Investment Thesis — D-Wave

For my first investment thesis that I’m publishing here on Medium, I thought I might as well start with what is probably the most outlandish (or otherwise most risky) position in my portfolio. I call it outlandish because my confidence in the company D-Wave (QBTS) is largely based on my philosophical beliefs behind the technology. It is risky because, in an emergent market, it is a company with very poor fundamentals. On such shaky ground, I figured this position would be perfect to begin this project of documenting the thinking behind my investments because the structure of this thesis will likely be just as shaky. I’m not sure who the audience of this document would be or if there will be an audience at all, but I do enjoy talking and thinking about investment strategy and diving deep into the companies and industries that I enjoy, and they say journaling is good for you. So here I go.

The Fundamentals

I want to get the elephant in the room out of the way. The company doesn’t have much in the way of intrinsic value. At the end of 2023, the company had $72,651 in debt, with assets that amount to a bleak $59,356. In previous years, the story wasn’t any better. The company was bleeding cash. Needless to say, when I first started investing into QBTS around Q3 of 2024 (at an average cost per share of $0.90), no one seemed to take this company or any other in this industry seriously. No analyst covered them, and you couldn’t blame them. With that said, my confidence in this company is purely speculative. My initial thinking behind the cost basis that I was willing to risk was such that as long as they can survive the hemorrhage of cash long enough for customers to innovate on their platform, I would see some kind of profit.

Luckily for my modest and burgeoning portfolio, the bet paid off much earlier than I thought it would. The story behind QBTS got better at the tail end of 2024, with 2 events that stick out to me.

• The first event — Around October of 2024, a Chinese research firm used the D-Wave quantum computer to factor a 22-bit RSA integer. An RSA key is a method of encryption that is most commonly used for securing data. What makes it an effective encryption method is that it takes a prohibitively long time for conventional computers to factor very large numbers. By itself, this feat doesn’t mean too much because we typically use 2048-bit integers. What I (and probably many other investors) see here though is the first spark between the rocks used for the invention of fire. From what was before a purely theoretical project, this demonstration proved what the technology might be capable of.
• The second event — Around November of that same year, D-Wave announced the sale of their on-prem flagship quantum computer to the German research company, Forschungszentrum Jülich, that operates an HPC supercomputer. This sale increased the bookings for the company by about $23 million. This influx of cash provided the company with liquidity to settle debts, reinvest into R&D and most importantly it buys the emerging development/engineering community time to innovate.

The company isn’t out of the woods yet. These events just provided the company with much needed extrinsic value and time. By the end of 2024, with a surge in extrinsic value, QBTS would close at $9.14/share from its $0.80/share lows just 2 quarters prior. In 2025, the hype machine would take the company to the uncomfortable highs of $45.00/share. At this peak, I sold a small portion of my position in the company and realized a profit of 40x my initial investment. Fundamentals be damned. I maintain the larger share of my position in QBTS because I see greater potential in the technology.

The Technology

D-Wave wasn’t the only company that benefitted from the rally in the quantum computing industry. There were a few other quantum computing “pure plays” like IonQ (IONQ), Regetti (RGTI) and Quantum Computing Inc. (QUBT). Not to mention the much larger incumbent companies that invested in their own projects like Microsoft, IBM and even nVidia. They all carved out a piece in this emerging industry, distinguishing themselves by methodology and applicability. The predominant methodology is the gate-based quantum computer. This approach is an attempt to build universal quantum computers. They are universal in the sense that they are designed to compute any kind of problem that can be written to the hardware. While D-Wave has a gate-based quantum computer, they specialize in what is called an annealing quantum computer. This approach is not taken by any of its competitors and the company owns many of the patents surrounding the method. Along with years of R&D, these factors create a moat around the company, protecting its value from competing firms.

The annealing quantum computer is designed for optimization problems. It works much differently than a gate-based method and has fewer obstacles for usefulness and commercialization. Being first-to-market is a compelling reason to favor D-Wave in this technology race, while they are selling computers to customers that will figure out how to use them their competitors are still deep in the R&D phase. Additionally, once a gate-based approach does expose itself they will be in a position to quickly adapt for their existing customers. Despite these factors however, I believe that the most compelling reason to look at D-Wave is a philosophical one regarding just how annealing works. The easiest way to gain a conceptual understanding of just what these calculations do and why they excel at optimization problems is to briefly turn to the physics.

Typically, when someone needs to predict various characteristics about the motion of objects that move from one point in space to another, they use Newton’s laws of motion. In 1744 however, two physicists independently derived another means of performing such calculations, Pierre-Louis Moreau de Maupertuis and Leonhard Euler, on the basis of Pierre de Fermat’s principle of least time that was introduced less than 100 years prior. The reason why Fermat published this work was because he needed a way to formally describe why rays of light behave the way that they do. This new means of calculating motion is called the principle of least action. Unlike Newton’s laws though, the principle of least action can be used to describe the motion of elementary particles as well. This principle was later described by the famous physicist Richard Feynman, among others, as nature’s optimization algorithm. It is described so because the principle formally describes the process by which nature uses the minimum resources, so to speak, necessary for an object to move from one point in space to another while obeying the laws of physics.

What the annealing method of quantum computing does is introduce nature’s optimization algorithm to computational workflows.

Conclusion

It is no surprise to me the skepticism around this technology for annealing in computation. It’s not something we’ve seen before, so naturally we have no real idea about what to do with it. When the electron was first being described in the theory of electromagnetism, it wasn’t completely obvious that we would go on to use it to perform logic and calculation in our computers as we do today. As an investor, I’m not saying that I myself know how this technology will be best deployed, but the value, about 250 years in the making, seems clear to me. It’s on this philosophical point that I rest my investment thesis for D-Wave Inc. They just need exposure and time.