50 lines
2.9 KiB
Typst
50 lines
2.9 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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= Introduction
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Quantum computers capable of performing the widely recognised algorithms
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like Shor's factorisation algorithm // TODO: cite
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and large data Grovers Search // TODO: cite
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are not techonologically feasible yet. // TODO: add citations
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This is due to the current quantum computers still having noisy qubits that which limits
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the circuit depth of algorithms that can be performed. They also have a limited amout of qubits,
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which is why this era of quantum computing is called the Noisy Intermediate-Scale Quantum (NISQ) era.
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This does not mean there are no uses for these NISQ quantum computers,
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Quantum Machine Learning and other quantum-classical methods are still promising prospects.
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Diverse real-world practical capabilities have been demonstrated like // TODO: list with citations
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The quantum-classical methods start with a quantum circuit that contains some amount of parametrized
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gates, called Parametrized Quantum Circuits or PQCs for short. This PQC is used with a training set
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where a classical computer takes measurement results and optimises the parameters to solve a problem.
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// TODO: find some way to explain why QAS is necessary here
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To address these challenges methods have been proposed for automatically finding the PQC,
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also known as Quantum Architecture Search (QAS),
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which can solve the problem like: ... // TODO: list with citations
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Most of these solutions evaluate performance of the circuit with optimisation during the search,
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these classical optimisations take a significant amount of time and are therefore not very scalable.
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// TODO: add citations
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Some of the solutions /* TODO: add citations to TF-QAS and evolutionary expressibility */ use
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proxies to optimize a PQC without any quantum-classical optimization, however they do not implement
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hardware constraints and noise patterns which may limit their applicability on real hardware.
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To address these limitations we propose QD-QAS, // Quality-Diversity Quantum Architecture Search
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a problem-agnostic yet hardware-specific quantum architecture search based on hardware topology,
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noise patters and gatesets.
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// These are just goals atm, but can be made concrete once I did them
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- We use Quality-Diversity Evolutionary Algorithms trained using proxies for Expressibility,
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Entanglement, and Noise to create a diverse set of PQCs to make sure every problem has a
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PQC that works well.
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- We benchmark this algorithm on both runtime and performance in against, TF-QAS@training-free,
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Random Sampling and GA-QAS@genetic-expressibility. Since both TF-QAS and GA-QAS don't include
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noise but QD-QAS does we will compare under multiple types and amounts of noise.
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- We also develop an open-source python library to efficiently work with Evolutionary algorithms
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combined with various proxies that is easily extendable to new usecases.
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