Should you be thinking of Quantum Computing?

Should you be thinking of Quantum Computing?

The short answer is, depends. If your organization is dealing with Deep Learning, Machine Learning, complex simulations or optimizations, you should care. Quantum computing is one of those technologies that we get hyped, look into them, frown in disappointment and then dismiss. The truth though, is that you shouldn’t. Not now.

In theory, Quantum Computing enables companies to run hard (exponential based) problems orders of magnitude faster than current technologies. I say in theory because in most cases, the mathematical algorithms aren’t there yet. That said, this is changing and fast.

When I mean fast, I mean exponentially fast. Some weeks ago, Microsoft released its Quantum Computing Toolkit. IBM released something similar last year called IBM Quantum Experience (IBM-Q), becoming the first company to offer Universal Quantum Computing in the cloud.

The news caught my attention. It surprised me that more and more technology companies are releasing Quantum simulators. I wondered, isn’t it far away from being useful? The truth is, it is, and it’s not. So let me separate two things.

Quantum Computers

On one side you have the Quantum Computer itself, the hardware. The speed of innovation on the hardware side is impressive. Right now there might be close to nine or ten different approaches to building a Quantum Computing. Some are very recent, like the Flip-Flip Qubit proposed by the University of New South Wales in Australia. Others are improvements over current technologies, like the Loop-Based technique from the University of Tokyo.

Hardware is still evolving. It reminds me of the early days of digital computers. Each company is outperforming the other’s architecture. The significant difference, in this case, is the speed of innovation. The acceleration of the space will bring forward a viable (like in 1000 – 4000 qubits) Universal Quantum Computer during the next few years, not more.

It’s easy to dismiss the technology as it’s currently subpar with traditional computing. There is an ongoing debate about how faster can Quantum Computers operate. A discussion that, so far, Quantum has been loosing. I don’t expect this to be the case for long though.

IBM's Quantum x2000 chip

Image: IBM’s Quantum x2000 chip

Quantum Algorithms

On the other hand, you’ve got Quantum Algorithms. This is the software abstraction that runs on top of the Quantum Computers.

Writing Quantum Algorithms is nothing like current programming. It’s the comeback of assembly language, but on steroids, it’s a trip down Universal Turing Machine memory-lane.

Quantum Computing requires a complete rewrite of the underlying math of any classical algorithm. Not all algorithms are suitable to run on Quantum. Quantum developers need to develop new mathematical devices to make them workable. And when I say Quantum developers, I mean, hardcore mathematicians and physics.

It all comes down to developing the right Quantum algorithm, something that isn’t easy or achievable by many. Here though is where the exciting space lies. Most technology leaders are investing in building their own Quantum Computers. Meanwhile, startups are focusing on developing the right algorithms for potential customers. One example of this is the Vancouver-based 1QBit.

In 2014, two Singularity University alumni, Landon Downs, President and Andrew Fursman, CEO co-founded 1QBit. Their goal? To bring the right Quantum algorithms to solve intractable problems. Their clients? Financial institutions like Dow Jones, Pharma companies, Technology moguls like Fujitsu, AI-heavy companies, etc.

Their focus is on developing the Quantum algorithms to solve expensive computational problems. Developing these takes time and effort, which is why it’s so important to start doing it now.

In a way, the fact that both IBM and Microsoft are encouraging developers to play with their Quantum languages is for a reason. There aren’t enough people qualified to be Quantum developers, and the need is becoming very real.

Quantum for what?

Three critical spaces are the ones driving the field. The obvious one is cryptography. Our current infrastructure’s security relies on Public-Private cryptography. Behind it, there is one of the toughest mathematical problems, which is the factorization of prime numbers.

In 1994, Peter Shor, an American professor of Applied Mathematics at MIT, developed a new algorithm to factor prime numbers called the Shor Algorithm. The new algorithm took advantage of the way Quantum Computing works, achieving considerable speedup times. It wasn’t until 2001 that someone attempted to put it in play with a real Quantum Computer. Fast-forward to 2014, scientists have already achieved the factoring of a six digit number.

While still some years away, everyone is expecting a breakthrough in no time. Such is the pace that the National Institute of Standards and Technology (NIST), the organization in charge of validating our most used cryptographic algorithms, is already talking about post-quantum cryptography (PDF).

But crypto, while important, is the tip of the iceberg. Artificial Intelligence, but more specifically, Machine Learning and Deep Learning algorithms, are becoming ubiquitous too. These algorithms need, not only massive amounts of data but tremendous computational speed. Such is the need that the industry is fine-tuning their chip designs to supply even fastest training capacity to their customers.

