What is Meant by Applied Quantum Computing?
Applied quantum computing involves using quantum computing technology to solve real-world problems in various industries. We will talk about the most important quantum applications and delve into practical use cases for each one.
5 Most Important Quantum Computing Applications
1. MATERIAL SCIENCE
Firstly, quantum computing may be able to simulate quantum systems, which can be useful in understanding molecule and material behaviour, benefitting verticals like materials science and drug discovery.
Companies like Google, IBM, Microsoft, and Intel have their own quantum computing research divisions while Airbus, Volkswagen and JP Morgan Chase are actively searching for solutions to some of their most pressing problems.
Generally, large companies use quantum computing to explore a wide range of applications in the emerging field and remain ahead of the curve. However, smaller companies and even startups are also in on the game, too.
With classical computers, as molecular interactions become more complex, calculations become less accurate. In the report, Exploring quantum use cases for chemicals and petroleum, published by IBM Institute for Business Value, one use case: “Developing chemical products, including catalysts and surfactants”, gives the example that the development of new chemical methods and materials is accelerated by the use of quantum computers in chemical and petroleum companies.
Taken from a Nature cover story from 2017, the report goes on to explain how IBM’s publicly available quantum computers could be used to model lithium hydride (LiH) and beryllium hydride (BeH2) in the chemicals and petroleum industries. These same hybrid methods may soon be used such as to develop new catalysts for reducing emissions or surfactants to improve subsurface recovery, the report noted.
Proof of this can be seen with ExxonMobil. The company is advancing the use of quantum computing in developing next-generation energy and manufacturing technologies and sees potential applications including optimizing power grids, developing more accurate environmental models and developing highly accurate quantum chemistry calculations for discovering new materials to capture carbon more efficiently.
2. QUANTUM COMPUTING IN FINANCE
Next, quantum computing is expected to improve financial modelling, thus improving market predictions and risk management.
In a press release from January 2023, quantum computing companies Multiverse Computing, Pasqal and one of France’s largest banks Crédit Agricole announced the end of a 1.5-year POC study “to evaluate the contribution of an algorithmic approach inspired by quantum computing, and the potential of quantum computers, in two areas: the valuation of financial products, and the assessment of credit risks.”
In the news release, the companies cite papers that provide more details on the work, with the most recent, Quantum-Inspired Tensor Neural Networks for Option Pricing, published on ArXiv in the same month.
In the study, two experiments were addressed on successful derivatives calculation and counterparties downgrades anticipation, with the conclusion being drawn that “a marked improvement in computing time requiring a smaller memory footprint was measured using quantum computing techniques, paving the way for their use in real-world applications in the valuation of derivatives. For the quantum computer, the chosen problem was tackled under real-world conditions. With a quantum processor of only 50 qubits, the results obtained are as accurate as the results in production. Our projections indicate that this performance could be bettered at 300 qubits, a power that should be available industrially in 2024.”
As a result of their research, the authors claim that they have developed the first quantum-enhanced ML algorithm for predicting credit rating downgrades.
Check out the full article by The Quantum Insider here.