update planning
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@ -56,7 +56,7 @@ or research usage, generally, one or a few topologies are used by the user in th
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The proposed method is to use user defined criteria, such as expressivity, entanglement (e.g., Schmidt
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strength@quantum-dynamics-physical-resource), and number of trainable parameters as a cost function, and generating an Ansatz to best fulfill
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the requirement given a topology. Methods of generating the Ansatz can be done either by procedural generation
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(algorithmic), using optimization methods such as genetic algorithms@architecture-search@evolutionary-architecture-search, or by using machine learning
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(algorithmic), using optimization methods such as genetic algorithms@genetic-expressibility@evolutionary-architecture-search, or by using machine learning
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methods such as generative AI@generative-quantum-eigensolver. The choice is to be determined by the MSc. student, based on a review of
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methods.
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Targets for the projects are comparisons on standard benchmarks of the QML Ansatz using hardware
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@ -95,6 +95,16 @@ This experience will be useful since my project will have a significant focus on
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= Risk Management
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== Project related
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The planned search does not perform well at all, in this case I'll try to figure out why it isn't perfoming decently.
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If I find that it just won't work I'll write down the negative result so someone else doesn't get the same idea later.
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To mitigate the risk of losing progress I will be using Git all throughout my development cycle. This will give a
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history of edits made to the code and texts so I can easily go back to an earlier point if something didn't work out.
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== Personal
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Due to the nature of making something new, there are a couple of risks in the time management.
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If, after the literature research, I decide to use a certain strategy and figure out that it won't work
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much later on this could lead to a significant amount of wasted time.
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@ -141,30 +151,37 @@ to join as well.
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week(9), [Work on Project],
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week(10), [Work on Project],
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week(11), [Work on Project],
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week(12), [Work on Project],
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week(13), [Work on Project],
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week(14), [Create progress report],
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week(15), [Midterm],
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week(16), [Benchmarking],
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week(17), [Implement Project Improvements],
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week(18), [Implement Project Improvements],
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week(19), [Benchmarking],
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week(20), [Implement Project Improvements],
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week(21), [Implement Project Improvements],
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week(22), [Testing with hardware],
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week(23), [Implement issue fixes],
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week(24), [Testing on hardware],
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week(25), [Write Report],
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week(26), [Write Report],
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week(27), [Draft 1],
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week(28), [Green Light],
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week(29), [Write Report],
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week(30), [Draft 2],
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week(31), [Write Report],
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week(32), [Final version],
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week(33), [],
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week(12), [Prepare project for surgery],
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week(13), [Prepare for surgery],
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week(14), [Surgery],
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week(15), [Recovery],
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week(16), [Recovery],
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week(17), [Recovery],
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week(18), [Recovery (might take longer, if so the rest slides)],
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week(19), [Work on Project],
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week(20), [Work on Project],
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week(21), [Work on Project],
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week(22), [Create progress report],
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week(23), [Midterm],
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week(24), [Benchmarking],
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week(25), [Implement Project Improvements],
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week(26), [Implement Project Improvements],
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week(27), [Benchmarking],
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week(28), [Implement Project Improvements],
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week(29), [Implement Project Improvements],
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week(30), [Testing with hardware],
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week(31), [Implement issue fixes],
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week(32), [Testing on hardware],
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week(33), [Write Report],
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week(34), [Help Organise Festival],
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week(35), [Help Organise Festival],
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week(36), [Help Organise Festival],
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week(37), [Write Report],
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week(38), [Draft 1],
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week(39), [Green Light],
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week(40), [Write Report],
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week(41), [Draft 2],
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week(42), [Write Report],
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week(43), [Final version],
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)
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@ -55,6 +55,11 @@
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#let today-offset = (datetime.today() - datetime(day: 10, month: 11, year: 2025)).weeks()
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= Week 13
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Pre-OP wrapup
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==
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= Week 12
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Making Baselines
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@ -3,4 +3,20 @@
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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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= Theory
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This section explains the concepts necessary to understand the goal.
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We start by explaining what Quantum Architecture Search is about,
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followed by an explanation of various proxies used in the implemented search, namely Expressivity and Entanglement
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== Quantum Architecture Search
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The act of finding the best parametrized quantum circuit to optimize for a specific quantum computer and task.
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In our case I will be talking about "Optimization-Free" QAS as well, which indicates that the parameters of the
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circuits aren't optimized during the search process.
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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.
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== Expressivity
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== Entanglement
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@ -2,3 +2,40 @@
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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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