Quantum Computing: It’s Early, but There Are Valuable Things to Do Today
Gaining a basic a understanding of quantum computing and where it shines can help create a path to value right now.
Quantum computing (QC) is not a straightforward, simple topic to distill, and it can be difficult to understand how an organization could begin to do something valuable with it. But knowing a bit about the potential use cases, how QC works, and what we can start to do now is valuable.
Before we can think about how QC can provide value, we need to try to wrap our heads around how QC is different from classical computing.
If We Squint, What Is Different About Quantum Computing?
QC is a fundamentally different mode of computation that takes advantage of the quantum properties of nature. The classical computing resources that underpin our world today operate using Boolean logic gates with tiny transistors to manipulate binary information, largely in a linear fashion. QC is different. The information that flows through a quantum computer is not encoded into definite 0s and 1s while it is manipulated by quantum gates; it lives as a probability of becoming a 0 or a 1. It is not until we measure output from a quantum computer that it takes on definite, classical values. QC doesn’t work linearly with one piece of data at a time; it essentially works with all the data at the same time.
Without going too far down the rabbit hole, there are notable differences in quantum information that are worth understanding:
- Qubits, not bits: Information exists as qubits within a QC. Qubits do not have a definite 0 or 1 state until they are measured. They are mathematically represented as a combination of probabilities that are the likelihood of measuring a 0 or 1.
- Superposition, not definite: Qubits live in a superposition of states—that is, they are partially 0 and partially 1 while being manipulated in a QC. This unique facet of nature is what gives a QC its power: the ability to manipulate large amounts of data simultaneously because all possibilities can be encoded in the probabilities of a collection of qubits. The trick is creating quantum algorithms that can tilt, combine, and amplify the probability of the best solution.
- Entangled, not separate: Qubits can be entangled together, where the value of one qubit is connected/correlated to the value of another qubit. This is an essential ingredient in QC that helps achieve scale when exploring a solution space. It is also a key component in error correction within quantum computers.
While it is certainly important to have a sense of how QC does what it does “under the covers,” most of us will interact with QCs via more traditional methods (clouds and APIs). In many ways, you can think of our future with QCs similar to the way we work with GPUs today: as specialized hardware that is abstracted from us to achieve better performance on a specific type of task (a QPU!).
A Few Things about Quantum Computing We Should Say Out Loud
A quantum computer will make everything faster, right? No, sorry. QCs excel at solving some very specific types of problems where the scale of the problem can make obtaining a reasonable solution intractable with current classical computing methods. Quantum computation methods do not fit all problem spaces, and there is no reason to abandon our friends the classical computers (for a while, anyway).
Since a quantum computer can operate on all the input data at once, it just gives us all the possible answers at once, right? No. But even if it did, you would still need to classically search through all those answers to find the right one and that would negate the value of using QC. While it is true that QCs operate on all inputs at once (that scale is part of their charm), the job of many quantum algorithms is to be able to recognize the best solution and amplify the probability of measuring that answer. This is quite different than classical computing, and frankly, it takes a few minutes to wrap your head around it.
Quantum computers are pricey and I just don’t need one. At this time, owning a quantum computer would be expensive, but not unlike the fact that you don’t need to own a rack of servers, there are many software packages you can work with directly from your laptop to understand the basics, experiment, and even run code on real quantum computers via cloud/API access. Many quantum use cases are applicable across industries, so if you dig deep, you will find something that can be optimized.
Now that we have heard a little about how QCs are different than their classical counterparts, let’s talk about some use cases where QC could improve outcomes.
Give Me Use Cases. Where Can Quantum Computing Help?
Quantum computing has sweet spots. There are classes of problems where QC could provide a significant speedup when the scale of the problem starts to exceed the limitations of our current classical computing, and those kinds of problems show up across many different industries:
- Chemistry and materials science: Simulating chemistry at the molecular level is an extremely difficult task using a classical computer as the number of variables that need to be tracked is so immense that classical computing is quickly outpaced. QC, by its very nature, can scale to meet the demands of chemical simulation and could provide insights that can lead to advanced discoveries in medicine and new materials, especially when combined with current techniques in machine learning.
- Supply chains and logistics: Optimizing routes, scheduling, inventory, and planning are dynamic problems that are difficult for classical computing methods to solve easily. Optimization, in general, is a class of problems where QC is well-positioned to generate value.
