Titulo Estágio
Empowering Classical AI with Quantum Computing
Áreas de especialidade
Sistemas Inteligentes
Engenharia de Software
Local do Estágio
DEI-FCTUC
Enquadramento
There is a large body of compelling evidence that Computation as we have known and used for decades is under challenge. As new models for computation emerge, its limits are being pushed beyond what pragmatically had been seen in practice. In this line, Quantum Computing (QC) has received renewed worldwide attention. Having its foundations been thoroughly studied, mainly from the point of view of its physical implementation, their potential has, even if preliminarily, just been witnessed.
In an article published in Nature in October 2019, Google describes how using a self-built 54-qubit processor correctly executed, in only 200 seconds, a benchmark that even the world’s fastest supercomputer would have taken an estimation of 10,000 years to complete; this way showing the so-called quantum supremacy.
In a follow-up, IBM has disputed the foundations of such estimation, and mainly the claim that quantum supremacy has been reached. IBM’s argument is mainly about the assertion that a properly crafted supercomputer could have reached the same result even more efficiently than the Google quantum computer. However, no empirical demonstration was provided to support such assertion. In essence, Google’s experiment provides clear evidence of the progress that has been made in terms of superconducting-based quantum computing. IBM itself has also made substantial progress to build universal quantum computers to support business, engineering and science.
The field of QC is evolving at a pace faster than people originally expected. For example, in March 2020 Honeywell announced a revolutionary quantum computer based on trapped-ion technology with quantum volume 64 – the highest quantum volume ever achieved, twice as the state of the art, previously owned by IBM. Quantum volume is a unit of measure indicating the fidelity of a quantum system. This important achievement shows that the field of quantum computing may reach industrial impact much sooner than originally expected.
Artificial Intelligence (AI), and its sub-field of Machine Learning (ML), has recently acquired a remarkable relevance in our lives, with benefits in most of their dimensions. Much of the AI algorithms are quite complex and require high computational features, which limits much of the AI application range and benefits. With the advent of QC, these two technologies, that have incredible potential in their own right, are being brought together, with the aim of building quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Such a breakthrough could lead to advances in various domain application of AI such as drug discovery, chemistry, and data science. Essentially, quantum AI algorithms, and especially ML algorithms, could allow us to solve complex problems much more quickly and to to tackle others that are currently unfeasible. For this reason, many companies (e.g., IBM, Honeywell, Google, Microsoft, Amazon, Mitsubishi, Volkswagen) have deployed quantum computing with AI to explore solutions to their problems.
In this project, we will explore the potential of QC to address computational issues within AI.
Objetivo
The candidate will join a group of researchers who are doing intensive, foundational work on QC and AI.
The general goal is bringing together AI algorithms and QC in order to solve classical AI problems faster.
In concrete, we aim at reaching the following goals:
- Analysis of Quantum Computing techniques and classical AI algorithms
- Analysis and specification of Quantum AI algorithms
- Implementation and testing of a Quantum AI algorithm
Plano de Trabalhos - Semestre 1
1- State of the Art [Sept – Oct]
2- Selection of Classical AI Algorithms [Nov]
3- Development and First Implementation of the Quantum AI Algorithm Selected [Dec – Jan]
4- Thesis Proposal Writing [Dec – Jan]
Plano de Trabalhos - Semestre 2
5- Improvement of the Quantum AI Algorithm Selected [Feb – Apr]
6- Experimental Tests [Apr – May]
7- Paper Writing [May – Jun]
8- Thesis Writing [Jan – Jul]
Condições
The eligible student will have at disposal all the necessary computational platforms, tools and devices.
He will work within a research team that is working on related issues.
It might also be possible to ensure funding for the student in the form of a research grant.
Observações
Co-orientador: João Paulo Fernandes; jpaulo@fe.up.pt
Orientador
Luís Macedo
macedo@dei.uc.pt 📩