Algorithms for artificial intelligence are rapidly entering our daily lives. Machine learning is already or soon will be the foundation of many systems that demand high levels of security. To name a few of these technologies, there are robotics, autonomous vehicles, banking, facial recognition, and military targeting software.
This poses a crucial question: How resistant to hostile attacks are these machine learning algorithms?
Security experts believe that incorporating quantum computing into machine learning models may produce fresh algorithms that are highly resistant to hostile attacks.
Data manipulation attacks' risks
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 from the physical world, people are capable of committing such attacks. To trick a self-driving car's artificial intelligence into thinking a stop sign is a speed restriction sign, someone may apply a sticker to it. Or, soldiers may wear clothing on the front lines that would make them appear to be natural terrain features to AI-based drones.
Attacks on data manipulation can have serious repercussions in any case.
For instance, a self-driving car may mistakenly believe there are no people on the road if it utilises a machine learning algorithm that has been compromised. In reality, there are people on the road.
What role quantum computing can play
In this article, we discuss the potential development of secure algorithms known as quantum machine learning models through the integration of quantum computing with machine learning.
In order to detect certain patterns in image data that are difficult to manipulate, these algorithms were painstakingly created to take advantage of unique quantum features. Resilient algorithms that are secure from even strong attacks would be the outcome. Furthermore, they wouldn't call for the pricey "adversarial training" that is currently required to train algorithms to fend off such assaults.
Quantum machine learning may also provide quicker algorithmic training and higher feature accuracy.
So how would it function?
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, specifically 0s and 1s, in traditional computers, which adhere to the principles of classical physics.
On the other hand, quantum computing adheres to the same rules as quantum physics. Quantum bits, or qubits, are used in quantum computers to store and process information. Qubits can be simultaneously 0, 1, or both 0 and 1.
A quantum system is considered to be in a superposition state when it is simultaneously in several states. It is possible to create smart algorithms that take advantage of this property using quantum computers.
Although employing quantum computing to protect machine learning models has tremendous potential advantages, it could potentially have drawbacks.
On the one hand, quantum machine learning models will offer vital security for a wide range of sensitive applications. Quantum computers, on the other hand, might be utilised to develop powerful adversarial attacks capable of readily misleading even the most advanced traditional machine learning models.
Moving forward, we'll need to think carefully about the best ways to defend our systems; an attacker with early quantum computers would pose a substantial security risk.
Obstacles to overcome
Due to constraints in the present generation of quantum processors, current research shows that quantum machine learning will be a few years away.
Today's quantum computers are relatively small (fewer than 500 qubits) and have substantial error rates. flaws can occur for a variety of causes, including poor qubit manufacture, flaws in control circuitry, or information loss (referred to as "quantum decoherence") caused by interaction with the environment.
Nonetheless, considerable progress in quantum hardware and software has been made in recent years. According to recent quantum hardware roadmaps, quantum devices built in the coming years are expected to include hundreds to thousands of qubits.
These devices should be able to run sophisticated quantum machine learning models to help secure a wide range of sectors that rely on machine learning and AI tools.
Governments and the commercial sector alike are increasing their investments in quantum technology around the world.
This month, the Australian government unveiled the National Quantum Strategy, which aims to expand the country's quantum sector and commercialise quantum technology. According to the CSIRO, Australia's quantum sector might be valued A$2.2 billion by 2030.
