Today, at TechCrunch Disrupt in San Francisco, I showed a simple machine learning demo, which I ran live on a real quantum computer in New York, through the cloud. Sure, that problem could just as easily have been solved using a classical algorithm on your laptop. But wouldn’t that have been a lot less exciting?
IBM quantum computer
I continue to be amazed by how much progress has been made in a short time; just a few years ago, the very thought of this would have been just a dream. Today, it’s still early days for quantum computing. But systems are getting better and better – and relatively soon, we’ll be in uncharted territory, where we can no longer simulate what the systems are doing. From there, it’s just a matter of time until we start solving at least certain kinds of problems better than we can now using today’s classical systems.
Getting to a future where quantum computers break new ground will require the collective talent and contributions of many brilliant people. If you are excited about this too, then get involved. Whether or not you know it, you have something important to contribute.
In a new preprint now on arXiv, “A Threshold for Quantum Advantage in Derivative Pricing”, our quantum research teams at IBM and Goldman Sachs provide the first detailed estimate of the quantum computing resources needed to achieve quantum advantage for derivative pricing – one of the most ubiquitous calculations in finance.
What does programming for the not-so-distant quantum future look like? From November 9 to 30, more than 3,300 people from 85 countries applied for the 2,000 seats of the IBM Quantum Challenge to find out. As our cloud-accessible quantum systems continue to advance in scale and capability with better processors of larger number of qubits, […]
As we looked closer at the kinds of jobs our systems execute, we noticed a richer structure of quantum-classical interactions including multiple domains of latency. These domains include real-time computation, where calculations must complete within the coherence time of the qubits, and near-time computation, which tolerates larger latency but which should be more generic. The constraints of these two domains are sufficiently different that they demand distinct solutions.