Quantum computing stocks have had a rough six months. Major players like IonQ (NYSE: IONQ) and D-Wave Quantum (NYSE: QBTS) saw their share prices tumble more than 65% between October 2025 and March 2026.

April, on the other hand, has been fantastic for quantum stocks. D-Wave's shares are up 25% since April 1, and IonQ's shares have soared more than 50%!

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But even with these gains, both IonQ and D-Wave are still trading more than 45% off their October highs. Which of these quantum stocks looks like the best one to buy on the dip?

What IonQ does

Quantum computing is a tough business to evaluate because quantum mechanics, quite frankly, doesn't make a lot of sense . Luckily, we don't have to understand quantum mechanics to compare IonQ's and D-Wave's very different quantum computer systems.

IonQ is developing a more traditional "gate-model" quantum computer, in which quantum particles that carry information are fired through "gates" that affect them. Such a system is measured by a metric called "two-qubit gate fidelity," expressed as a percentage.

In October, IonQ became the first quantum computing company to achieve two-qubit gate fidelity of 99.99%. That may sound really good, but given the number of computations required to run even a single complex equation, it's estimated that quantum computers will likely need to achieve fidelity of 99.99999% or even higher to break into the mainstream. Classical computers could be considered to have fidelity (accuracy) of practically 100%.

How D-Wave differs

Unlike IonQ, D-Wave builds quantum annealers that perform a very specific type of quantum computing. Quantum annealing is very good at problems like finding optimal configurations of data, such as an optimal delivery route for a logistics company.

Quantum annealing is less accurate than gate-model quantum computing, but it can be much faster and less expensive. That's because, when you're looking for the optimal solution from among millions of possibilities, you might not need to precisely pick the No. 1 solution. For example, trying to find the optimal delivery route among 20 locations would give you more than 60 quadrillion possibilities. If you could even pick one of the top 1,000 options, you'd probably be achieving near-optimal fuel use.