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report/sections/2theory.typ
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#import "@preview/unify:0.7.1": num, numrange, qty, qtyrange
#import "@preview/zero:0.5.0"
= Theory
This section explains the concepts necessary to understand the goal.
We start by explaining what Quantum Architecture Search is about,
followed by an explanation of various proxies used in the implemented search, namely Expressivity and Entanglement
== Quantum Architecture Search
The act of finding the best parametrized quantum circuit to optimize for a specific quantum computer and task.
In our case I will be talking about "Optimization-Free" QAS as well, which indicates that the parameters of the
circuits aren't optimized during the search process.
A consequence of this limitation are task agnostic results like the task agnostic nature of the Hardware Efficient Ansatze as described by Kandala@hea-kandala.
== Expressivity
== Entanglement

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report/sections/3methods.typ
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#import "@preview/physica:0.9.6": *
#import "@preview/unify:0.7.1": num, numrange, qty, qtyrange
#import "@preview/zero:0.5.0"
= Methods
In this chapter I'll go into what I made, how I made it and a bit of why I did it a certain way.
It won't be written in a chronological order.
I will start by talking about the created algorithm, after which I will explain the benchmarks,
and finally the shortly mention the algorithms@training-free@genetic-expressibility used as baselines.
== Quality-Diversity Quantum Architecture Search
== Benchmarking
As the goal of QD-QAS is to create quantum circuits that are hardware-specific but task-agnostic.
The same search outputs should be tested on multiple problems. They should be compared to the
transpiled versions of Hardware Efficient Ansatze@hea-kandala as well as other searches that don't involve
search-time optimization.
== Baseline tests
We started by implementing the protocols from Training-Free Quantum Architecture Search@training-free and
Genetic optimization of ansatz expressibility for enhanced variational quantum algorithm performance@genetic-expressibility.
So we have some related algorithms to compare our QD-QAS against.
In this section I'll go into how these implementations went.
=== Training-Free Quantum Architecture Search
As the source code wasn't linked anywhere in the paper I started by trying to replicate it purely from the texts.
But doing it this way I ran into an issue with reproducibility. This led me to contact the authors who
sent me the code very quickly. Translating their code so I could use it with the same testing as mine was
my next goal. I already had large parts the same but not all so this isn't done yet as of 06-02-2026,
but I might be able to finish later today.
=== Genetic optimization of ansatz expressibility for enhanced variational quantum algorithm performance
Like the other paper there was no link to a repository with the code, but this paper included more pseudocode
samples so I think I was able to replicate it quite quickly. I need to create some more actual benchmarks to compare
them (and mine once I make it)