Process Tomography on a 7-Qubit Quantum Processor via Tensor Network
  Contraction Path Finding
Quantum process tomography (QPT), where a quantum channel is reconstructed
through the analysis of repeated quantum measurements, is an important tool for
validating the operation of a quantum processor. We detail the combined use of
an existing QPT approach based on tensor networks (TNs) and unsupervised
learning with TN contraction path finding algorithms in order to use TNs of
arbitrary topologies for reconstruction. Experiments were conducted on the
7-qubit IBM Quantum Falcon Processor ibmq_casablanca, where we demonstrate this
technique by matching the topology of the tensor networks used for
reconstruction with the topology of the processor, allowing us to extend past
the characterisation of linear nearest neighbour circuits. Furthermore, we
conduct single-qubit gate set tomography (GST) on each individual qubit to
correct for separable errors during the state preparation and measurement
phases of QPT, which are separate from the channel under consideration but may
negatively impact the quality of its reconstruction. We are able to report a
fidelity of 0.89 against the ideal unitary channel of a single-cycle random
quantum circuit performed on ibmq_casablanca, after obtaining just $1.1 \times
10^5$ measurements for the reconstruction of this 7-qubit process. This
represents more than five orders of magnitude fewer total measurements than the
number needed to conduct full, traditional QPT on a 7-qubit process.
Aidan Dang Gregory A. L. White Lloyd C. L. Hollenberg Charles D. Hill