with Quantum Optimization
Bao Bach, Cameron Ibrahim, Reuben Tate +3
Many combinatorial optimization problems admit a maximin fairness variant, where the aim is to find a distribution over possible solutions which maximizes an expected worst-case ou…
Reductions of QAOA Induced by Classical Symmetries: Theoretical Insights and Practical Implications
Boris Tsvelikhovskiy, Bao Bach, Jose Falla +1
The performance of the Quantum Approximate Optimization Algorithm (QAOA) is closely tied to the structure of the dynamical Lie algebra (DLA) generated by its Hamiltonians, which de…
Solving Large-Scale QUBO with Transferred Parameters from Multilevel QAOA of low depth
Bao G Bach, Filip B. Maciejewski, Ilya Safro
The Quantum Approximate Optimization Algorithm (QAOA) is a promising quantum approach for tackling combinatorial optimization problems. However, hardware constraints such as limite…
Provably faster randomized and quantum algorithms for -means clustering via uniform sampling
Tyler Chen, Archan Ray, Akshay Seshadri +6
The -means algorithm (Lloyd's algorithm) is a widely used method for clustering unlabeled data. A key bottleneck of the -means algorithm is that each iteration requires time…
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach
Kien X. Nguyen, Bao Bach, Ilya Safro
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates to achieve the quantum advantage in solving combinatorial optimization problems. The proce…
QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa +2
In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuit…