It goes without saying that quantum computing is complex. But people buy extraordinarily complex things through simple processes every day. After all, few smartphone buyers know how their devices work. Even a humble bar of soap makes it to the shelf only after the raw materials are mined, refined, manufactured, packaged, shipped, and stored.
The question is: will the complexity of quantum computing ever be limited to the point where end users can buy it “out of the box”? The answer is: that depends on what you mean by out-of-the-box.
If you’re envisioning something like a Quantum MacBook that you might find on the shelves at Best Buy, that’s unlikely to be the case. For most users, it may never be worth having your own on-premises quantum computer. Unless you are a large, well-funded organization in a unique position to benefit from exclusive access to a quantum device, such as a government entity or a major financial institution, quantum computing resources will always be more practical to access through the cloud. .
On the other hand, if you’re envisioning a digital marketplace for quantum-powered apps, that could very well become a reality in the next two to five years. But just because you can download some quantum software doesn’t mean that it will instantly provide an advantage over classical computing, or even that it will be useful at all.
Some readers may have already seen this with “out of the box” AI solutions. While there are many commercial AI solutions available on the market, these solutions do not provide an advantage without some level of customization. For example, consider the similarity of all the automated chat bots you come across while browsing the web. These chatbots have become table stakes, not perks.
In general, the less customization required to make an out-of-the-box solution work, the less likely it is to provide an advantage that a competitor could not as easily install. Every organization will have a unique mix of data, IT infrastructure, teams, and problems to solve. Any useful algorithm will need to adapt to that unique environment to have an impact. This is true for AI and it is even more true for quantum computing.
Quantum applications demand dedicated expertise
Now, some quantum use cases will lend themselves more easily than others to out-of-the-box applications. Several classic optimization solutions are already available out of the box, such as Gurobi other CPlex, and it’s no stretch to imagine quantum-powered versions in the future. Although optimization use cases vary widely, they can all be mapped to well-known mathematical formulations, such as a mixed-integer programming problem. However, a domain expert is still needed to understand which variables or constraints should be prioritized. A technical expert is also needed to turn business problems into mathematical problems that a software solution can solve, and then modify the software for best performance.
Any advantage of off-the-shelf quantum software will depend on having a specialized team that can tailor the software to a company’s unique problems. This includes both experts in quantum computing other experts who deeply understand business issues. It may seem like you can wait until the software is fully developed to start hiring quantum talent, but unfortunately the talent pool is rapidly shrinking. In our recent survey On enterprise quantum adoption, we found that 69% of enterprises have started down the quantum adoption journey, and 51% of these organizations have already started assembling their quantum teams. If you wait too long, the best minds will be gone.
You’ll also want to foster relationships with outside consultants. The executives we surveyed agreed: 96 percent said they couldn’t successfully adopt quantum computing without outside help. Outside consultants can save you time and energy by helping you identify use cases, anticipate roadblocks, and build the software infrastructure you’ll need to effectively harness quantum computing.
Building the infrastructure for quantum computing
Quantum computing will never exist in a vacuum, and to add value, quantum computing components must integrate seamlessly with the rest of the enterprise technology stack. This includes HPC clusters, ETL processes, data stores, S3 buckets, security policies, etc. Classical computers will need to process data before and after it is run through the quantum algorithms.
This infrastructure is important: any acceleration of quantum computing can easily be offset by mundane problems like disorganized data storage and suboptimal ETL processes. Expecting a quantum algorithm to provide an advantage with shoddy classical infrastructure around it is like hoping a flight will save you time when you don’t have a car to take you to and from the airport.
These same infrastructure issues often arise in many current machine learning (ML) use cases. There may be many standard tools available, but any useful ML application will ultimately be unique to the target of the model and the data used to train it. You need a streamlined process for preparing and cleaning data, making sure data complies with privacy and governance policies, tracking and correcting deviations in the model, and of course making sure the model does what you want it to do. .
As enterprise ML users know, maintaining these applications is an ongoing process. Ideally, you would have a development environment for prototyping, a staging environment for testing, and then a production environment for scaling the model for enterprise use, leveraging HPC and cloud resources. The complexity associated with building and deploying ML applications in production required the creation of the field of MLOps (also known as AIOps) to manage this complexity.
The complexity only multiplies when you add quantum computing, which requires a similar “QuantumOps” process to manage the complexity and make it useful in production. Quantum hardware is evolving rapidly, and to keep up, you’ll want a way to compare the performance of new quantum hardware backends as they come out to ensure you have the best setup for your problem. The last thing you want is to spend millions developing a quantum application, only to have a new device or software component render your work obsolete. Having an environment that gives you the flexibility to tune your models, try different configurations, track and compare changes, and quickly iterate will be critical.
A future out of the box?
In the future, quantum computing may be as invisible as the processor running the device you’re reading this on now. Quantum apps can be as easily accessible as your internet browser app or email app.
But accessible is not the same as useful.
To gain a significant advantage from quantum computing, you need to lay the groundwork by building the necessary equipment and infrastructure. Although fault-tolerant quantum devices are still years away, companies can build their workflows well in advance and trade in these more powerful backend devices once they come online.
Ultimately, every business will have unique challenges that will require unique quantum applications. Business-to-business applications may be similar, but any quantum advantage will depend on tailoring the quantum application to the needs and capabilities of the business. This is in direct contrast to the idea of an out-of-the-box quantum app, as appealing as that sounds.
Jhonathan Romero Fontalvo is Founder and Director of Professional Services at Computer Zapata.