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Finding Order in Large-scale Structures by Quantum Computing

Project description

New approaches to quantum algorithm development

Quantum machine learning is an especially interesting aspect of the emerging field of quantum computing. However, since recent developments are mainly based on heuristic approaches that are as yet unable to be tested properly, quantum machine learning lacks a firm theoretical understanding. With this in mind, the EU-funded QuantOrder project aims to outline new approaches for quantum algorithm development while also improving aspects of the quantum machine learning theory. The project’s ideas will all focus on finding large-scale structures in various objects, taking advantage of the efficiency that quantum computers demonstrate in recognising patterns.

Objective

Quantum computing is an emerging, interdisciplinary field of science in the intersection of computer science, mathematics and physics. Recent experimental advances in building a physical quantum computer show the urgency of finding possible applications. On the other hand to date we only have very small quantum computers, which are mostly useful for proof of concept demonstrations, thus for the time being one needs to focus on building and understanding the underlying mathematical theory. A particularly interesting aspect of quantum computing is quantum machine learning, which also needs a more firm theoretical understanding, because many of the recent developments are based on heuristic approaches which cannot be properly tested yet, due to the limitations of the available hardware.

This proposal outlines new approaches and ideas for quantum algorithm development, and attempts to improve some aspects of the theory of quantum machine learning, while also encompasses some fundamental theoretical questions. The described ideas are all related to the problem of finding large-scale structures in various objects. Since quantum computers tend to be quite efficient at recognizing patterns, it is a promising angle of approach. The relevant ideas are inspired by multiple related disciplines, and several of the proposed tools were recently co-developed by the applicant.

The supervisor has an outstanding track record in developing the mathematical theory of large-scale structures emerging in graphs, groups and networks, while the applicant has demonstrated strong problem solving skills and the ability of developing novel quantum algorithms, which promises a fruitful collaboration in the implementation of the proposed action.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-MSCA-IF-2019

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Coordinator

HUN-REN RENYI ALFRED MATEMATIKAI KUTATOINTEZET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 139 850,88
Address
REALTANODA STREET 13-15
1053 Budapest
Hungary

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Region
Közép-Magyarország Budapest Budapest
Activity type
Other
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 139 850,88
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