19 citations · 28 across the 12 of their papers we have counts for
10 papers · 1 filter
An efficient algorithm for approximate shadow Hamiltonian simulation
Abhijit Chakraborty, Bharath Sambasivam, Karunya Shirali +4
We propose an efficient algorithm based on shadow Hamiltonian simulation to approximately simulate the real-time dynamics of observables under time-independent Hamiltonians. Shadow…
Q3SAT-GPT: A Generative Model for Discovering Quantum Circuits for the 3-SAT Problem
Pratim Ugale, Ilya Tyagin, Karunya Shirali +2
This work introduces Q3SAT-GPT, a generative model for discovering quantum circuits for the Max-E3-SAT problem. Our method learns from high-performing QAOA-style ansätze to directl…
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…
Floquet-ADAPT-VQE: A Quantum Algorithm to Simulate Non-Equilibrium Physics in Periodically Driven Systems
Abhishek Kumar, Karunya Shirali, Nicholas J. Mayhall +2
Periodically driven quantum systems exhibit many fascinating phenomena absent in equilibrium systems, but their simulation is more challenging than that of static systems. Conseque…
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…
Scaling adaptive quantum simulation algorithms via operator pool tiling
John S. Van Dyke, Karunya Shirali, George S. Barron +3
Adaptive variational quantum simulation algorithms use information from the quantum computer to dynamically create optimal trial wavefunctions for a given problem Hamiltonian. A ke…