Publications (10)
QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits
Ilya Tyagin, Marwa H. Farag, Kyle Sherbert +3
Quantum computing has the potential to improve our ability to solve certain optimization problems that are computationally difficult for classical computers, by offering new algori…
Adaptive Quantum Generative Training using an Unbounded Loss Function
Kyle Sherbert, Jim Furches, Karunya Shirali +2
We propose a generative quantum learning algorithm, Rényi-ADAPT, using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be…
A systematic variational approach to band theory in a quantum computer
Kyle Sherbert, Frank Cerasoli, Marco Buongiorno Nardelli
Quantum computers promise to revolutionize our ability to simulate molecules, and cloud-based hardware is becoming increasingly accessible to a wide body of researchers. Algorithms…
TEPID-ADAPT: Adaptive variational method for simultaneous preparation of low-temperature Gibbs and low-lying eigenstates
Bharath Sambasivam, Kyle Sherbert, Karunya Shirali +4
Preparing Gibbs states, which describe systems in equilibrium at finite temperature, is of great importance, particularly at low temperatures. In this work, we propose a new method…
Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
Kyle Sherbert, Naveed Naimipour, Haleh Safavi +2
Compressive sensing is a sensing protocol that facilitates reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, t…
Surrogate Constructed Scalable Circuits ADAPT-VQE in the Schwinger model
Erik Gustafson, Kyle Sherbert, Adrien Florio +8
Inspired by recent advancements of simulating periodic systems on quantum computers, we develop a new approach, (SC)-ADAPT-VQE, to further advance the simulation of these syste…