30 citations · 67 across the 11 of their papers we have counts for
11 papers
TIMES-ADAPT: A Quantum algorithm for real-time evolution in low-energy subspaces using fixed-depth circuits
Bharath Sambasivam, Kyle Sherbert, Karunya Shirali +3
We propose a new variational quantum algorithm, which we refer to as TIMES-ADAPT, that prepares time-evolved states in a low-energy or symmetric subspace of a time-independent Hami…
To break, or not to break: Symmetries in adaptive quantum simulations, a case study on the Schwinger model
Karunya Shailesh Shirali, Kyle Sherbert, Yanzhu Chen +4
We investigate the role of symmetries in constructing resource-efficient operator pools for adaptive variational quantum eigensolvers. In particular, we focus on the lattice Schwin…
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…
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…
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…
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…
Parameterization and optimizability of pulse-level VQEs
Kyle M Sherbert, Hisham Amer, Sophia E Economou +2
In conventional variational quantum eigensolvers (VQEs), trial states are prepared by applying series of parameterized gates to a reference state, with the gate parameters being va…
Orthogonal-ansatz VQE: Locating excited states without modifying a cost-function
Kyle Sherbert, Marco Buongiorno Nardelli
Most literature in the Variational Quantum Eigensolver (VQE) algorithm focuses on finding the ground state of a physical system, by minimizing a quantum-computed cost-function. Whe…
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…
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…
Quantum computation of silicon electronic band structure
Frank T. Cerasoli, Kyle Sherbert, Jagoda Sławińska +1
Development of quantum architectures during the last decade has inspired hybrid classical-quantum algorithms in physics and quantum chemistry that promise simulations of fermionic…