The Future of Quantum in the Cloud
Five years ago, IBM was the first to make a quantum computer available over the cloud. The approach made sense instantly. These are rare, expensive machines and incredibly temperamental. It didn’t seem wise for potential users to consider investing in owning one. IBM kept going with the approach – today, anyone can access about half of IBM’s 20 quantum machines for free on its expanded cloud environment. But not every system manufacturer wants to create a cloud, and not every potential quantum customer wants to access different clouds for each task.
Microsoft and Amazon kicked off a new movement by creating cloud environments that let users access quantum computers from multiple hardware providers. In the case of Microsoft Azure Quantum, users can run code on real quantum systems from Honeywell, IonQ, and Quantum Circuits, Inc. What are the benefits of this approach? What can we use these machines for in the near future and beyond?
Cloud quantum development
These cloud services are much more than portals to advanced quantum machines. Microsoft Azure Quantum and Amazon Braket are complete ecosystems with reusable code and APIs that allow for interfacing with standard or classical applications. The cloud giants can concentrate on providing these rich cloud development platforms and secure, easy access. In turn, companies like Honeywell and IonQ can focus on what they do best: building and improving their quantum computers.
Classic computers are not going away, and quantum computers will not exist in a vacuum. Expect classic and quantum computing to live for the foreseeable future as a hybrid pair, with classic systems passing tasks on to quantum ones whenever the latter can provide a measurable edge and cost-savings benefit. Therefore, it makes sense for users to access quantum computers in the same cloud environment where they perform classical workloads. This way, developers can create applications where classic and quantum hardware interact on the back end seamlessly.
Such a usage approach is not that different from today’s tiered cloud infrastructure. For example, systems with expensive GPUs are only used in the cloud when they can assist with tasks such as machine learning.
Using all this power today – and considering future options for growth and real-world applications
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