While in its infancy, the role of quantum computing in the enterprise has tremendous potential and implications.

CTOs and CIOs in companies large and small are aware of Moore’s Law and its implications for how computational power tends to double every two years, explaining how processors can grow smaller and smaller while delivering more power, at cheaper rates.

But even as advances in conventional computing continue, there is a mismatch between today’s CPUs and their problem-solving capabilities, with newer problems that demand much more complex programming to work through. What’s called for in tackling complicated problems is a shift in how we design and build the computers themselves.

This is where quantum computing comes in, as a potential means to manage extremely large amounts of real-time data in parallel, which is impossible to achieve with today’s ordinary computers based on binary calculations.

Quantum Computing Basics

Quantum computing began as an idea put forward by physicists Richard Feynman and Paul Benioff in the 1980s, according to Los Alamos National Laboratory. LANL obtained its first experimental quantum computer decades later in 2015 and has updated it in 2019 so theoretical physicists can test its potential for solving intense, high-energy problems.

Put simply, quantum computing is a more complex method of computing than is possible with classic computers. Conventional computers rely on binary bits of “1” and “0” to represent data being manipulated. But quantum computers harness two properties of quantum mechanics: superimposition and entanglement.

Superimposition involves using “qubits” which can represent a “1,” a “0” or a combination called “superimposition” of the possible states of the qubits. With a computer that has n qubits, people have 2n possibilities superimposed, allowing for an exponential set of states to use for solving complex problems, according to IBM.

Entanglement means that two qubits can be located far away (such as across the solar system) but are still able to behave in a way that their actions remain correlated. The implications for computing are much faster calculations with states of qubits in superimposition.

For comparison, an algorithm that would take 330 years to solve running on a classical computer would take 10 minutes on a quantum computer, per IBM.

Quantum computers are still in their infancy, but now is the time to get ready to use them in the enterprise. We’re getting closer to full-fledged quantum computing now, since Google published a report in Nature on October 23, 2019, that it had achieved quantum supremacy.

Google said its quantum computer conducted calculations that the world’s best classical supercomputer would need 10,000 years to finish. Quantum supremacy represents a milestone in showing quantum computing is actually possible now and can out-compute the current state-of-the-art computers built by conventional means.

Keep in mind that while quantum computers are more complex than classic computers, you won’t always expect to get a specific answer to a question. Rather, you will get a range of possible answers. So quantum computers are not expected to completely replace classic computers.

McKinsey explained that “Instead, quantum computers will be used for different kinds of problems, incredibly complex ones in which eliminating an enormous range of possibilities will save an enormous amount of time.” This feeds into applications such as longer and more precise weather forecasts, pharmaceutical drug discovery, more efficient cybersecurity, and any situation where complex calculations are hampered by older fashioned, linear computing.

Quantum Computing Capabilities for Business

It’s useful to think of the four main capabilities of quantum computers to see how they will have a place in the enterprise, in terms of simulation, optimization, artificial intelligence, and prime factorization.

•  Simulation: The pharmaceutical industry provides a good example of quantum computing for simulation. Classical computers cannot simulate in great detail molecules, even those with just a few atoms, let alone proteins, which have thousands of atoms.

Lacking computer processing power, scientists will instead synthesize molecules to study and measure their various characteristics. During iterative research, it will become very expensive and time-consuming to keep making new synthetic variants. But when modeled using qubits, there’s more horsepower and researchers can work on virtual molecules to get faster results.

• Optimization: Many enterprises could use help with optimizing their processes, but cannot do so readily with ordinary computers and software. Quantum computing to provide a range of answers very quickly will make it easy for, say a ground transportation services company to allocate its fleet more efficiently.

How can dispatchers determine the shortest route for delivery drivers? Or what can a portfolio manager do to optimize the risk and performance of stocks? McKinsey noted these types of optimization, made possible with qubits, will speed up problem-solving.

• Artificial Intelligence: Automobile manufacturers are developing vehicles with autonomous capability, but still in a very limited fashion, with truly self-driving cars still on the horizon, pending testing and regulations to protect the public. Autonomous vehicles are a prime candidate for artificial intelligence development.

Since there are so many complex options and multiple sources of data streaming in to control a car’s operation, classic computers cannot handle the information. But quantum computing to crunch through the torrent of driving data will help cars learn how to avoid hitting people and obstacles and to stay in their lane.

• Prime Factorization: Prime numbers are an essential component of modern cryptography. Longer prime numbers multiplied against each other make for longer strings of code that are harder for serial-processing, classical computers to calculate. Quantum computing in the enterprise environment would usher in a safer and more secure era of data collection, storage, and use.

Business Applications of Quantum Computing

As you and fellow stakeholders consider preparing your enterprise for quantum computing, it’s useful to review its potential applications in such sectors as energy, finance, insurance, logistics, and transportation, to get a better idea of the role it will play in businesses.

* Energy: Energy companies have a hard time predicting usage, which as it increases, in turn, drives the development of production capacity. The energy industry also has an interest in optimizing its existing networks. To that end, ExxonMobil has partnered with IBM on quantum computing, noted Towards Data Science.

• Finance: There are many applications for quantum computing in the financial sector, from managers optimizing their portfolios to determining the price of assets or predicting how the market will move. Analyzing risk and detecting attempts at fraud are other examples of complicated computing that banks such as JP Morgan Chase and Barclays have partnered with IBM to solve.
• Insurance: In the world of insurance, qubits may be routinely harnessed to assign values to bonds, derivatives and other instruments. Insurers could use quantum computing machines to quantify operational risk levels for customers. For example, Towards Data Science reports that 1Qbit is teaming up with insurance company Allianz to experiment in this regard with qubits.
• Logistics: Large supply chain networks are notoriously unwieldy, with classic computing struggling to keep up with data demands. Now, sales giant Alibaba is reportedly working on its own quantum computing hardware to reduce supply issues.
• Transportation: A company with a large number of vehicles in its fleet will find it difficult to optimize their usage with off-the-shelf classic computer technology and software. Since quantum computing is made to quickly provide answers (a range of answers) to complex problems, major companies from Daimler to Ford to Volkswagen are starting to look at quantum computing to help them scale up more efficiently.

What’s Needed Next

While quantum computing is still in its infancy, work continues apace with companies and governments across the world endeavoring to attain a measure of quantum supremacy, after Google revealed its first milestone in 2019. As Deloitte noted, we will need new hardware engineering, so computers can be built with chips that can keep qubits stable at least 10 times longer than what’s currently possible.

And developers will need to create new software as well as algorithms to take advantage of qubit computations. It’s going to be an exciting period that companies need to start preparing for sooner rather than later, trying to play catch-up with more forward-thinking firms.