Quantum Benchmark Suite for Logistics: KPIs, Datasets and Reproducible Tests

Hook: Why logistics teams need a standardized Agentic AI benchmark now

Logistics teams face three linked pain points in 2026: a steep learning curve for new paradigms like Agentic AI, fragmented evaluation of solvers (classical vs. quantum solvers), and no agreed way to compare real-world impact. That gap stalls pilot decisions — as the 2025 Ortec survey found, 42% of logistics leaders are still holding back on Agentic AI despite broad interest. This article proposes a practical, reproducible Quantum Benchmark Suite for Logistics that lets practitioners and vendors directly compare Agentic AI, classical optimization, and quantum solvers on standard datasets, KPIs and test harnesses.

Executive summary (most important first)

• Introduce a standardized benchmark suite that covers representative logistics problems: VRP, VRPTW, Pickup & Delivery, multimodal routing, and warehouse batch picking.
• Define a compact KPI set: solution quality (gap), time-to-solution, reproducibility (variance), resource cost (compute & qubit-hours), and operational KPIs (on-time rate, fuel/CO2).
• Provide dataset tiers (small/medium/large) and an open format (FLQ-Logistics JSON + seed control) for reproducible experiments.
• Offer a reference harness with Docker + Python APIs to run three arms: classical optimization (Gurobi/OR-Tools), Agentic AI orchestration (LLM-driven planning + heuristics), and quantum solvers (QAOA / annealers / hybrid).
• Deliver scoring, statistical tests, and visualization conventions to produce defensible performance claims.

Why a common benchmark matters in 2026

Late 2025 and early 2026 saw two parallel trends: wider interest in autonomous, Agentic AI orchestration for planning, and continued improvements in quantum hardware and hybrid SDKs. But these ecosystems evolved separately. Without a shared benchmark, procurement and R&D teams cannot reliably answer: "Does quantum or Agentic AI give operational advantage for my routing problem, and at what cost?"

A standardized benchmark addresses three practical needs:

• Decision clarity — actionable, auditable comparisons for PoCs and vendor claims.
• Reproducible research — experiments that peers can rerun and extend.
• Tooling convergence — common input/output formats make integration into CI/CD easier (important for cloud and DevOps teams).

Benchmark scope: problem families and real-world scenarios

The suite focuses on logistics problems where quantum and agentic approaches claim value: combinatorial routing and scheduling under constraints and uncertainty. Include both deterministic instances and dynamic, stochastic environments to reflect operations.

Primary problem families

• Capacitated Vehicle Routing Problem (CVRP) — classic, core of route planning.
• Vehicle Routing with Time Windows (VRPTW) — adds scheduling constraints.
• Pickup & Delivery (PDP) — paired pickup and drop-off demands.
• Dynamic VRP — new requests arrive; evaluate online re-planning.
• Multimodal & Intermodal Routing — combined truck/rail/sea legs for freight.
• Warehouse Batch Picking and Sequencing — combinatorial items-to-bins and picker routing.

Scenario examples (reproducible)

• Last-mile e-commerce (Urban VRPTW) — 200 addresses, 50 vehicles, short time windows; emphasize latency and on-time deliveries.
• Regional drayage (Multimodal) — ports to distribution centers with transfer nodes, cargo size constraints.
• Cold-chain PDP with fragmentation — perishable windows, demand uncertainty.
• Warehouse pick-and-pack (Batch size 1–100) — focus on makespan and conveyor throughput.

Datasets: sources, tiers and format

To be useful, the benchmark must include both canonical academic datasets and operational datasets representative of modern logistics. The suite defines three tiers and an open interchange format.

Tier definitions

• Tier S (Small): 10–100 customers/nodes, for algorithm prototyping and quick iteration.
• Tier M (Medium): 100–1,000 nodes — typical regional operations used for PoCs.
• Tier L (Large): 1,000–10,000 nodes — stress tests and cloud-scale runs.

Recommended datasets (examples)

• Solomon VRPTW instances (classic baseline) wrapped into the suite format.
• CVRPLIB sets for CVRP analysis.
• Publicly shared carrier GPS traces (anonymized) for dynamic VRP scenarios — processed into binned request streams.
• Warehouse order streams sourced from public logistics datasets (or synthetic workloads calibrated to industry distributions).

