When will the Quantum Data Center Arrive?

The future of Big Data and Quantum Data Centers.

Hey Everyone,

This will be a somewhat rambling and speculative Op-Ed.

2022 was a pretty exciting year for Quantum computing and innovation. Quantum Foundry published quite a few articles about startups, partnerships and a whole lot of news in the industry.

If Quantum computers that are scalable come about, we’d have basically a Quantum internet, Quantum supercomputers and Quantum data centers.

IBM thinks by 2025 we will have effectively removed the main boundaries in the way of scaling quantum processors up with modular quantum hardware and the accompanying control electronics and cryogenic infrastructure. IBM however is know for puffy marketing, IBM Watson comes to mind.

Last November, 2021 the IDC published its forecast for the worldwide quantum computing market that projects customer spending for quantum computing will grow from $412 million in 2020 to $8.6 billion in 2027.

Meanwhile as LLMs challenge demand for compute, major Cloud players are also going to bolster their quantum computing efforts in 2023 to anticipate a world where scalable Quantum computers will exist, whether that’s in 2028, 2033 or 2035 nobody knows for sure. Enterprise leaders like IBM, Microsoft, Amazon and Google continue to make progress in quantum computing.

Quantum Data Centers could in theory also reduce their carbon footprint. Few people are aware of this but Data centers represent a massive drain on our world’s energy resources and are a major source of greenhouse gas emissions. These computing hubs produce 200 million tons of CO2 annually and consume 2% of electricity worldwide, according to Accenture, which projects that figure will reach 8% by 2030.

We may need Quantum computing to scale to build a better world. It’s not clear what aspects of computing scalable Quantum computers might complement, replace or improve upon. One of the fields I’m most excited about is Quantum Machine Learning, or QML.

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning.

Quantum computing is elegant because it envisions a world where we have evolved our fundamental paradigm. Superposition and entanglement are concepts that drive quantum computing's appeal because they increase potential computing power, in contrast to the way that on/off or one/zero states define classical computing.

What happens when we unlock the Quantum gates of a new paradigm of computing? What will happen to how we process Big Data, how the encryption works on the old internet and so forth? There is likely to be an exponential increase of computing power with every qubit added to the computation that exploits the properties of superposition and entanglement - but it’s difficult to predict what happens to our world in that prism.