It’s not about who uses AI or not anymore. It’s about who can re-train their models faster.

The quest for fast Deep Learning training is pushing the investment on Quantum Computers too. It’s not about who uses AI or not anymore. It’s about who can re-train their models faster.

So far, the inroads into Quantum Deep Learning haven’t been much. The underlying mathematics behind most Artificial Neural Networks don’t play well with Quantum Computation. This is changing though, and quickly.

Last but not least, optimization problems, for example in the logistics and operations industries, will also benefit from it. Calculating the perfect route to transport goods, with the least cost, is still a costly problem for classic computers. There are traditional optimizations, but they’re sub-optimal. As more companies go into e-commerce or ride-sharing services, being able to slash costs in logistics is becoming critical.

If we add Autonomous Vehicles (AV) on top of this, the picture starts becoming clear. AV requires both, faster Deep Learning algorithms, but also better-optimized routes. Both problems Quantum Computers should be able to assist within a few years.

Conclusions

Quantum Computing isn’t for everyone. It’s only suitable for some mathematical issues. For those that are suitable, it will allow faster and more powerful computations. While the hardware isn’t there yet, it’s evolving at an exponential rate. The bottleneck isn’t with the hardware per se, but on the capacity to develop the right Quantum Algorithms. The development of such algorithms isn’t trivial and requires extensive mathematical knowledge. Something that isn’t common.

Those organizations that start training their people in this space and start focusing on their own Industry Quantum Algorithms will gain a massive competitive advantage during the next five to ten years.

As a side note, I wonder if the current Deep Learning models can’t be applied to the task of developing new Quantum Algorithms. Just a final thought to get your mind reeling.

If you enjoyed this post, please share. And don’t forget to subscribe to our weekly newsletter and to follow us on Twitter!

Introducing The Aleph

Introducing The Aleph

The Aleph is born out of a personal frustration with how most technology news gets reported. I love technology. I love the empowerment technological advance delivers.

Still, most of what we write about it tends to remain on the surface. We record the facts, but not the implications. We talk about immediate impact, but can’t see beyond the actual product or service.
 
In an ever-increasing short-sighted society, it’s important to cultivate a broader understandingAt The Aleph we’ll try to bring such perspective. We’ll strive to connect the current innovations with their impact across a broad set of industries.
 
We want to be a source of critical and systemic thinking about innovation. We want our readers to understand how much disruption-risk certain technologies are generating.
 
In the end, we want to inspire our readers to think beyond the news horizon and start thinking strategically about their future businesses.
Some analysis will touch on the immediate impact, but others will project further into the active threat in the mid and long-term for all kinds of organizations.

Why The Aleph?

The Aleph or Alef (א) is, strictly speaking, the first letter of the Hebrew alphabet. According to the theological treatise Bahir (“Book of the Bright“), it’s,

“The primordial one that contains all numbers.”

It’s also the inspiration of one of the most famous short stories by José Luis Borges, El Aleph,

“He explained that an Aleph is one of the points in space that contains all other points. […]
In that single gigantic instant, I saw millions of acts both delightful and awful; not one of them occupied the same point in space, without overlapping or transparency. What my eyes beheld was simultaneous, but what I shall now write down will be successive, because language is successive.”

— José Luís Borges, El Aleph, September 1945.

We drank from such inspirations to define our core values. The Aleph Report, the one point in space that tries to contain all other points; all the ramifications and implications of one concept.

Like the Aleph, we can only write about it in succession, but we’ll try to bring the systemic view to everything we cover.

Who will do it?

I will be the primary voice behind The Aleph. During the past decade, I’ve been working hand in hand with cutting-edge technology companies all across the globe. This experience enables me to watch, first hand, the effects of disruption in many industries.

How often?

The idea is to find the best balance between news, analysis and opinionated voice for all.

We will publish a weekly post with the analysis of the current news and how it affects specific industries. Some weeks we might post more than one, but we’ll keep it pretty light at the beginning. In the future, we will start releasing our reports on a daily basis to our subscribers, so stay tuned.

This is why any feedback will be more than welcome. Please don’t hesitate in sending any comments or topics you want to see covered.

How do I read you?

We’ll be posting our weekly analysis on this blog each Monday. Also, we’ll run our weekly reports through our newsletter, so make sure you subscribe and that you follow us on Twitter, LinkedIn, and our RSS.

Want to contact us? Feel free to ping us at weekly AT thealeph com.