Tom McCarthy breaks out the current state of quantum computing. For me what’s valuable here is not predictions on potential commercial applications or the timeline but instead the heuristic to use to track progress and the clear line in the sand for a commercially viable technology:

The key limitation is the size of the problem(s) that the QC can handle. Runtime, integration with real-time data, and performance vs classical optimization techniques also matter, but the main constraint is how many variables a 1 million qubit QC can handle. 1 million physical qubits gives you about 1,000 logical qubits, which means you can handle, at most, a 1,000-variable optimization problem. That’s pretty small for conventional techniques, and many classical solvers will be able to provide near-optimal solutions quickly, using much cheaper GPUs or CPUs than a QC. Convex optimization programs are very efficient and able to handle millions of variables.

Here’s a neat heuristic to cut through the noise:

How Can I Tell if QCs Are Progressing? Qubits, qubits, qubits. Look out for signs of exponential growth in qubit count. Except in the recent case of neutral atom systems, we have not seen exponential growth in qubit counts over the past 10 years. They have slowly progressed from 5 q in 2016 through 53 q in 2019 through to 441 q in 2025.12IBM Yorktown Q 5 in 2016, Google Sycamore in 2019, D. Bluvstein et al in 2025. There’s also some fraud, including to do with Majorana qubits, which are Microsoft’s chosen modality. Also, look out for how many qubits in a processor can be used for computation - no point having a 1,000 q machine if only 200 of those qubits can be used in an algorithm.