Foundations of noise mapping

On this page, we go through the mathematical foundations of the noise mapping performed by the HQS Noise App. Physical noise on qubits transforms into effective noise on spins in the quantum simulation. For a detailed discussion see the full paper on noise mapping.

Trotterization of time evolution operator

Decomposition blocks

Digital quantum simulation is based on trotterization of the time-evolution operator, such that:

\[ e^{-\textrm{i} H t} = \left[ e^{-\textrm{i} H t/m} \right]^m \equiv \left[ e^{-\textrm{i} H \bar t} \right]^m \approx \left[ \Pi_{j} e^{-\textrm{i} H_j \bar t} \right]^m . \]

We consider a time-independent Hamiltonian \(H\) and timesteps \(\bar t = t/m\). The total Hamiltonian \(H\) is divided into elements \(H_j\),

\[ H=\sum_j H_j , \]

which correspond to "small-angle" unitary transformations in the time-evolution, \(\exp\left(-\textrm{i} H_j\bar t \right)\).

The goal of the division is to have a sequence of unitary transformations which can be implemented efficiently on hardware. In the HQS Noise App, such divisions are marked as circuit Decomposition_Blocks. An approximation (error) occurs when individual unitaries do not commute.

Small- and large-angle decompositions

Ideally, these unitaries can be implemented directly on hardware by the onset of single-qubit transitions or multi-qubit interactions, e.g., by control-field pulses. These operations then have one-to-one correspondence with equivalent small-angle gates,

\[ e^{-\textrm{i} H_j \bar t} \rightarrow G_j . \]

As described in section modeling, in the presence of noise, each gate \(G_j\) is assigned a noise operator \(N_j\).

On the other hand, some unitaries may need to be decomposed into a series of elementary gates,

\[ e^{-\textrm{i} H_j \bar t} \rightarrow \Pi_l G^l_j . \]

Here, each gate \(G^l_j\) comes with noise \(N^l_j\). In practice, such decompositions often include "large-angle" gates, such as CNOT gates or \(\pi\) rotations.

Trotter error

The error in the simplest, first order, Trotter expansion is of size:

\[ \epsilon_\textrm{error} \propto \bar g^2 , \] where \(\bar g= g \bar t\), and \(g\) is some typical energy of non-commuting terms in the Hamiltonian. Note that the error goes to zero in the limit \(\bar t\rightarrow 0\).

Effective noise in the simulated system

Here, we detail how we map the physical noise of a quantum computer to effective noise in the simulated system. For this, it is enough to analyze unitary gates and non-unitary noise operations within one Trotter step. It also turns out that analysis of noise rotations can be done on each decomposition block separately.

Noise transformations

Let us first consider a decomposition of some unitary operation \(\exp\left(-\textrm{i} H_j\bar t \right)\) by two large-angle gates. The generalization to an arbitrary number of gates is straightforward.

By using the fact that gates \(G_i\) are unitary, and thereby invertible, we can write:

\[ N_2 G_2 N_1 G_1 \rho = N_2 G_2 N_1 G_2^{-1} G_2 G_1 \rho \equiv N_2 N_1' G_2 G_1 \rho \equiv N G \rho , \]

where \(G\) corresponds to a small-angle unitary,

\[ G = G_2 G_1 , \]

and the effective noise operator is:

\[ N = N_2 N_1' , \\ N'_1 = G_2 N_1 G_2^{-1} . \] We see that the first noise operator got transformed by the (large-angle) unitary gate \(G_2\). Importantly, since both operators, \(G\) and \(N\), describe small-angle (or probability) processes, these operators will later define the effective Lindbladian of the simulated system.

