10 papers
Partition Function Estimation Using Analog Quantum Processors
Thinh Le, Elijah Pelofske
We evaluate using programmable superconducting flux qubit D-Wave quantum annealers to approximate the partition function of Ising models. We propose the use of two distinct quantum…
Depth One Quantum Alternating Operator Ansatz as an Approximate Gibbs Distribution Sampler
Elijah Pelofske
This study numerically investigates the thermal sampling properties of QAOA, the Quantum Alternating Operator Ansatz which was generalized from the original Quantum Approximate Opt…
Erasing Classical Memory with Quantum Fluctuations: Shannon Information Entropy of Reverse Quantum Annealing
Elijah Pelofske, Cristiano Nisoli
Quantum annealers can provide non-local optimization by tunneling between states in a process that ideally eliminates memory of the initial configuration. We study the crossover be…
Magnetic Memory and Hysteresis from Quantum Transitions: Theory and Experiments on Quantum Annealers
Frank Barrows, Elijah Pelofske, Pratik Sathe +2
Quantum annealing leverages quantum tunneling for non-local searches, thereby minimizing memory effects that typically arise from metastabilities. Nonetheless, recent work has demo…
Variational Quantum Simulations of a Two-Dimensional Frustrated Transverse-Field Ising Model on a Trapped-Ion Quantum Computer
Ammar Kirmani, Elijah Pelofske, Andreas Bärtschi +2
Quantum computers are an ideal platform to study the ground state properties of strongly correlated systems due to the limitation of classical computing techniques particularly for…
Quantum Approximate Multi-Objective Optimization
Ayse Kotil, Elijah Pelofske, Stephanie Riedmüller +4
The goal of multi-objective optimization is to understand optimal trade-offs between competing objective functions by finding the Pareto front, i.e., the set of all Pareto optimal…