mthree 0.20.0

M3: Matrix-free measurement mitigation

mthree

Matrix-free Measurement Mitigation (M3).

M3 is a measurement mitigation technique that solves for corrected measurement probabilities using a dimensionality reduction step followed by either direct LU factorization or a preconditioned iterative method that nominally converges in O(1) steps, and can be computed in parallel. For example, M3 can compute corrections on 42 qubit GHZ problems in under two seconds on a quad-core machine (depending on the number of unique bitstrings in the output).

Documentation

Online Documentation @ Qiskit.org

Installation

You can pip install M3 in serial mode using PyPi via:

pip install mthree

Alternatively, one can install from source:

python setup.py install

To enable openmp one must have an openmp 3.0+ enabled compiler and install with:

python setup.py install --with-openmp

Optionally one can also set -march=native using:

python setup.py install --with-native

The openmp and native flags can be used simultaneously using a comma.

OpenMP on OSX

On OSX one must install LLVM using homebrew (You cannot use GCC):

brew install llvm

after which the following (or the like) must be executed in the terminal:

export PATH="/usr/local/opt/llvm/bin:$PATH"

and

export LDFLAGS="-L/usr/local/opt/llvm/lib -Wl,-rpath,/usr/local/opt/llvm/lib"
export CPPFLAGS="-I/usr/local/opt/llvm/include"

Then installation with openmp can be accomplished using:

CC=clang CXX=clang python setup.py install --with-openmp

Usage

Basic usage

M3 is simple to use:

import mthree
# Specify a mitigator object targeting a given backend
mit = mthree.M3Mitigation(backend)

# Compute the 1Q calibration matrices for the given qubits and given number of shots
# By default it is over all backend qubits at 8192 shots.
mit.cals_from_system(qubits, shots)

# Apply mitigation to a given dict of raw counts over the specified qubits
m3_quasi = mit.apply_correction(raw_counts, qubits)

Note that here qubits is a list of which qubits are measured to yield the bits in the output. For example the list [4,3,1,2,0] indicates that a measurement on physical qubit 4 was written to classical bit zero in the output bit-strings, physical qubit 3 maps to classical bit 1, etc. The fact that the zeroth bit is right-most in the bitstring is handled internally.

Error bounds

It is possible to compute error bounds in a similarly efficient manner. This is not done by default, but rather turned on using:

m3_quasi = mit.apply_correction(raw_counts, qubits, return_mitigation_overhead=True)

Then the distribution itself can be called to return things like the expectation value and the standard deviation:

expval, stddev = quasi.expval_and_stddev()

Closest probability distribution

The results of M3 mitigation are quasi-probabilities that nominally contain small negative values. This is suitable for use in computing corrected expectation values. However, if one needs a true probability distribution then it is possible to convert from quasi-probabilites to the closest true probability distribution in L2-norm using:

closest_probs = m3_quasi.nearest_probability_distribution()

Truncating based on Hamming distance

An additional benefit of the way M3 works is that it is possible to compute the effect of mitigation when only looking at errors that are up to a given Hamming distance away. This allows for investigating if large weight errors have much impact on the output. This can be controlled by the distance keyword argument in apply_correction:

m3_quasi = mit.apply_correction(raw_counts, qubits, distance=DIST)

By default, M3 computes errors out to the full distance. At large numbers of unique bit-strings truncating to small Hamming distance can have some performance benefits.

License