Quantum computers remain 100,000 times short of solving recognized scientific problems: study

Researchers introduce a system-level benchmark measuring circuit scale and speed, revealing architecture trade-offs across superconducting and trapped-ion processors

A new benchmarking study posted 15 September 2026 on the arXiv preprint server has concluded that today’s quantum computers are roughly 100,000 times less capable than what would be required to solve recognized scientific problems.

The work comes from researchers at Sandia National Laboratories, with contributions from Quantinuum and NVIDIA, and introduces a framework called QUOPS — the Quantum Universal Operations Performance System — designed to measure both the scale of circuits a machine can execute and the speed at which it completes them.

QUOPS, rather than relying on component-level metrics such as qubit counts, gate accuracy and gate speed, which do not always predict how a complete machine will perform on a substantial calculation, instead treats the quantum computer as a whole system. The benchmark feeds randomized circuits of varying widths and depths into the hardware, then checks whether the output passes a predefined accuracy threshold. The result is distilled into two numbers: Q, representing the largest qualifying circuit the machine can run, and omega, the effective number of quantum operations completed per second at that scale. Together, these figures capture a trade-off that single specifications obscure.

Researchers applied the benchmark to processors from various companies:

• Google’s Willow and IBM’s Boston, both superconducting systems, ran operations faster and produced higher QUOPS rates.
• Quantinuum’s Helios trapped-ion processor, which provides all-to-all qubit connectivity, successfully handled larger circuits and achieved a higher Q score but operated at a lower rate.

The comparison does not crown one architecture as superior. Instead, the results show that machines reach performance through different combinations of accuracy, speed and connectivity — trade-offs that may matter differently depending on the workload.

Paper yet to be peer-reviewed

By mapping resource requirements for problems such as factoring RSA-2048 and calculating FeMoco energy eigenvalues, the researchers estimated target Q scores on the order of hundreds of millions. Specifically:

• Current hardware falls about 100,000 times short of those targets, lending support to the ongoing push toward fault-tolerant quantum computing, which uses error correction to protect calculations from accumulated noise.
• The team also applied QUOPS to a small fault-tolerant setup using up to eight logical qubits on Quantinuum’s Helios-1, demonstrating that the same benchmark can track both present-day physical-qubit machines and emerging logical-qubit systems.
• The paper has not yet undergone peer review.

The study’s authors argue that QUOPS offers a more realistic yardstick for evaluating quantum hardware because it reflects end-to-end system performance rather than isolated component specs. For cybersecurity professionals and technology strategists, the findings underscore that claims about “quantum advantage” must be scrutinized against concrete benchmarks tied to real-world workloads.

While the 100,000-fold gap may seem daunting, the framework provides a clear path for tracking progress as the field moves from noisy intermediate-scale quantum devices toward fault-tolerant architectures capable of scientific utility.