slb / byu-iqc-slb-challenge
Public(m, 3) array of indices into the points, one row per triangle.
Your solve(points) below is scored at the end of this notebook on the 100 evaluation
instances, with the functions of metric.py in this workspace: the score is the fraction of
instances for which it returns exactly the Delaunay triangulation. That only checks that the
answers are right; the jury also looks at
generate_samples() returns the evaluation instances: points 0 to 3 are the corners of
the unit square, the rest are random. Use the slider to browse them.solve with your algorithm. It starts out as scipy's classical
Delaunay, which computes the triangulation with Qhull, so the template scores 100%.QPU submits circuits written with
qiskit, pytket or guppy to the quantum computers and emulators of aqora's
providers. In an aqora workspace it is already
authenticated as you.
See working with jobs and
reading results for what you can
do with a submitted job.
The cells below are a skeleton to build on: build_circuit returns a
placeholder circuit, to replace with the one your approach needs. Keep in mind
that a scored trial has 100 patients, more qubits than an emulator can simulate. ┌───┐ ░ ┌─┐
q_0: ┤ H ├──■───░─┤M├───
└───┘┌─┴─┐ ░ └╥┘┌─┐
q_1: ─────┤ X ├─░──╫─┤M├
└───┘ ░ ║ └╥┘
meas: 2/══════════════╩══╩═
0 1 build_circuit() as one job each time you click
it. It doesn't run when you edit the notebook
(mo.ui.run_button), so the
emulator isn't called on every change.
qpu_platform picks the platform as provider:name.
scaleway:EMU-AER-32C-256M is a public Aer emulator; see
choosing a platform for
the others.qpu_team is your team's username: jobs are attributed to it and count
against its quota
(submitting as a team).
With None, they are attributed to you.evaluate from metric.py runs your solve on the 100 evaluation instances and checks every
answer; aggregate turns that into the score. If your solve submits jobs to a QPU, put this
cell behind a mo.ui.run_button like the one above, so that it doesn't run on every edit.