Algorithms based on artificial intelligence are rapidly integrating into daily life. Machine learning either now supports many systems that need good security or soon will. These systems include, among others, those for facial recognition, banking, military targeting, robots, and autonomous cars.
How resistant to hostile attacks are these machine learning algorithms? This poses an important point.
My University of Melbourne colleagues and I propose a potential fix for the weakness of machine learning models in an article that was just published in Nature Machine Intelligence.
We suggest that the incorporation of quantum computing in these models might result in novel algorithms that are very resistant to hostile attacks.
The dangers of data manipulation attacks
For certain tasks, machine learning algorithms may be extremely precise and effective. They are very helpful for categorising and locating visual features. But they are also quite susceptible to data manipulation assaults, which can be very dangerous for security.
There are various techniques to conduct data manipulation assaults, which require the very delicate alteration of image data. An attack could be conducted by introducing erroneous data into a dataset used to train an algorithm, causing it to pick up incorrect information.
In situations where the AI system continues to train the underlying algorithms while in use, manipulated data can also be introduced during the testing phase (after training is complete).
Even people in the physical world are capable of carrying out such attacks. A stop sign could have a sticker on it to trick a self-driving car’s artificial intelligence into thinking it is a speed restriction sign. Or, soldiers could dress in uniforms that will make them appear to be landscape elements to AI-based drones on the front lines.
Attacks that manipulate data can have negative effects in either case. For instance, a self-driving car may mistakenly believe there aren’t any people on the road if it utilises a machine learning algorithm that has been corrupted.
How quantum computing can help
In our essay, we discuss how combining quantum computing with machine learning could result in quantum machine learning models, which are safe algorithms.
These algorithms were carefully created to take use of unique quantum characteristics that would enable them to identify precise patterns in visual data that are difficult to distort. The outcome would be robust algorithms that are secure from even strong attackers. Additionally, they wouldn’t need the pricey “adversarial training” that is currently done to educate algorithms how to fend off such assaults.
Beyond this, quantum machine learning might provide quicker algorithmic training and higher feature accuracy.
So how would it work?
The smallest unit of data that modern classical computers can handle is called a “bit,” which is stored and processed as binary digits. Bits are represented as binary numbers, more particularly as 0s and 1s, in traditional computers, which adhere to the principles of classical physics.
Contrarily, quantum computing is based on the same concepts as quantum physics. Qubits (quantum bits), which can exist as 0, 1, or both simultaneously, are used to store and process information in quantum computers. A quantum system is said to be in a superposition state when several states are present at once. Intelligent algorithms that take use of this characteristic can be created using quantum computers.
Although employing quantum computing to protect machine learning models has tremendous potential advantages, it could potentially have drawbacks.
One the one hand, sensitive applications will benefit greatly from the crucial security that quantum machine learning models will offer. On the other hand, formidable adversarial assaults that might readily trick even the most advanced traditional machine learning models could be produced by quantum computers.
In the future, we’ll need to think carefully about the best ways to safeguard our systems because an attacker with access to first-generation quantum computers would offer a substantial security risk.
Limitations to overcome
The current research indicates that, because of limitations in the current generation of quantum processors, quantum machine learning is still a few years away from being a reality.
Modern quantum computers have high mistake rates and are quite tiny (fewer than 500 qubits). Errors can occur for a number of causes, such as incorrect qubit manufacture, flaws in the control circuitry, or information loss (also known as “quantum decoherence”) caused by interactions with the environment.
However, over the past few years, we’ve witnessed tremendous advancements in both quantum hardware and software. Recent roadmaps for quantum hardware predict that quantum devices produced in the next few years will feature hundreds to thousands of qubits.
To assist safeguard a wide range of businesses that depend on machine learning and AI tools, these devices ought to be able to execute potent quantum machine learning models.
Both public and business sectors are expanding their spending on quantum technology globally.
The Australian government unveiled its National Quantum Strategy earlier this month with the intention of developing the country’s quantum sector and commercialising quantum technologies. The CSIRO estimates that by 2030, Australia’s quantum sector will be worth roughly A$2.2 billion.
Conclusion
In conclusion, quantum computing has emerged as a promising tool to enhance the security and efficiency of AI systems, ranging from self-driving cars to military surveillance.
By leveraging the principles of quantum mechanics, quantum computing can address the limitations of classical computing and provide unprecedented computational power and algorithmic capabilities.
Quantum encryption and quantum-resistant algorithms offer robust solutions to safeguard sensitive data and protect AI systems from potential threats.
Furthermore, quantum machine learning algorithms have the potential to significantly improve the performance and training of AI models, enabling them to handle complex tasks with greater accuracy and speed.
As we look to the future, the integration of quantum computing with AI systems holds immense potential to secure and advance the future of artificial intelligence, paving the way for new frontiers in technology and ensuring a safer and more reliable future.
