Hamiltonian simulation

Also known as Trotter formulas or Trotter-Suzuki decompositions, Product formulas simulate the sum-of-terms of a Hamiltonian by simulating each one separately for a small time slice. [4] If ${\displaystyle H=A+B+C}$, then ${\displaystyle U=e^{-i(A+B+C)t}=(e^{-iAt/r}e^{-iBt/r}e^{-iCt/r})^{r}}$ for a large ${\displaystyle r}$; where ${\displaystyle r}$ is the number of time steps to simulate for. The large the ${\displaystyle r}$, the more accurate the simulation.

If the Hamiltonian is represented as a Sparse matrix, the distributed edge coloring algorithm can be used to decompose it into a sum of terms; which can then be simulated by a Trotter-Suzuki algorithm. [5]

${\displaystyle e^{-iHt}=\sum _{n\mathop {=} 0}^{\infty }{\frac {(-iHt)^{n}}{n!}}=I-iHt-{\frac {H^{2}t^{2}}{2}}+{\frac {iH^{3}t^{3}}{6}}+...}$ by the Taylor series expansion. [6] This says that during the evolution of a quantum state, the Hamiltonian is applied over and over again to the system with a various number of repetitions. The first term is the identity matrix so the system doesn't change at first, but in the second term the Hamiltonian is applied once. For practical implementations, the series has to be truncated (${\displaystyle \sum _{n\mathop {=} 0}^{N}{\frac {(-iHt)^{n}}{n!}}}$) where the bigger the ${\displaystyle N}$, the more accurate the simulation. [7]

In the quantum walk, a unitary operation whose spectrum is related to the Hamiltonian in implemented then the Quantum phase estimation algorithm is used to adjust the eigenvalues. This makes it unnecessary to decompose the Hamiltonian into a sum-of-terms like the Trotter-Suzuki methods. [6]

The quantum signal processing algorithm works by transducing the eigenvalues of the Hamiltonian into an ancilla qubit, transforming the eigenvalues with single qubit rotations and finally projecting the ancilla. [8] It has been proved to be optimal in query complexity when it comes to Hamiltonian simulation. [8]