41 lines
2.1 KiB
Typst
41 lines
2.1 KiB
Typst
#import "@preview/classy-tudelft-thesis:0.1.0": *
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#import "@preview/physica:0.9.6": *
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#import "@preview/unify:0.7.1": num, numrange, qty, qtyrange
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#import "@preview/zero:0.5.0"
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= Methods
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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.
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It won't be written in a chronological order.
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I will start by talking about the created algorithm, after which I will explain the benchmarks,
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and finally the shortly mention the algorithms@training-free@genetic-expressibility used as baselines.
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== Quality-Diversity Quantum Architecture Search
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== Benchmarking
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As the goal of QD-QAS is to create quantum circuits that are hardware-specific but task-agnostic.
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The same search outputs should be tested on multiple problems. They should be compared to the
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transpiled versions of Hardware Efficient Ansatze@hea-kandala as well as other searches that don't involve
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search-time optimization.
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== Baseline tests
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We started by implementing the protocols from Training-Free Quantum Architecture Search@training-free and
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Genetic optimization of ansatz expressibility for enhanced variational quantum algorithm performance@genetic-expressibility.
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So we have some related algorithms to compare our QD-QAS against.
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In this section I'll go into how these implementations went.
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=== Training-Free Quantum Architecture Search
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As the source code wasn't linked anywhere in the paper I started by trying to replicate it purely from the texts.
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But doing it this way I ran into an issue with reproducibility. This led me to contact the authors who
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sent me the code very quickly. Translating their code so I could use it with the same testing as mine was
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my next goal. I already had large parts the same but not all so this isn't done yet as of 06-02-2026,
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but I might be able to finish later today.
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=== Genetic optimization of ansatz expressibility for enhanced variational quantum algorithm performance
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Like the other paper there was no link to a repository with the code, but this paper included more pseudocode
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samples so I think I was able to replicate it quite quickly. I need to create some more actual benchmarks to compare
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them (and mine once I make it)
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