Adiabatic Quantum Computation

TL;DR

This paper extends the quantum adiabatic theorem to degenerate ground states and investigates the efficiency of adiabatic quantum computation through generalized Hamiltonian constructions based on spectral graph theory.

Contribution

It generalizes Kitaev's Hamiltonian to new graph families, analyzing trade-offs between spectral gap, running time, and output probability in adiabatic quantum algorithms.

Findings

Spectral gap influences the efficiency of quantum circuit simulation.

Trade-offs exist between spectral gap, initial/final vertex fractions, and success probability.

Certain graph structures likely hinder improvements in adiabatic quantum computation efficiency.

Abstract

The quantum adiabatic theorem ensures that a slowly changing system, initially prepared in its ground state, will evolve to its final ground state with arbitrary precision. As a first result this thesis extends the original theorem to projection operators keeping the statement valid for Hamiltonians with degenerate ground spaces. Yet the main focus of this work lies in studying the efficiency of quantum circuit simulations by adabatic quantum computation. The standard Hamiltonian construction by Kitaev is based on a path graph reflecting the $L$ computation steps and influencing the scaling of the necessary evolution time by its spectral gap of $\mathcal{O}\left(\frac{1}{L^2}\right)$. Aspiring to an improved running time we generalize Kitaev's Hamiltonian to so-called standard graph Hamiltonians based on graph families with a different spectral gap. In this generalized construction the first two time derivatives of the Hamiltonian and the fraction of initial vertices appear as additional parameters of running time. In a first step the time derivatives can be proven to be constant. Expansion results from spectral graph theory however impose a trade-off between the spectral gap and the fraction of initial vertices as well as the fraction of final vertices which corresponds to the probability for obtaining the correct computational output. Graphs with spectral gap $\mathcal{O}\left(\frac{1}{L^k}\right)$, $k<2$, turn out to contradict very likely, graphs with $k<1$ even for sure at least one of the efficiency criterias for running time, output probability or Hamiltonian implementation. The above results may also be obstacles for a possible quantum PCP-theorem in complexity theory claiming the local Hamiltonian problem with constant gap to be QMA-complete since the very same Kitaev Hamiltonian is constructed in the QMA-hardness proof for the Local Hamiltonian problem.