In early December, Alphabet‘s Google announced a breakthrough in quantum computing that has the potential to push the entire industry forward. Its Willow quantum chip can reduce errors exponentially as it adds more qubits (the fundamental building blocks of quantum computing chips).
Error correction at scale is a challenge the industry has faced since the first quantum error correction function was introduced in 1995. Willow’s breakthrough allowed it to solve a benchmark computation in 5 minutes that would take today’s best classic supercomputer an estimated 10 septillion years to complete. The task is tailor-made for quantum computers and only useful for performance measurements, but that’s still a big acceleration.
The technology introduced by Willow also opens the door for quantum computing companies to advance their own development and speed up their timeline for commercial viability.
Many quantum computing stocks have seen their prices skyrocket since Google’s announcement. Some have gotten well ahead of Wall Street analysts’ price targets, even as they rush to update their estimates.
But one stock could still see upside as high as 45% from its price as of this writing, based on the highest price target on Wall Street of $9 per share. Even the median price target ($7.50 per share) is 21% higher than its current price.
(Quantum computing stocks are nearly as volatile as qubits these days. So, by the time you’re reading this, those numbers may look quite a bit different.)
Here’s why Wall Street thinks there’s still room left for D-Wave Quantum (NYSE: QBTS) to keep climbing in 2025.
The best-known quantum computing companies all use different methods for developing quantum computers and determining the position of qubits in their chips.
Each company will argue in favor of its approach. The truth is, different approaches are best suited for different types of tasks and problems.
Google and many others take a gate-based approach. It’s kind of a compromise between natural quantum physics and classical computers, where you set up a detailed program to follow — with a very different set of commands. This method offers more flexibility and could result in a general-purpose chip that can solve all sorts of problems. A gate-based chip is better suited for more advanced tasks like machine learning or cracking cryptography.
The problem with gate-based quantum chips is noise. Since qubits aren’t very stable, subtle interactions with the environment may change their value before a computation is complete, thus producing an error. Stray cosmic radiation can be enough to introduce a significant error, or a photon’s worth of light.
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