This thesis looks at how we can leverage current and near future quantum computers to effectively simulate quantum field theories. The scope of this thesis covers how to simulate the (1+1)-D Transverse Ising model on digital quantum computers and the methods to handle multiple types of machine errors to effectively extract real-time dynamic information. We leverage modifications to the transverse Ising model to study the calculation of phase shifts from scattering a particle off of simple potentials and demonstrate that these calculations are feasible on current quantum hardware. In looking toward the future we demonstrate two different state preparation algorithms. One algorithm projected cooling we show can extract information about the second order phase transition in the Transverse Ising Model and has excellent scaling properties. The second algorithm involves leveraging classical field theory simulations to circumvent quantum state preparation methods on that should prove tractable on near term quantum computing hardware. These tools together highlight the promise of quantum computing to tackle interesting problems in high energy physics.This thesis looks at how we can leverage current and near future quantum computers to effectively simulate quantum field theories. The scope of this thesis covers how to simulate the (1+1)-D Transverse Ising model on digital quantum computers and the methods to handle multiple types of machine errors to effectively extract real-time dynamic information. We leverage modifications to the transverse Ising model to study the calculation of phase shifts from scattering a particle off of simple potentials and demonstrate that these calculations are feasible on current quantum hardware. In looking toward the future we demonstrate two different state preparation algorithms. One algorithm projected cooling we show can extract information about the second order phase transition in the Transverse Ising Model and has excellent scaling properties. The second algorithm involves leveraging classical field theory simulations to circumvent quantum state preparation methods on that should prove tractable on near term quantum computing hardware. These tools together highlight the promise of quantum computing to tackle interesting problems in high energy physics.

May 1, 20210 citations

Quantum Algorithm for the Transverse-Field Ising Model

Robson Giles

Jan 1, 20240 citations

Type II Quantum Computing Algorithm for Computational Fluid Dynamics

James Scoville

An algorithm is presented to simulate fluid dynamics on a three qubit type II quantum computer: a lattice of small quantum computers that communicate classical information. The algorithm presented is called a three qubit factorized quantum lattice gas algorithm. It is modeled after classical lattice gas algorithms which move virtual particles along an imaginary lattice and change the particles' momentums using collision rules when they meet at a lattice node. Instead of moving particles, the quantum algorithm presented here moves probabilities, which interact via a unitary collision operator. Probabilities are determined using ensemble measurement and are moved with classical communications channels. The lattice node spacing is defined to be a microscopic scale length. A mesoscopic governing equation for the lattice is derived for the most general three qubit collision operator which preserves particle number. In the continuum limit of the lattice, a governing macroscopic partial differential equation-the diffusion equation-is derived for a particular collision operator using a Chapman-Enskog expansion. A numerical simulation of the algorithm is carried out on a conventional desktop computer and compared to the analytic solution of the diffusion equation. The simulation agrees very well with the known solution.An algorithm is presented to simulate fluid dynamics on a three qubit type II quantum computer: a lattice of small quantum computers that communicate classical information. The algorithm presented is called a three qubit factorized quantum lattice gas algorithm. It is modeled after classical lattice gas algorithms which move virtual particles along an imaginary lattice and change the particles' momentums using collision rules when they meet at a lattice node. Instead of moving particles, the quantum algorithm presented here moves probabilities, which interact via a unitary collision operator. Probabilities are determined using ensemble measurement and are moved with classical communications channels. The lattice node spacing is defined to be a microscopic scale length. A mesoscopic governing equation for the lattice is derived for the most general three qubit collision operator which preserves particle number. In the continuum limit of the lattice, a governing macroscopic partial differential equation-the diffusion equation-is derived for a particular collision operator using a Chapman-Enskog expansion. A numerical simulation of the algorithm is carried out on a conventional desktop computer and compared to the analytic solution of the diffusion equation. The simulation agrees very well with the known solution.

Oct 3, 20120 citations

On Complexity of the Quantum Ising Model

Sergey B. Bravyi, Matthew B. Hastings

Communications in Mathematical PhysicsNov 11, 201656 citations

A fast algorithm for investigations on the three-dimensional Ising model

Michael J. Creutz, P. Mitra, Kevin J. M. Moriarty

Computer Physics CommunicationsOct 1, 198413 citations

On connection among quantum-inspired algorithms of the Ising model

Bowen Liu, Kaizhi Wang, Dongmei Xiao +1 more

Various combinatorial optimization NP-hard problems can be reduced to finding the minimizer of an Ising model, which is a discrete mathematical model. It is an intellectual challenge to develop some mathematical tools or algorithms for solving the Ising model. Over the past decades, some continuous approaches or algorithms have been proposed from physical, mathematical or computational views for optimizing the Ising model such as quantum annealing, the coherent Ising machine, simulated annealing, adiabatic Hamiltonian systems, etc.. However, the mathematical principle of these algorithms is far from being understood. In this paper, we reveal the mathematical mechanism of dynamical system algorithms for the Ising model by Morse theory and variational methods. We prove that the dynamical system algorithms can be designed to minimize a continuous function whose local minimum points give all the candidates of the Ising model and the global minimum gives the minimizer of Ising problem. Using this mathematical mechanism, we can easily understand several dynamical system algorithms of the Ising model such as the coherent Ising machine, the Kerr-nonlinear parametric oscillators and the simulated bifurcation algorithm. Furthermore, motivated by the works of C. Conley, we study transit and capture properties of the simulated bifurcation algorithm to explain its convergence by the low energy transit and capture in celestial mechanics. A detailed discussion on $2$ -spin and $3$ -spin Ising models is presented as application.Various combinatorial optimization NP-hard problems can be reduced to finding the minimizer of an Ising model, which is a discrete mathematical model. It is an intellectual challenge to develop some mathematical tools or algorithms for solving the Ising model. Over the past decades, some continuous approaches or algorithms have been proposed from physical, mathematical or computational views for optimizing the Ising model such as quantum annealing, the coherent Ising machine, simulated annealing, adiabatic Hamiltonian systems, etc.. However, the mathematical principle of these algorithms is far from being understood. In this paper, we reveal the mathematical mechanism of dynamical system algorithms for the Ising model by Morse theory and variational methods. We prove that the dynamical system algorithms can be designed to minimize a continuous function whose local minimum points give all the candidates of the Ising model and the global minimum gives the minimizer of Ising problem. Using this mathematical mechanism, we can easily understand several dynamical system algorithms of the Ising model such as the coherent Ising machine, the Kerr-nonlinear parametric oscillators and the simulated bifurcation algorithm. Furthermore, motivated by the works of C. Conley, we study transit and capture properties of the simulated bifurcation algorithm to explain its convergence by the low energy transit and capture in celestial mechanics. A detailed discussion on $2$-spin and $3$-spin Ising models is presented as application.

arXiv:2012.01156Communications in Mathematical SciencesJan 1, 20232 citations

2

-spin and

3

-spin Ising models is presented as application.