FLQ-Logistics JSON (suite interchange format)

Each scenario is a single JSON file with strict keys: metadata, nodes, vehicles, demand streams, time windows, stochastic parameters, and a seed field. Example skeleton:

{
  "scenario_id": "urban_vrptw_200_v50_seed1234",
  "seed": 1234,
  "nodes": [{"id":1, "x": -73.9, "y":40.7, "demand":5, "tw": [480, 1020]} ...],
  "vehicles": [{"id":"v1","capacity":100,"start_node":0}],
  "dynamic_events": [{"time": 540, "type":"new_request", "request":{...}}],
  "metric_weights": {"cost":1.0, "on_time":2.0}
}

For a developer-focused guide on dataset and content hygiene (including training-data considerations), see the developer guide that outlines metadata and compliance best practices.

KPIs: how to measure impact (not just objective values)

Keep the KPI set compact so results are comparable across solvers. Every test run must report:

1. Solution quality — objective value (total distance/cost), and optimality gap when a reference optimum or best-known solution exists.
2. Time-to-solution — wall-clock until first feasible solution and until final solution (with specified tolerances).
3. Robustness — variance across N runs (N≥30 for stochastic solvers) and worst-case performance in dynamic scenarios.
4. Resource cost — CPU/GPU hours, cloud cost in USD, and for quantum: qubit-hours and backend cost.
5. Operational KPIs — on-time delivery rate, fleet utilization, fuel consumption and CO2 estimates.
6. Reproducibility metadata — seed, solver versions, hardware specs, and full logs.

Scoring and composite metrics

To compare across dimensions, define a normalized scoring function. Let q be normalized quality (0 worst, 1 best), t be time score (based on latency buckets), r be robustness (1 - coefficient of variation), and c be cost-efficiency (quality per dollar). Composite score:

Score = wq * q + wt * t + wr * r + wc * c

Default weights: wq=0.5, wt=0.2, wr=0.2, wc=0.1. The suite publishes results under multiple weightings to reflect different operational priorities.

Reproducible test harness: architecture and orchestration

Experiments must be runnable from a repo with a single command. The harness uses Docker and a Python runner that orchestrates three executor types:

• Classical optimization executor — runs solvers like OR-Tools and commercial solvers (Gurobi), with tuned parameters and time budgets.
• Agentic AI executor — runs a planner composed of an LLM-driven agent (e.g., LangChain/AutoAgent) orchestrating heuristic solvers or APIs, with a deterministic seed for the agent's decision policy.
• Quantum executor — interfaces to real devices (D-Wave, ion-trap providers) or simulators (state-vector/noise-aware). Supports QAOA, quantum annealing and hybrid quantum-classical loops.

Reference Docker command

docker run --rm -v $(pwd)/data:/data flowqubit/bench-harness:latest \
  python3 run_benchmark.py --scenario /data/urban_vrptw_200_v50_seed1234.json \
  --executors classical,agentic,quantum --runs 30 --time_budget 300

Minimal Python API (example)

from flowqubit_bench import BenchmarkRunner

runner = BenchmarkRunner(scenario_path='scenarios/urban.json')
runner.register_executor('classical', OrToolsExecutor(time_limit=300))
runner.register_executor('agentic', AgenticExecutor(llm_model='gpt-4o', policy_seed=42))
runner.register_executor('quantum', QaoaExecutor(backend='braket_sv', shots=1024))
results = runner.run(runs=10)
print(results.summary())

How to run fair comparisons

Fairness requires identical constraints, seeds for stochasticity, and matched compute budgets. Key rules:

• Fix random seeds in dataset and solver where possible; report seeds in metadata.
• Match wall-clock or CPU/GPU budgets across solvers. For quantum devices, use a realistic qubit-hour budget and include queue/wait time in time-to-solution.
• Report pre- and post-processing steps separately (e.g., LLM prompt engineering time counts as compute).
• Run at least N=30 independent trials for stochastic methods; use bootstrapping to estimate confidence intervals.

Practical examples & preliminary results (illustrative)

Below are representative, synthetic outcomes from an internal PoC run (for illustration) on the urban VRPTW Tier M instance. Values are fictional but demonstrate how to present results.

Example outcome snapshot

Scenario: urban_vrptw_200_v50
Runs: 30 per executor
Classical (OR-Tools): avg_cost=10200, std=120, avg_time_to_first=12s, avg_time_final=45s
Agentic AI (LLM orchestrated + heuristics): avg_cost=9850, std=300, avg_time_to_first=40s, avg_time_final=200s
Quantum (Hybrid QAOA + local search): avg_cost=10120, std=250, avg_time_to_first=300s, avg_time_final=900s
Composite scores (default weights): classical=0.78, agentic=0.81, quantum=0.63

In this illustrative run, Agentic AI delivered slightly better average cost but higher variance and longer finalization latency. Quantum hybrid improved over a naive quantum-only approach but suffered long wall-clock times due to queuing and classical post-processing.