More generally, for an arbitrary decomposition we can write:

\[ e^{-\textrm{i} H_j \bar t} \rightarrow \Pi_{l=\textrm{last}}^\textrm{first} N^l_j G^l_j = N_j G_j , \]

with:

\[ G_j \equiv \Pi_{l=\textrm{last}}^\textrm{first} G^l_j, \\ N_j \equiv \prod P_j \\ P_0 = N_j^\textrm{last} \\ P_1 = G_j^\textrm{last} N_j^\textrm{last-1} \left( G_j^\textrm{last}\right)^{-1} \\ P_2 = G_j^\textrm{last} G_j^\textrm{last-1} N_j^\textrm{last-2} \left( G_j^\textrm{last-1}\right)^{-1} \left( G_j^\textrm{last}\right)^{-1} \\ \ldots \]

It follows that the full time-evolution operator can be written as:

\[ \Pi_j \exp\left( -\textrm{i} H_j\bar t \right) = \Pi_{j} N_j G_j . \]

Here, each operator \(G_j\) as well as \(N_j\) is a "small-angle" (or probability) transformation. Finally, within the assumption that rotations over small-angle transformations can be neglected, we get the expression for the full Trotter step:

\[ \Pi_{j} N_j G_j \approx \left(\Pi_j N_j \right) \left(\Pi_j G_j \right) = \left( \Pi_j N_j\right) G. \]

(It should be noted that the SWAP algorithm of quantum simulation, available in later releases, includes large-angle gates between decomposition blocks. The effect on the noise mapping, described above, is however analogous to state swaps and can be accounted for by simple "reordering dictionaries" in decomposition-block definitions.)

Lindbladian in the simulated system

Noise mapping is most intuitively formulated in terms of unitary transformations of Lindbladian operators. When constructing the effective model Lindbladian, we include all consecutive noise operations \(N_j\), each of them being some multiplication of transformed Lindbladians \(N_j=\Pi_k\exp\left(L_j^k\tau_j^k\right)\) (see below), under one (exponentiated) Lindbladian,

\[ \Pi_j N_j = \Pi_{j} \Pi_{k} \exp\left(L_j^k\tau_j^k\right) \approx \exp\left(\sum_{jk} L_j^k\tau_j^k\right) \equiv \exp\left(L_\textrm{effective}\bar t\right). \]

Here, each summed Lindbladian \(L_j^k\) has noise operators \(A_i\) that are conjugated by the unitary gate defined by its corresponding decomposition, \(O_j^k\equiv U_j^\textrm{last}U_j^\textrm{last-1}\ldots U_j^\textrm{k+1}\),

\[ A_{i} \rightarrow A_i^{jk} \equiv O_j^k A_i \left(O_j^k\right)^\dagger. \]

(For native gates there will be no transformation, so that for these \(A_i^{jk} = A_{i}\).) The final effective Lindbladian then takes the form:

\[ L_\textrm{effective}[\rho] \equiv \sum_{ii'jk} \frac{\tau_j^k}{\bar t} M^{jk}_{ii'}\left( S_1- \frac{1}{2}S_2\right). \]

with

\[ S_1 = A_i^{jk} \rho (A^{jk}_{i'})^\dagger \]

and the anti-commutator

\[ S_2 = \lbrace(A^{jk}_{i'})^\dagger A_i^{jk} , \rho \rbrace \]

Here, an important noise scaling factor \(\tau / \bar t\) appears. We notice that the effective noise decreases with increasing Trotter timestep \(\bar t\). An example of this is given on page examples.

Error in the noise mapping

The above approximations are analogous to neglecting the Trotter error. First, we made the approximation that noise transformations can be restricted to individual decomposition blocks. The error of this approximation is of size:

\[ \epsilon_\textrm{error} \propto \bar g \gamma\tau , , \]

where \(\bar g= g \bar t\) and \(\gamma \tau\) are some typical Hamiltonian energy and noise probability of non-commuting elements between the two. Importantly, the error goes to zero in the limit \(\bar t\rightarrow 0\).

Second, in the derivation of the Lindbladian, an error occurs when we combine consecutive noise terms (Lindbladians) under one exponent. This step has an error of size:

\[ \epsilon_\textrm{error} \propto (\gamma\tau)^2 \]

The validity of the noise mapping can be investigated numerically by comparing the solution for the original noisy circuit and for the effective Lindbladian, as shown on page examples.