Direct Application of the Phase Estimation Algorithm to Find the Eigenvalues of the Hamiltonians

Abstract

The eigenvalue of a Hamiltonian, $\mathcal{H}$, can be estimated through the phase estimation algorithm given the matrix exponential of the Hamiltonian, $exp(-i\mathcal{H})$. The difficulty of this exponentiation impedes the applications of the phase estimation algorithm particularly when $\mathcal{H}$ is composed of non-commuting terms. In this paper, we present a method to use the Hamiltonian matrix directly in the phase estimation algorithm by using an ancilla based framework: In this framework, we also show how to find the power of the Hamiltonian matrix-which is necessary in the phase estimation algorithm-through the successive applications. This may eliminate the necessity of matrix exponential for the phase estimation algorithm and therefore provide an efficient way to estimate the eigenvalues of particular Hamiltonians. The classical and quantum algorithmic complexities of the framework are analyzed for the Hamiltonians which can be written as a sum of simple unitary matrices and shown that a Hamiltonian of order $2^n$ written as a sum of $L$ number of simple terms can be used in the phase estimation algorithm with $(n+1+logL)$ number of qubits and $O(2^{a}nL)$ number of quantum operations, where $a$ is the number of iterations in the phase estimation. In addition, we use the Hamiltonian of the hydrogen molecule as an example system and present the simulation results for finding its ground state energy.

10 pages, 3 figures

References in corpus (4)

• Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations
• Simulating Hamiltonian dynamics with a truncated Taylor series
• Engineering of arbitrary U(N) transformations by quantum Householder reflections
• Universal Programmable Quantum Circuit Schemes to Emulate an Operator

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• Variational quantum eigensolver with embedded entanglement using a tensor-network ansatz
• A Generalized Circuit for the Hamiltonian Dynamics Through the Truncated Series
• Quantum Spectral Clustering through a Biased Phase Estimation Algorithm
• Some Error Analysis for the Quantum Phase Estimation Algorithms
• Context aware quantum simulation of a matrix stored in quantum memory