Can Quantum Computers Solve Real Business Problems?

In a not-so-distant future, quantum computers with unbelievable processing power will be used to solve some of the most intractable computational problems known to humankind. But can quantum computers be used today to address real-world business problems?   

The answer is yes – but with a catch. To benefit from quantum computing today, you need to find the right problem and address it in the right way on the right kind of quantum computer.

What does this mean, exactly? To better understand, let’s dive into some basics about where quantum computing stands today.

The steampunk chandelier vs. the annealer

If you search Google images for quantum computing, you’re bound to see something resembling a steampunk chandelier.

Such “chandeliers” are comprised of an intricate collection of tubes and wires that culminate in a small steel cylinder. They’re difficult to build, incredibly temperamental, and their qubits — the counterpart to bits in classical computing — must be maintained at almost absolute zero temperatures.

These machines also make a lot of errors and need to be maintained by highly trained, expensive quantum engineers. These requirements are why they’re confined to the lab. According to prevailing estimates, quantum computers of the steampunk chandelier variety may be ready for prime time in about a decade.

Then there are the quantum annealers — which look more like what we’d expect from a computer processor and are housed in “boxes” that wouldn’t seem out of place in an everyday data center.

Quantum annealing computers are commercially viable now. But unlike their chandelier counterparts that eventually will be used to solve a wide range of computational problems, quantum annealers focus on one specific kind of optimization problem known in mathematics as a nondeterministic polynomial (NP) complete problem.

The classic example of an NP-complete problem is the traveling salesperson. Here, the challenge is calculating the most efficient route for visiting customers in, say, 80 different cities and returning home traversing the minimum distance.

Without belaboring the math, the number of possible combinations is on the order of 10²⁰ — which is larger than the age of the universe. For even the most powerful classical supercomputers, this is an insurmountable challenge. Our traveling salesperson — selflessly seeking only to achieve optimal efficiency — would die before an answer is found.

The good news is quantum annealing computers can provide an answer in minutes — a solution we’ve never seen before.

What’s the big deal?

Indeed, few companies would cite the traveling salesperson problem as their primary obstacle to success. However, this may be shortsighted when you consider there are many closely related optimization problems in business that, if resolved, could deliver tangible benefits.

Consider that more than ever, everyday commerce has shifted to the home delivery of goods. Instead of a person (a human intelligence) getting into a car, driving to the box store, buying a coffee machine and bringing it home, we now depend on artificial intelligence to route the truck that gets the coffee machine to the front door — on demand.   

Optimizing logistics for home delivery is only one example. Similar optimization problems include:

• Airline scheduling and planning
• Airline routing fraud detection
• Logistic flow/loading optimization risk profiling
• Pharmaceuticals/new drug discovery/testing
• Financial services portfolio optimization, risk management
• New chemical compounds/catalysts reactions modeling
• Traffic routing risk management
• Haulage scheduling and planning trading optimization
• Constant fixed-charge transportation
• Simple transportation.

If these problems could be solved, we could also solve other issues, such as freeing up the logjams for container ships to enter the Port of Los Angeles or meeting fluctuating consumer demand throughout the COVID-19 pandemic.

But to actually tackle optimization problems like these, you need a quantum computer — and the reality is that today quantum annealers are the only game in town.

Access and partnership

The good news is you don’t have to build a quantum machine yourself. Today, many companies in the quantum annealing space sell access to their systems via the cloud for a relatively reasonable price.

But again, there’s a catch. To benefit from quantum annealing today, you must first get your ducks in a row. Defining your problem is the critical first step. Then, you need to collect the correct data with the right KPIs across a wide range of internal and external areas.

Let’s say the challenge is to optimize the last mile of delivery as described earlier. For such a scenario, you’ll want data on product demand, on-time delivery promises, driver schedules, traffic patterns, weather conditions and much more.  

Using this range of data leaves us with the biggest challenge: How do you build an algorithm to interrogate your data sets? Such an algorithm needs to frame the issue in terms of an NP-complete problem that can take advantage of the nature of a quantum annealing computer – which involves manipulating magnetics fields, managing quantum tunneling and measuring the energy states of qubits. This task requires a high level of expertise.

How to move forward

If you’re seeking to benefit from quantum computing today, the best advice is to seek out a partner with two specific characteristics:

1. deep experience managing sensitive enterprise data at scale for advanced analytics
2. a demonstrated commitment to the science of quantum computing and the resources on hand to prove it

To the extent that modern business has hit a wall in its ability to improve efficiency, increase productivity and optimize operations using prevailing methods, quantum computing — specifically quantum annealing — offers real-world solutions that can be realized today. All you need is the right partner with the right expertise.

For more information or to discuss how technology can help solve real-world business problems, contact us.