Classical computers thrive on the curve of Moore’s law, with performance roughly doubling every year; after n years, classical computers are 2^n times faster. This means we’ve seen roughly a 1000x increase in computing power in under a *decade*. It’s an amazing feat, and has driven classic computing-related tech — and will drive the next platforms built on top of it (like VR, AR, etc.) — to seemingly miraculous advances that touch our everyday lives, work, and play. So you would think a similar dynamic would apply to the next major computing platform.

But quantum computing — which unlike classical computing, is based on nature’s more complex operating system of quantum mechanics — will take the world by surprise. Even established veterans of the first few computing revolutions could be caught off guard, unable to foresee the jump from impressive demo to devastatingly impressive production machine. How so? Because it turns out that quantum computing has its own Moore’s law, and that law takes exponential scaling to a whole new level.

In the quantum hyperscaling Moore’s Law, the speed of a quantum computer is exponential in the number of coherent quantum elements or “qubits” — that is, 2^**q**. But successfully incorporating technological advances in using silicon technology would enable the qubits *themselves* to follow Moore’s law (q = 2^n)… making the resulting performance power of the quantum computer 2^2^n. This means that the performance of quantum computing is exponentially more rapid than Moore’s Law. It’s as if Moore’s law itself sped up like Moore’s law.

That sounds almost like a koan, so let’s break down the trajectory: At first, quantum technology will seem vastly inferior to its classical counterparts. And then, within just a single generation, the tables will be turned. Consider an application in a specific domain where it takes 100 qubits for a quantum computer to beat a classical one. Quantum computers will have 8, then 16, then 32, then 64 qubits; years will go by and the classical machines will continue to dominate. But then, in the very next year in the transition to 128 qubits, classical machines will never be able to compete against quantum computing ever again, once the “quantum intercept” has been achieved:

The critical boundary — different numbers of qubits — where this quantum intercept occurs will depend on the nature of the application. But this pattern will repeat in domain after domain, just as we’ve seen classical computers impact domain after domain. The difference here is that whereas many of those industries saw the march of classical computing coming, some won’t be able to see quantum computing coming as it will tip over so suddenly.

Still, we can at least predict some areas where quantum computing will likely have an impact sooner than later, simply because of the limitations of what classic computers can’t do. Some of these include: the ability to model the chemistry (more precisely, quantum chemistry) of molecules involved in drugs, or in industrial processes such as energy; new advances in machine learning; and novel cryptography.

Of course, even though quantum computing appears to have turned the corner from a scientific to an engineering problem, it’s still a massive engineering challenge. What’s needed to bring this to fruition? A full-stack approach that allows for rapid testing and pushing innovation at all scales, from gates to chips to fabs to programming languages. While big companies can certainly emulate a full-stack approach, I believe startups have an advantage here because they are built natively to do the fast iteration and closed feedback loops between design-build-test across expertise that’s otherwise siloed across huge departments. And they can do this without the competing demands for attention that large incumbent players face, given their entrenched interests in their own current core platforms.

That’s where our investment, Rigetti Computing, comes in. What it takes other institutions empirically 2 months to do, Rigetti currently does every 2 weeks.

Let’s first talk about the role of the fab, the factory where integrated circuits are typically manufactured. Success here is defined as success in scaling. Given that Moore’s Law is as much a force of human nature and economics as it is of technical prowess, those same commercial dynamics will be at play here too. But scaling here has challenges beyond the typical engineering roadblocks in classical silicon circuits, since it’s not just about more qubits, but about more high-fidelity, quantum coherent qubits. Because quantum mechanics allows the qubit to be in two states at the same time (unlike classical computer bits), the quantum computing community has worked hard to get the complete benefits of quantum computation at scale while lowering error rates — that is, make quantum computing more reliable. That’s where Rigetti brings deep know-how, and with the world’s first *commercial* quantum integrated circuit fab, can take the lead there.

But so far we’ve been focused on hardware. The key to quantum computing, as it has been with every previous turn of the computing revolution, will be *software* — and that includes everything from programming languages for quantum computers to creating an environment where developers can begin building applications. Contrary to popular folklore about computing innovation, most of the killer apps today are a product of such an ecosystem, not just of a few isolated inventors working in a garage. With the release of Forest 1.0 — a programming and virtual machine environment for executing quantum computing over the cloud — and Quil — the first instruction language for hybrid quantum/classical computing — anyone can develop algorithms for quantum computing, now.

Because there might yet be a way to get ahead of being surprised by the next revolution in computing. Programming a quantum computer is very different than programming classical machines. Understanding the underlying hardware itself is key to success. And with the rapid switch to a regime where quantum intercept kicks in, we need time to prepare for what’s coming.