Quantum progress in research and industry Brief — 2026-09-29
Today: Quantum computing is moving closer to industrial relevance as commercially accessible hardware demonstrates a new quantum-advantage benchmark while quantum-enhanced AI methods accelerate advanced manufacturing simulation.
Top Stories
1. 🤖 IBM Nighthawk r2 demonstrates quantum-advantage benchmark on commercially accessible hardware
ScienceAlert · September 29, 2026
Bottom line: Researchers running IBM’s 120-qubit Nighthawk r2 through its standard cloud workflow generated one million random-circuit samples in 19 seconds, with the study estimating roughly 110 years for a specified classical simulation approach.
The experiment used 61 qubits and 36 circuit cycles, producing 918 two-qubit gates. The researchers estimated the equivalent classical workload at approximately 1.2 × 10²⁷ operations on the basis of tensor-network contraction and a conservative Frontier supercomputer performance assumption. The circuits, samples and analysis were released publicly for further testing.
Why it matters: The result is notable because the experiment used commercially accessible quantum hardware rather than a bespoke research system. However, the 110-year comparison is a benchmark-specific estimate rather than a fundamental lower bound on classical computation, and the work remains a preprint rather than peer-reviewed research.
2. 🤖 Empa and Terra Quantum use AI to compress laser-welding simulation from hours to milliseconds
Swiss Federal Administration · September 29, 2026
Bottom line: Empa and Terra Quantum developed a Fourier Neural Operator that predicts three-dimensional laser-welding behaviour in milliseconds instead of requiring hours for conventional high-fidelity simulation.
The Laser Processing Fourier Neural Operator produced predictions in approximately eight milliseconds at standard resolution and 88 milliseconds at twice the resolution. The approach models complex interactions involving heat transfer, fluid flow, phase changes and laser-induced keyhole formation.
Why it matters: Real-time surrogate modelling could make advanced manufacturing optimisation and digital twins economically practical at production speed. The work also illustrates an increasingly important convergence between AI, physics-based simulation and quantum-technology companies, although the demonstrated manufacturing acceleration comes primarily from the machine-learning model rather than quantum hardware executing the production workflow.