noa/thesis
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

write some preliminary introduction

Browse source
This commit is contained in:
Noa Aarts 2025-12-17 12:20:22 +01:00
parent bfd2584500
commit 3c49a1ea15
Signed by: noa
GPG key ID: 1850932741EFF672
2 changed files with 38 additions and 19 deletions
Download patch file Download diff file Expand all files Collapse all files

2
report/sections/0default-template.typ
View file

@ -94,7 +94,7 @@ Tables and images can be inserted into the document via the `#figure` function.
== Referencing stuff <sec:refs> == Referencing stuff <sec:refs>
Tables, Figures and Equations are numbered by section. When refering to one of these items, the number becomes green (color is customizable). For example, this is @sec:refs and the afterwards we have @subsec:chem. For figures, it is possible to attach an additional supplement to a figure, for example @fig:large-image could have subpanels like @fig:large-image[a], which you can specify via ```typ @fig:label[a]```. Tables, Figures and Equations are numbered by section. When refering to one of these items, the number becomes green (color is customizable). For example, this is @sec:refs and the afterwards we have @subsec:chem. For figures, it is possible to attach an additional supplement to a figure, for example @fig:large-image could have subpanels like @fig:large-image[a], which you can specify via ```typ @fig:label[a]```.
References are formatted as @yamanaka_nanoscale_2000. Several references are joined according to @asmatulu_characterization_2019@binnig_atomic_1986@boussinesq_application_1885. References are formatted as. Several references are joined according to.
=== Chemical formula <subsec:chem> === Chemical formula <subsec:chem>

55
report/sections/1introduction.typ
View file

@ -5,27 +5,46 @@
= Introduction = Introduction
In the NISQ era quantum-classical hybrid methods are better due to the limited depth, bla bla bla Quantum computers capable of performing the widely recognised algorithms
like Shor's factorisation algorithm // TODO: cite
and large data Grovers Search // TODO: cite
are not techonologically feasible yet. // TODO: add citations
There has been significant research into various methods of Quantum Architecture Search (QAS), This is due to the current quantum computers still having noisy qubits that which limits
(list a bunch). the circuit depth of algorithms that can be performed. They also have a limited amout of qubits,
Most of these have been searches that are specific to the problem that is being solved, which is why this era of quantum computing is called the Noisy Intermediate-Scale Quantum (NISQ) era.
so the Paremetrized quantum circuits (PQCs) are generally not transferrable between different problems.
There has also been a bit of research into task-agnostic QAS, where search This does not mean there are no uses for these NISQ quantum computers,
can be performed once per hardware iteration instead of per problem, using proxies like expressibility Quantum Machine Learning and other quantum-classical methods are still promising prospects.
and entanglement (CITE THE PAPER). Diverse real-world practical capabilities have been demonstrated like // TODO: list with citations
Where maybe some finetuning can help specialise into a specific problem afterwards. (HYPOTHETICAL)
These task-agnostic searches have yet to include things like noise, arbitrary connectivity graphs The quantum-classical methods start with a quantum circuit that contains some amount of parametrized
and allowed gate types on a per-qubit basis. And also haven't shown great scalability to architectures gates, called Parametrized Quantum Circuits or PQCs for short. This PQC is used with a training set
with more qubits. where a classical computer takes measurement results and optimises the parameters to solve a problem.
This research proposes a Task-Agnostic Evolutionary QAS implementing // TODO: find some way to explain why QAS is necessary here
hardware constraints and noise profiles able to target circuits with a
user-specified expressibility and entanglement to be able to mitigate the barren plateaus from
an expressibility that's too high (cite).
To achieve these goals a python library will be made that allows for composing various filters To address these challenges methods have been proposed for automatically finding the PQC,
and generators together (with multithreading) to help with developing and iterating on QAS methods. also known as Quantum Architecture Search (QAS),
which can solve the problem like: ... // TODO: list with citations
Most of these solutions evaluate performance of the circuit with optimisation during the search,
these classical optimisations take a significant amount of time and are therefore not very scalable.
// TODO: add citations
Some of the solutions /* TODO: add citations to TF-QAS and evolutionary expressibility */ use
proxies to optimize a PQC without any quantum-classical optimization, however they do not implement
hardware constraints and noise patterns which may limit their applicability on real hardware.
To address these limitations we propose QD-QAS, // Quality-Diversity Quantum Architecture Search
a problem-agnostic yet hardware-specific quantum architecture search based on hardware topology,
noise patters and gatesets.
// These are just goals atm, but can be made concrete once I did them
- We use Quality-Diversity Evolutionary Algorithms trained using proxies for Expressibility,
Entanglement, and Noise to create a diverse set of PQCs to make sure every problem has a
PQC that works well.
- We benchmark this algorithm on both runtime and performance in against, TF-QAS@training-free,
Random Sampling and GA-QAS@genetic-expressibility. Since both TF-QAS and GA-QAS don't include
noise but QD-QAS does we will compare under multiple types and amounts of noise.
- We also develop an open-source python library to efficiently work with Evolutionary algorithms
combined with various proxies that is easily extendable to new usecases.