Workload Pattern Catalog — Operating Quantum Computers

This appendix summarizes common quantum workload patterns in a format useful for planning, review, and platform design.

D.1 Pattern summary

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D.1 Pattern summary · Figure 1

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mindmap
  root((Quantum workload patterns))
    Chemistry
      Ground-state estimation
      Dynamics
      Materials screening
    Optimization
      QUBO / Ising
      Hybrid decomposition
      Candidate generation
    Sampling
      Distribution estimation
      Generative sampling
      Monte Carlo support
    Cryptography
      Risk assessment
      PQC migration
      Algorithm agility
    Fault tolerance
      Resource estimation
      Logical service planning

Pattern	Primary output	Common risk
chemistry ground-state estimation	energy estimate	modeling assumptions dominate
optimization candidate generation	feasible candidates	classical baseline outperforms
sampling	distribution or samples	validation is weak
quantum ML experiment	model metric	data leakage or classical baseline weakness
resource estimation	qubits/runtime/cost	assumptions are stale
PQC migration	risk reduction	inventory incomplete

D.2 Chemistry ground-state pattern

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D.2 Chemistry ground-state pattern · Figure 2

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flowchart LR
    Molecule[Molecule] --> Hamiltonian[Hamiltonian]
    Hamiltonian --> Mapping[Qubit mapping]
    Mapping --> Ansatz[Ansatz]
    Ansatz --> Measurement[Measurements]
    Measurement --> Energy[Energy estimate]

Review questions:

What active space is used?
What classical baseline is available?
What mapping and symmetry reductions are applied?
What is the measurement cost?
What scientific claim is supported?

D.3 Optimization candidate-generation pattern

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D.3 Optimization candidate-generation pattern · Figure 3

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flowchart LR
    Model[Optimization model] --> Solver[Quantum / hybrid sampler]
    Solver --> Candidates[Candidate set]
    Candidates --> Repair[Repair / validate]
    Repair --> Rank[Rank]
    Rank --> Decision[Decision]

Review questions:

What is the incumbent classical method?
Are constraints hard-coded, penalized, repaired, or rejected?
Is candidate diversity valuable?
What ablation isolates the quantum component?
What is the cost per useful candidate?

D.4 Sampling pattern

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D.4 Sampling pattern · Figure 4

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flowchart TB
    Circuit[Sampling circuit] --> Backend[Backend]
    Backend --> Counts[Counts / samples]
    Counts --> Distribution[Distribution estimate]
    Distribution --> Test[Validation tests]
    Test --> Report[Sampling report]

Review questions:

What distribution is expected?
What statistical tests apply?
How many samples are required?
How is hardware noise separated from intended structure?
What downstream decision uses the samples?

D.5 Resource-estimation pattern

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D.5 Resource-estimation pattern · Figure 5

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flowchart LR
    Algorithm[Algorithm] --> Logical[Logical resource estimate]
    Logical --> Physical[Physical assumptions]
    Physical --> Runtime[Runtime and qubits]
    Runtime --> Bottleneck[Bottleneck analysis]
    Bottleneck --> Roadmap[Roadmap decision]

Review questions:

What algorithm version is estimated?
What error-correction code assumptions are used?
What physical error rates are assumed?
What is the dominant bottleneck?
What investment would move the estimate most?

D.6 PQC migration pattern

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D.6 PQC migration pattern · Figure 6

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flowchart LR
    Inventory[Crypto inventory] --> Classify[Risk classify]
    Classify --> Migrate[Migrate]
    Migrate --> Validate[Validate]
    Validate --> Monitor[Monitor]

Review questions:

Is the cryptographic inventory complete?
Which data has long confidentiality lifetime?
Which systems cannot be migrated quickly?
What algorithms and parameter sets are approved?
What exceptions exist and when do they expire?

D.7 Platform-selection pattern

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D.7 Platform-selection pattern · Figure 7

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flowchart TB
    Workload[Workload] --> Requirements[Requirements]
    Requirements --> Targets[Target candidates]
    Targets --> Trial[Trial runs]
    Trial --> Evidence[Evidence comparison]
    Evidence --> Select[Select target]

Review questions:

What target capabilities are required?
What portability level is needed?
What benchmark suite represents the workload?
What data and metadata are available?
What is the exit plan?

D.8 Pattern decision tree

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D.8 Pattern decision tree · Figure 8

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flowchart TB
    Start[New quantum idea] --> Decision{Does it support a decision?}
    Decision -- no --> Research[Keep as research note]
    Decision -- yes --> Baseline{Baseline exists?}
    Baseline -- no --> BuildBaseline[Build classical baseline]
    Baseline -- yes --> Type{Workload type}
    Type -- chemistry --> Chem[Chemistry pattern]
    Type -- optimization --> Opt[Optimization pattern]
    Type -- sampling --> Sampling[Sampling pattern]
    Type -- security --> PQC[PQC pattern]
    Type -- FT planning --> Resource[Resource-estimation pattern]

Use this tree during intake so the team does not approve vague experiments.