While quantum computing is still in the early phases, there have already been many innovations and breakthroughs. Companies like IBM, Microsoft, Google and Honeywell have been investing aggressively in the technology.
So then what is quantum computing? Well, it is similar to traditional computing, which relies on bits—that is, the 0’s and 1’s to encode information. But quantum computing as its own version of this: the quantum bit or qubit. This is where the information can have multiple states at the same time. And the reason for this is the impact of the effects of quantum mechanics, like superposition and entanglement. Yes, this is all about the spooky world of Schrodinger’s cat, which is both alive and dead at the same time!
“Quantum computing is a new kind of computing, using the same physical rules that atoms follow in order to manipulate information,” said Dr. Jay Gambetta, who is an IBM Fellow and vice president of IBM Quantum. “At this fundamental level, quantum computers execute quantum circuits—like a computer’s logical circuits, but now using the physical phenomena of superposition, entanglement, and interference to implement mathematical calculations out of the reach of even our most advanced supercomputers.”
One of the fertile areas for quantum computing is AI (Artificial Intelligence), which relies on processing huge amounts of complex datasets. There is also a need to evolve algorithms to allow for better learning, reasoning and understanding
Then what are some of the things we may see with quantum computing and AI? Let’s take a look:
Christopher Savoie, who is the CEO and founder of Zapata Computing:
Generative models are those models that don’t just limit themselves to answering a question, but that actually generate output such as an image, music, video, etc. As an example, imagine you have a lot of pictures of the side of a face, but not a lot of pictures of the front of a face. If you want security detection capabilities to be able to recognize dual facial recognition on the front side of a face, you can actually use these generative models very accurately to create more samples of frontal views of a face. Inserting quantum processing units into the classical framework has the potential to boost the quality of the images generated. And how does this help us with classical machine learning? Well, traditional machine learning algorithms are as good as the data you feed them. If you try to train a classical face detection model with a small dataset of faces, this model won’t be very good. However, you can use quantum-enhanced generative models to enlarge this dataset with more images (both in terms of quantity and variety), which can significantly improve the detection model. This isn’t limited to generating faces, you can also generate fake molecules, cancer cells, or MRI scans, which are very similar to the real thing. This allows us to train better machine learning models, which can then apply to real data and real-world problems.
Ilyas Khan, who is the CEO of Cambridge Quantum Computing:
For the first time, a Natural Language Processing (NLP) algorithm is “meaning aware” and has been executed on a quantum computer. When we refer to meaning aware we mean that computers can actually understand whole sentences and not just individual words and that the awareness can be expanded to whole phrases and ultimately real time speech without requiring stochastic guesswork that is the state of the art today and which is computationally so expensive. Full scale implementation is dependent on quantum computers becoming much larger than is currently the case. This development of research in NLP is a prime example of the fact that as realistic quantum computers become available, more use cases will also become apparent. Of course, this has been proven to be the case in the past 30 years on classical computers as a precedent.
Dr. Itamar Sivan, who is the CEO and co-founder of Quantum Machines:
Roughly speaking, AI and ML are good ways to ask a computer to provide an answer to a problem based on some past experience. It might be challenging to tell a computer what a cat is, for instance. Still, if you show a neural network enough images of cats and tell it they are cats, then the computer will be able to correctly identify other cats that it did not see before. It appears that some of the most prominent and widely used AI and ML algorithms can be sped-up significantly if run on quantum computers. For some algorithms we are even anticipate exponential speed-ups, which clearly does not mean performing a task faster, but rather turning a previously impossible task and making it possible, or even easy. While the potential is undoubtedly immense, this still remains to be proven and realized with hardware.
Tony Uttley, who is the President of Honeywell Quantum Solutions:
One of the areas being looked at currently is in the area of artificial intelligence within financial trading. Quantum physics is probabilistic, meaning the outcomes constitute a predicted distribution. In certain classes of problems, where outcomes are governed by unintuitive and surprising relationships among the different input factors, quantum computers have the potential to better predict that distribution thereby leading to a more correct answer. Dr. Hayes states: “The basic idea is that there are problems that require an AI to generate new data that it hasn’t seen before in order to make a decision. Solving this problem may require coming up with an underlying model for the probability distribution in question that it could use in new situations.”
As it pertains to AI/ML, I think what I’m most encouraged by is the potential for classical and quantum to work together leveraging the elastic nature of the cloud and the powerful, specific problem-solving capabilities of quantum computing. I get the sense that a lot of people are looking at quantum versus classical computing, but in reality, it will be the two working together harmoniously to solve challenging and complex problems. Both have strengths and the development now is for quantum computing to function as part of the solution. Over time, both computing formats will continue to advance, but the ability to accelerate workloads on traditional GPUs and ASICs while also leveraging the power of quantum computing is a recipe for faster, more robust results, which is what the market should be eager to see as quantum computing becomes more widely accessible.
For me, I see a few applications for quantum computing in the immediate future that will gain popularity, but of course there will be many more. Financial Services and Healthcare are immediate applications where Quantum Computing can take advantage of speed and specificity to help tackle complexities. Fraud detection and drug compound identifications have been touted as some of the most exciting use cases. Given the current state of cybercrime and the attention to healthcare in the wake of the pandemic, this makes a lot of sense.
