This article is made possible thanks to collaboration between The European Sting and the World Economic Forum. /
Author: Johnny Wood, Writer, Forum Agenda
- Quantum computing threatens all current cybersecurity protocols.
- But quantum machine learning, with its ability to handle huge data sets, could offer a stronger form of cybersecurity.
- Organizations should start long-term planning for the new quantum environment.
As quantum computing becomes a reality, we are witnessing the formation of a quantum economy. Several companies already offer quantum as a service or quantum in the cloud. However, the ecosystem will soon include many additional services such as quantum circuit optimization and efficiency advisors. On top of this foundation will be built business models, industries and even secondary technologies and products. Prices will fall and availability will increase, including lower barriers to entry.
Quantum computing will have a major impact on several industries, including finance. Financial institutions use quantum technology to enhance current financial forecasts. And in pharma, drug discovery and optimization are enhanced by running simulations with quantum computers. Quantum computing is a new paradigm and represents a revolutionary way to ask questions and build solutions. Many new approaches will build on it and will bring about major changes in many areas.
Quantum machine learning (QML), a combination of quantum computing and machine learning (ML), holds tremendous potential for several reasons. Both fields have uncertainty at their core, and that shortcoming turns into a strength in the quantum field. In addition to application areas that exhibit a high degree of ambiguity, machine learning’s internal inaccuracies enable the computation of results that are otherwise unachievable in terms of input volume and computational speed (e.g., image classification, The concept of “cat” and “cat”) “dog” is not clearly defined). The probabilistic nature of quantum computing, which is a major obstacle for many algorithms, fits well with these properties. For these reasons, QML is on its way to becoming one of the first applications of quantum computing outside academia. As a result, there is already a great deal of interest in quantum machine learning from academia, industry and government.
quantum cyber security
Cybersecurity is fragile across the digital ecosystem. Businesses and governments already face many threats and struggle to keep their computing environments secure. Conceptually, these threats include state-sponsored activities such as terrorism, cyberwarfare, and industrial espionage, as well as well-funded and deeply motivated malicious actors, Crime-as-a-Service, and state-sponsored attacks. activities are included.
Quantum computing has the potential to make things worse. Adding quantum computing capabilities to this threat could make it even more effective for malicious actors. For example, new computational options offered by quantum computing may make it easier to find exploitable security gaps or trick existing ML and QML models into doing what attackers want. .
But the situation is not entirely bleak. Artificial intelligence and machine learning have been used for some time to meet cybersecurity goals and protect against threats. ML can detect anomalous behavior or identify malicious emails and executable code. Most of the time these applications require a lot of data during the training phase. As a result, traditional ML for cybersecurity is very resource intensive and costly. However, innovations over the past decade have reduced the cost of ML, whether it be in hardware (such as tensor processing units) or AI models (such as ChatAI and ImageAI in various forms and flavors), thereby reducing the development of marketable products. has become more sustainable.
Quantum machine learning has the potential to offer significant efficiency benefits during the model training phase, making ML a more effective security tool. Inherent aspects of quantum computing, such as entanglement and superposition, facilitate and simplify the processing of huge datasets. Currently, unresolved issues of data encoding and loading onto quantum computers are obstacles to processing large amounts of data on quantum computers, but it is hoped that the industry will overcome these challenges over the next decade. we fully expect.
Machine learning in the real world
Academia has advanced cybersecurity QML based on simulations. However, at some point simulation becomes insufficient and real-world testing becomes necessary. Detecting spam in email can be considered a benchmark. It’s well-understood, not overly complex, and has a large amount of useful training data available. Capgemini worked with the Fraunhofer IAIS to use real quantum computers to perform spam filtering. While this method is currently not cost effective, it shows what is possible and the advantages and disadvantages of using today’s quantum devices for cybersecurity QML.
Other uses of quantum machine learning in cybersecurity can also be predicted, such as mapping critical infrastructure and cybercriminal ecosystems. Combined, these applications make QML a very effective tool for defenders.
Stay ahead of the quantum curve
Given the current state of quantum computing, cybersecurity companies do not need to make concrete plans to integrate quantum computing into their daily business operations in the near future. That said, the quantum computing ecosystem is evolving with significant recent advances (like Google’s announcement of the first quantum error correction algorithm) and products (like IBM’s Osprey chip with 433 qubits). . At the same time, research projects are breaking down barriers from purely theoretical to simulations and now to real quantum experiments. These changes not only mean that the amount of quantum computing hype can be reduced, but that we will soon have reliable data points to predict the near future of quantum computing.
Investing in quantum computing requires long-term thinking. No visible gains will materialize in the near future. But when you wait for someone else to carry the burden, you run the risk of being ill-prepared and falling behind. An impending revolution is likely to destroy an unprepared organization.
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What is the World Economic Forum doing on cybersecurity?
The World Economic Forum Cybersecurity Center drives global efforts to address systemic cybersecurity challenges. It is an independent and impartial platform that fosters cooperation on cybersecurity between the public and private sectors. Below are some examples of the impact the Center has had.
cyber security training: Salesforce, Fortinet, and the Global Cyber Alliance have teamed up with the Forum to provide free, accessible training to the next generation of cybersecurity professionals around the world.
cyber resilience: Working with partners, the center plays a vital role in enhancing cyber resilience across multiple industries, including oil & gas, power, manufacturing, and aviation.
IoT security: The Council on the Connected World, led by the Forum, establishes IoT security requirements for consumer devices and protects them from cyberthreats. The effort calls on major manufacturers and vendors around the world to prioritize better IoT security measures.
Paris Calls for Trust and Security in Cyberspace: Our Forum stresses the importance of trust and cooperation in cyberspace and is proud to be a signatory to the Paris Call, which aims to ensure digital peace and security in the world.
Contact us for details on how to participate.
In conclusion, the cybersecurity industry is undergoing changes that reflect changes in the broader digital ecosystem. While we cannot say exactly how QML will fit into cybersecurity operations, we can foresee its usefulness as a security tool. Considering the cybersecurity landscape as a whole, we can’t get there any time soon.
