Results & proof

What we measured, at what size, against which method.

Every number below has a source, a status and a condition. Hardware means measured on a quantum processor; computation means classical; projection means forward from a measured law.

Measured results with status
ResultWhat it meansStatus
Award winner · GIC 2026 · 2nd worldwideJudged among the best in the world on industrial materials chemistry
Source

Challenge judging · GIC 2026 Advanced Materials track, judged result. GIC challenge page (pqic.org) ↗

Judged result
40 qubitsTwice the published chemistry scale, every step within chemical accuracy
Source

Published paper + our computation · The ~20-qubit baseline is the published work on this method; the 40-qubit figure is our GPU computation with hardware sampling.

Classical computation
173 h → minutesTraining cost of the challenge method, on one GPU
Source

Published paper + our computation · 173 GPU-hours is the published training cost of the challenge method; minutes on one GPU is our own computation.

Classical computation
4 of 4Formulations ranked in the right order, every gap within 0.3%, protocol frozen before the run
Source

Device result · IBM Heron, protocol frozen before the run. Detail in the EUV photoresist case.

Measured on quantum hardware
0.0029%Processor clock read from our own readout; loss traced to one qubit
Source

Device result · Read from the processor's own output with our device-quality readout.

Measured on quantum hardware
TighterEnergies than the standard estimator, same chip, same run
Source

Device result · Same chip, same run, compared with the standard estimator.

Measured on quantum hardware
126 qubitsA single run, constant-depth circuits (three entangling layers at every size)
Source

Device result · A 126-qubit run on a superconducting processor, three entangling layers.

Measured on quantum hardware
95.6% vs 39.5%Safe episodes with a hardware-measured disturbance model vs white noise
Source

Device result + simulation · Disturbance model measured on quantum hardware; the safety evaluation runs in simulation.

Measured on quantum hardware
2^20 plansGrid-expansion plan, globally optimal
Source

Our classical computation · Exhaustive classical search over all 2^20 build plans.

Classical computation
72% · 1.4%Pairwise model error found; protein site model within 1.4% of exact
Source

Device result + simulation · Measured on quantum hardware and in simulation, validated against exact references.

Measured on quantum hardware
~93 logical qubitsFlow-solver crossover, projected from a measured cost law
Source

Projection from a measured law · A forward projection from a cost law we measured. Not a measurement.

Projection

Featured evidence

The materials result, in two charts

Bare cluster3.49 eVSulfonate3.34 eVCarboxylate3.01 eVHydroxide2.21 eV
Four resist formulations ranked before synthesis by predicted electronic gap (lower activates more easily under EUV light). The ranking held on quantum hardware; every difference within 0.3%; protocol frozen before the run.
60%70%80%90%100%74.0% · 12q90.4% · 40q
Accuracy that improves with scale: 74.0% of the target at 12 qubits, 90.4% at 40 qubits, with circuits kept to three entangling layers. GPU computation plus hardware sampling.

Platform network results

Delivered through Merlin’s platform network

Clients anonymised to their sector; quoted with their published conditions; not presented as our own measurements.

Chemicals / manufacturing

Chemical-plant production planning

Planning time 2–6 h → 1–10 min, including verification

Condition
Baseline: the prior manual or spreadsheet planning. Multi-grade (100+ grades) scheduling. Self-reported in a customer interview.
Compute
Classical solvers · no quantum hardware
Status
Field, in production use

Through our platform network · partner and client not named

Automotive / materials

Automotive materials discovery (transparent conductors)

Score 123.5 vs 100.5 (NSGA-II) and 76.2 (TPE) in a 60-second run; reached NSGA-II's 60-second score in about 10 s

Condition
One public dataset; computational only. Any percentage is derived from the paper's table, not stated by its authors.
Compute
Quantum-inspired, on classical hardware
Status
Preprint

Does not show that quantum beats classical.

Through our platform network · partner and client not named

Mobility / smart city

Urban traffic-signal timing

−20% vehicle waiting time vs the conventional method

Condition
Simulation in a virtual environment, June 2020. Not a field trial.
Compute
Quantum-inspired optimisation
Status
Simulation, 2020

Does not show that quantum beats classical.

Through our platform network · partner and client not named

Telecom

5G carrier-aggregation tuning

Average downlink throughput about +10%

Condition
Field pilot on a live network in a limited trial area, with no added hardware. Secondary-cell data volume up to +50% is an upper bound.
Compute
Quantum-inspired (Ising machine)
Status
Pilot

Does not show that quantum beats classical.

Through our platform network · partner and client not named

Energy

Power-system unit commitment at industrial scale

25,755 variables and 48,939 constraints; quantum solutions were feasible

Condition
A commercial MILP solver was better at short runtimes; the hybrid was competitive at longer runtimes. Next-generation figures are projections. The publisher states that it does not establish commercial quantum benefit.
Compute
Photonic quantum device + classical
Status
Pilot / national-lab testbed

Does not show that quantum beats classical.

Through our platform network · partner and client not named

Energy / utilities

Distribution-grid reconfiguration

More than 7× the probability of finding the optimum vs the unoptimised quantum workflow

Condition
69-bus network. The baseline is a quantum workflow, not a classical one. About 230 buses is a projected threshold, not yet demonstrated.
Compute
Superconducting quantum device
Status
Hardware demo + projection

Does not show that quantum beats classical.

Through our platform network · partner and client not named

Cross-industry optimisation

Higher-order binary optimisation runtime benchmark

Up to about 80× faster than a commercial solver and more than 3× faster than simulated annealing, in time to an approximate solution

Condition
Best case over five author-selected hard instances per size (N = 80–156), chosen from 250 screened; self-reported. “Up to” is a best case.
Compute
Superconducting quantum device (156 qubits)
Status
Preprint

Does not show that quantum beats classical.

Through our platform network · partner and client not named

What we do not claim

The lines we never cross

A result is only useful if you can trust its edges. These are ours.

  • Quantum hardware beating every classical method. We name the method and the size we beat, or we say nothing.
  • A fraud-detection gain or a drug-ranking gain. Those are method results; pilots on your data come next.
  • Any speed advantage for quantum in grid planning, or a learning-speed advantage in autonomy.
  • An advantage for flow simulation on present hardware.
  • That a ranked material will perform in the fab.

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