6 citations · 6 across the 6 of their papers we have counts for
6 papers
Phase-Selective Amplitude Amplification for Constrained Optimization
Massimiliano Cutugno
This work introduces a variant of Grover amplitude amplification using stabilizer and blade qubits to improve boosting robustness across objective distributions. We give geometric…
Variational Amplitude Amplification for Solving QUBO Problems
Daniel Koch, Massimiliano Cutugno, Saahil Patel +2
We investigate the use of amplitude amplification on the gate-based model of quantum computing as a means for solving combinatorial optimization problems. This study focuses primar…
Physics Simulation Via Quantum Graph Neural Network
Benjamin Collis, Saahil Patel, Daniel Koch +3
We develop and implement two realizations of quantum graph neural networks (QGNN), applied to the task of particle interaction simulation. The first QGNN is a speculative quantum-c…
Quantum Computing Approaches for Mission Covering Optimization
Massimiliano Cutugno, Annarita Giani, Paul M. Alsing +2
We study quantum computing algorithms for solving certain constrained resource allocation problems we coin as Mission Covering Optimization (MCO). We compare formulations of constr…
Information loss and run time from practical application of quantum data compression
Saahil Patel, Benjamin Collis, William Duong +4
We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of…
Gaussian Amplitude Amplification for Quantum Pathfinding
Daniel Koch, Massimiliano Cutugno, Samuel Karlson +3
We study an oracle operation, along with its circuit design, which combined with the Grover diffusion operator boosts the probability of finding minimum or maximum solutions on a w…