# Quick Start Guide¶

Below we will assume that you are developing in a jupyter notebook.

## Getting ready to benchmark (QVM)¶

First thing you need to do is open up a terminal and run:

$quilc -S$ qvm -S
\$ jupyter lab


Inside the notebook we need to get some basic pyQuil objects, namely a QuantumComputer, as well as a BenchmarkConnection for some routines. We’ll also need to be able to construct a pyQuil Program.

from pyquil import get_qc
from pyquil.api import get_benchmarker

noisy_qc = get_qc('2q-qvm', noisy=True)
bm = get_benchmarker()

from pyquil import Program
from pyquil.gates import *


Now we are ready to run through some simple examples. We’ll start with state tomography on the plus state.

from forest.benchmarking.tomography import do_tomography

# prepare the plus state
qubit = 1
state_prep = Program(H(qubit))

# tomograph the noisy plus state
state_estimate, _, _ = do_tomography(noisy_qc, state_prep, qubits=[qubit], kind='state')


Process tomography is quite similar (note that this will take a long time with the default arguments).

# specify a process
qubits = [0, 1]
process = Program(CNOT(*qubits))
# tomograph the noisy process CNOT
process_estimate, _, _ = do_tomography(noisy_qc, process, qubits, kind='process')


If we only care about the fidelity of our state or process then we can turn to Direct Fidelity Estimation (DFE) to save time / runs on the quantum computer. Here we use the BenchmarkConnection bm to do some of the operations with the Clifford group.

from forest.benchmarking.direct_fidelity_estimation import do_dfe

# fidelity of a state preparation
(fidelity_est, std_err), _, _ = do_dfe(noisy_qc, bm, state_prep, qubits=[qubit], kind='state')

# process fidelity
(proc_fidelity_est, std_err), _, _ = do_dfe(noisy_qc, bm, process, qubits, kind='process')


Finally we can get estimates of the average error rate for our Clifford gates using Randomized Benchmarking (RB). Here again we use bm to generate random sequences of Clifford gates (compiled to native gates).

from forest.benchmarking.randomized_benchmarking import do_rb

# simultaneous single qubit RB on q0 and q1
qubit_groups = [(0,), (1,)]
num_sequences_per_depth = 20
depths = [2, 10, 20] * num_sequences_per_depth
rb_decays, _, _ = do_rb(noisy_qc, bm, qubit_groups, depths)


These are just a few examples! Peruse the examples notebooks to see more.

## Getting ready to benchmark (QPU)¶

Todo

QMI then re log into qcs and document getting forest benchmarking working