- Financial services: Risk analysis, modeling, and portfolio optimization are all complex optimization problems where QC can improve outcomes.
- Machine learning and artificial intelligence: Many of the problems in training AI models are optimization problems and are well suited for QC. Quantum machine learning is an active field of research as there is immense value to everyone in the ability to encode and operate on large amounts of training data and reduce training time and the energy it consumes.
These are a few samples of use cases where QC can generate impact in the near future. There are certainly more. The TL;DR is that QC shines where it has something to optimize.
More broadly, over time, QC will have impacts on security, climate change, and geopolitics.
Are Quantum Computers Ready for My Prime-Time Use Cases?
There is plenty of investment moving forward in QC, both in the US and across the world. No question that the industry is growing and moving at a fast pace. Cloud providers all have roadmaps for QC and offer cloud-based access to various quantum hardware back ends. In addition, there are many QC companies that offer access to their hardware and software right now, and you can start experimenting as soon as you finish reading this article.
But with one caveat — we are still in the NISQ era. NISQ era? Noisy intermediate-scale quantum hardware. It turns out that pushing qubits around (i.e. quantum information held in superposition) is not quite as simple as moving classical bits. In fact, it is downright tricky. Qubits have a tendency to interact with any stray anything in their environment, destroying the quantum information (a problem known as decoherence) and creating errors during computation.
Right now, the hardware industry’s focus is on achieving scale and fault tolerance (i.e. error correction). Many quantum algorithms today are designed for the NISQ era, using quantum hardware where possible and mixing in classical methods as needed.
There are big benefits to the NISQ era. The development of hybrid quantum-classical algorithms is helping companies understand how to tackle real-world scenarios today and is fostering valuable dialog between business and academia. The NISQ era helps us understand how to incorporate QC into our organizations and helps us focus on creative ways to deliver real value to businesses with quantum computing.
So what Can Be Done Now?
Create a Team
Similar to the challenges many organizations face today in managing talent in AI, quantum will be no exception. There is certainly a talent gap in quantum computing; however, building a cross-discipline team is a key to success in implementing any new technology and generating a return on that investment. While you will need an expert or two in QC (people who understand the algorithms, have used the tools, and know the industry), you will also need team members from software development and cloud, and people who know how things actually work day to day. While QC knowledge is important, many of today’s AI experts have competency in computer science, stats, probability, and linear algebra—all valuable things to form a foundational understanding of how to use QC.
Soft skills and business acumen matter. Building a team whose job is to dig deep into the organization’s hardest computational problems and determine if they can gain advantage now or sometime in the near future is not a trivial task. You need good collaborators, technicians, and solid executive support.
Partner to Find Opportunity
While most business leaders may not be interested purely in quantum mechanics and discussions of gates vs. annealing, it’s the opportunities to improve supply chains and delight customers that move us toward creating value.
We need to be clear on what a quantum team is doing: it’s experimental, speculative discovery work, and it does not follow a traditional linear path. There will be dead ends, and there may not be a clear path to advantage…yet.
However, investing time in discovering the organization’s biggest opportunities for improvement by rethinking existing workloads or optimization points that lead to better outcomes for customers is not a wasted effort. You may discover quick wins or things that can be improved with other methods (new architecture, AI/ML, better data pipelines, etc.). Business knows where the pain points are and where they would like to see improvements. Building trust on quantum will be easier when business partners understand that the team is there to help drive improvements, regardless of how those improvements are implemented.
Set the Foundation
The real cost of bringing new technology into any organization is not just the cost of the technology itself; it is the integration of the technology into the current IT infrastructure and the cost of updating processes to support it. Many organizations today are struggling to integrate AI into their existing ecosystems: how to manage part of a software product that may be deployed, trained, and monitored for drift separately from the traditional app codebase.
QC will pose a similar challenge. Developing a foundation based on automation and SRE principles will provide value now and lower costs in the future, regardless of when quantum is adopted. Similar to AI teams, a quantum team needs to view the problem and solution holistically. The real quantum advantage is achieved when a quantum workload is deployed to production, is fully supported operationally, and has a measurable impact on the business.
The future is bright for quantum computing! The industry continues to grow by leaps and bounds weekly. It is an incredible time to begin to discover how it can improve our lives, our businesses, and the world at large!
