Is Quantum Computing the Future of AI? Posted on : Nov 12 - 2021

Quantum computing has grabbed the imagination of computer scientists as one possible future of the discipline after we’ve reached the limits of digital binary computers. Thanks to its capability to hold many different possible outcomes in the “quantum state,” quantum computing could potentially deliver a big computational upgrade for machine learning and AI problems. However, there are still a lot of unanswered questions around quantum computing, and it’s unclear if the devices will help with the building wave of investment in enterprise AI.

We’ve done quite well with the line of binary computers that first appeared in the 1950s and have evolved into the basis of today’s multi-trillion-dollar IT sector. With just two bits and three Boolean algebraic operators, we created tremendous data-crunching machines that have automated many manual tasks and had a large impact on the world around us. From basic accounting and supply chain routing to flight control computers and understanding the genome, it’s tough to overstate the impact that computers have had on our modern lives.

But as we approach the limits of what classical binary computers can do, quantum computers have emerged with the (as yet unfulfilled) promise of a tremendous upgrade in computational power. Instead of being restricted to Boolean linear algebraic functions on 1s and 0s, quantum computing allows us to use linear algebra upon quantum bits, or qubits, that are composed of numbers, vectors, and matrices interacting in quantum states, including superposition, entanglement, and interference.

Quantum computing opens the door potentially solving very large and complex computational problems that are basically impossible to solve on traditional computers. This includes things like using brute-force methods to guess the passcode used to encrypt a piece of data using a 256-bit algorithm. Data encrypted with AES-256 is considered secure precisely because it can’t be cracked with a brute-force attack (it’s possible, but it would take many thousands of years with current technology, which makes it practically impossible). But with quantum computers’ ability to compute with multiple possible states, solving such problems will now be within practical reach.

Another example is the traveling salesman problem. Given a number of geographic locations, figuring out the most efficient path among them is actually an extremely compute-intensive problem. UPS, which spends billions on fuel for its delivery trucks, has gone so far as to limit the number of left turns its drivers make in an attempt to maximize delivery time and minimize fuel use, making it an interesting twist on the old traveling salesman problem.

Which brings us to machine learning and AI. The latest incarnation of machine learning, deep learning, is pushing the limits of what traditional computers can handle. Large transformer models, such as OpenAI’s GPT-3, which has 175 billion parameters, take months to train on classical computers. As future models grow into the trillions of parameters, they will take even longer to train. That is one reason why users are adopting novel microprocessor architectures that deliver better performance than what traditional CPUs and even GPUs can deliver. View more