01 / Approach
From data to useful intelligence
Intelligence is more than producing an answer. It means finding a pattern worth trusting, understanding what remains uncertain, and connecting that insight to a useful decision.
QAI explores hybrid methods for that purpose. Classical computation structures inputs and checks outputs. AI learns relationships from data. A selected quantum routine contributes to a part of the calculation, then returns its results to the classical workflow for interpretation.
- 01
Define the question
Identify the prediction or decision, relevant evidence, and what a useful result should mean.
- 02
Learn a baseline
Build a strong classical or AI approach. Establish the quality and compute cost that a new method must improve on.
- 03
Test a quantum step
Evaluate a suitable quantum routine for one part of the task. Return its output to the model or solver.
- 04
Measure the result
Compare quality, uncertainty, time, and compute. Keep the added step when it improves the agreed outcome.
02 / The quantum role
Where quantum enters
The quantum part addresses a selected mathematical subproblem: comparing data, sampling a distribution, or exploring candidate solutions. Its measurement results return to the classical model or solver, where they become usable information.
These are candidate methods to evaluate. Suitability depends on the data, formulation, available hardware, and a matched baseline.
Compare patterns
A quantum kernel encodes data as quantum states and estimates their similarity. A classical learning algorithm uses those similarities to classify new observations.
Explore uncertainty
Draw samples from a candidate distribution to examine plausible outcomes. Compare how accurately it represents the uncertainty, along with the cost of producing those samples.
Compare candidate plans
Compare alternatives within defined constraints. Evaluate a selected quantum optimization routine beside a classical solver, using the same objective and resource budget.
03 / Evaluation
Measure the whole result
Compare against credible classical and AI baselines using the same task, data splits, constraints, and success criteria. Record result quality, latency, and total compute, including data preparation, quantum encoding, communication, measurement, and repeated execution.
Repeat runs to assess variability. Disclose hardware, noise conditions, run counts, resource budgets, and the limits of the comparison.
No benchmark results are published on this page. Discuss a workload to define a matched evaluation; any improvement must be demonstrated by its results.
04 / QAI demo
From evidence
to understanding
The planned walkthrough makes a QAI evaluation visible: define a question, establish a classical or AI baseline, evaluate a suitable quantum step, and compare the complete result. The evidence determines whether the added step is useful.
Seek a stronger signal
Define the question. Compare the methods. Examine the evidence.
- 01 / Baseline
Define useful intelligence
Agree on the task, data, and success criteria. Establish a classical or AI comparison.
- 02 / Candidate
Evaluate a quantum step
Test a suitable routine for part of the task and return its output to the classical workflow.
- 03 / Evidence
Compare the whole result
Examine quality, uncertainty, latency, and total compute, including repeated runs and their variability.