Statistical analysis and significance

Run statistical tests to verify claims. Recommended steps:

1. Compute bootstrapped confidence intervals for objective and KPIs.
2. Use paired tests (Wilcoxon signed-rank) when comparing runs on identical seeds.
3. Report effect sizes (Cohen's d) and p-values.

For dynamic scenarios, also report time-to-recovery after a disruption and use survival analysis to compare response times.

2026 trends and how they shape the suite

Key developments shaping this benchmark in 2026:

• Agentic AI maturation — more production pilots in 2026 (per Ortec and industry reporting) mean agentic orchestrators are now commonly integrated into planner stacks. The suite includes agentic orchestration baselines and prompt/version metadata.
• Hybrid quantum toolchains — gate-model and annealing providers improved SDKs and error mitigation in late 2025; hybrid algorithms like QAOA+local-search are now viable baselines.
• Cloud-native benchmarking — providers expose cost metrics and telemetry; the harness collects these to compute resource KPIs.

Advanced strategies and recommendations for teams

How to use the suite in practice:

1. Start small, prove value — use Tier S to iterate. Measure on-time rate and solution gap first.
2. Run paired PoCs — run classical and Agentic AI baselines before adding quantum. Baselines help identify where quantum can add value (e.g., tighter combinatorics or atypical constraints).
3. Profile hotspots — identify sub-problems that dominate runtime and consider hybridization: classical pre-processing, quantum core, classical post-processing.
4. Track total cost of ownership — include model maintenance, LLM prompts, and quantum access fees in ROI estimates.
5. Automate CI benchmarking — integrate benchmark runs into your CI pipeline to detect regressions as models or LLMs evolve.

Common objections and mitigations

Teams often raise three objections:

• "Quantum is too slow / immature" — Mitigation: run quantum only on problem subgraphs or use annealers for warm-starting classical heuristics.
• "Agentic AI is unpredictable" — Mitigation: lock agent policies, use prompt chains with deterministic heuristics, and evaluate variance across seeds.
• "Benchmarks don't match our constraints" — Mitigation: customize scenarios using the FLQ-Logistics JSON to reflect your operational constraints and share them for community reproducibility.

Governance, openness and community contributions

For adoption, the benchmark must be open, versioned and community-governed. Recommended governance model:

• Open GitHub repo with scenario and harness specs (MIT or Apache 2.0 license).
• Semantic versioning for dataset and harness releases.
• Mandatory metadata for runs (hardware, solver versions, cost), with a public leaderboard for verified submissions.
• Annual workshop (virtual) to curate new scenarios and publish challenge tracks (e.g., dynamic VRP 2026 challenge).

Next steps: how to adopt the suite in your organization

1. Clone the reference harness and run Tier S examples with your standard solver in CI.
2. Run a 2-week PoC comparing classical vs Agentic AI on your representative Tier M scenario.
3. If results suggest a combinatorial advantage, run a quantum hybrid pilot on a constrained subproblem and measure qubit-hour cost per percentage point of gap reduction.

Actionable checklist (quick start)

• Download FLQ-Logistics scenario files.
• Prepare Docker environment and register executor configs.
• Run baseline classical experiments (N=30) and collect KPIs.
• Run Agentic AI experiments with fixed policy seed.
• Run quantum experiments accounting for queue/latency and report qubit-hours.
• Publish results with full metadata and run bootstrapped statistical tests.

Conclusion and call-to-action

In 2026, logistics teams must move from vendor claims to auditable comparisons. A standardized Quantum Benchmark Suite for Logistics — with common datasets, compact KPIs, and a reproducible harness — makes those comparisons possible. Start with Tier S, iterate to Tier M, and use the suite's scoring and statistical rigor to inform procurement and R&D decisions.

Ready to run your first benchmark? Clone the reference harness, upload one representative scenario from your operations, and run the three arms (classical, Agentic AI, quantum). Share anonymized results to the public leaderboard and join the community workshop to shape the 2026 challenge tracks.

Practical benchmarking transforms speculation into evidence. Use the suite to answer: "Where does quantum, Agentic AI, or classical optimization actually move the needle for my logistics KPIs?"

Get involved

Visit the FlowQubit benchmark repo to get the code, datasets and a starter notebook: github.com/flowqubit/quantum-logistics-bench. Contribute a scenario or run a verified submission to the 2026 leaderboard.

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