Benchmarking OLAP: Can Quantum-Inspired Optimizers Beat ClickHouse?

Can quantum-inspired optimizers beat ClickHouse? A practical benchmarking blueprint for 2026

Hook: If you’re running OLAP at enterprise scale, you’re constantly battling I/O, skewed joins, and exploding materialized view space — and you need actionable guidance on whether the latest quantum-inspired optimizers deliver real, deployable gains over fast classical OLAP engines like ClickHouse. With ClickHouse’s $400M scale-up funding and $15B valuation signaling aggressive investment in scale and performance, now is the right moment to benchmark: where do quantum-inspired techniques help, at what cost, and how do you measure real ROI?

Why this matters in 2026

In late 2025 ClickHouse closed a $400M round led by Dragoneer at a reported $15B valuation, accelerating its push into cluster management, faster IO paths, and enterprise support. Bloomberg’s coverage put the investment in context: the classical OLAP market is being industrialized and optimized aggressively. At the same time, 2025–2026 saw rapid growth in quantum-inspired optimization tools (digital annealers, hybrid quantum-classical services, and combinatorial solvers) and more mature hybrid workflows that can be integrated into traditional data stacks.

That convergence raises a practical question for data engineering and platform teams: can quantum-inspired optimizers deliver net gains for OLAP workloads — and when? This article outlines benchmark scenarios, integration patterns, cost trade-offs, and realistic win-cases you can run in your environment.

Executive summary (inverted pyramid)

• Short answer: Quantum-inspired optimizers can win in targeted combinatorial subproblems inside OLAP systems (join ordering, materialized view selection, partitioning, secondary index configuration) — but they are not a universal replacement for engine-level performance features in ClickHouse (vectorized execution, columnar compression, NVMe IO).
• Best-fit scenarios: complex multi-way join planning, high-dimensional MV selection, and cross-node placement problems where the search space is enormous and cost models are brittle.
• Cost trade-offs: optimization runs add compute and service costs; benefits must exceed that overhead via reduced query latency, lower cluster size, or fewer materialized views.
• Benchmark approach: reproduce both baseline (ClickHouse tuned) and hybrid (ClickHouse + quantum-inspired optimizer) across realistic datasets (TPC-DS/TPC-H scales 10TB–1PB, production event logs), concurrency and mixed query types, and measure latency, throughput, cost-per-query, and energy.

What “quantum-inspired” means in production OLAP

“Quantum-inspired” covers a range of classical and hybrid techniques that borrow mathematical formulations from quantum computing (QUBO, Ising models) and solve them on classical accelerators or specialized hardware such as digital annealers. Examples in the market include digital annealers, D-Wave hybrid solvers, and vendor-specific combinatorial services. These tools specialize at exploring huge combinatorial spaces faster than naive heuristics.

In OLAP, candidate uses are:

• Join ordering and distribution: find near-optimal join orders and data shuffles across nodes when cardinalities are uncertain.
• Materialized view (MV) selection: choose a subset of MVs under storage and maintenance cost constraints.
• Partitioning and placement: map shards/partitions to nodes to minimize cross-node traffic.
• Index/configuration tuning: select secondary structures and compression zones to optimize latency/IO tradeoffs.

Why ClickHouse matters as the baseline

ClickHouse is an industrial-strength, columnar OLAP engine focused on high concurrency, low-latency analytic queries and fast ingestion. With renewed funding in 2025–2026, ClickHouse has doubled down on scale-up/scale-out improvements: faster NVMe paths, improved compaction, and cloud-managed offerings. That means your baseline performance is already high — an optimizer must beat a carefully tuned ClickHouse cluster, not a default install.

Benchmark scenarios: what to measure and why

Design experiments that reflect the full lifecycle of production OLAP workloads. Below are prioritized scenarios with concrete metrics.

1) Complex multi-way join workloads (best candidate)

Why test it: Join ordering is a classic combinatorial problem; small improvements can reduce intermediate row explosion and network shuffles.

Workload profile:

• Datasets: TPC-DS at 10TB, 100TB, 1PB; production ad-tech or clickstream logs modeled as fact tables with dozens of dimension tables.
• Queries: multi-way star schema joins with group-bys, nested aggregations, and filter predicates producing high intermediate result sizes.
• Metrics: 95th/99th percentile latency, median latency, intermediate row counts, network bytes transferred, CPU seconds spent on join operators.

2) Materialized view selection and maintenance (high payoff when MV space is constrained)

Why test it: Choosing which MVs to maintain under tight storage and refresh budgets is an exponential selection problem where quantum-inspired solvers can enumerate better Pareto-optimal sets.

Workload profile:

• Datasets: 50–200 candidate MVs derived from analytics queries across 10TB–100TB data.
• Constraints: storage budget, refresh window (nightly or near-real-time windows), maintenance CPU budget.
• Metrics: query latency distribution before/after MV selection, total storage used, refresh CPU hours, and effective cost savings (fewer cluster nodes or lower IO).

3) High-cardinality group-bys and sketch aggregation (borderline)

Why test it: Cardinality and grouping are often I/O-bound. Quantum-inspired optimizers can suggest partitioning or pre-aggregation strategies that reduce IO, but gains depend heavily on storage layout.

4) Mixed OLAP + ML inference workloads (emerging hybrid wins)

Why test it: Many enterprises run feature extraction pipelines inside OLAP. Quantum-inspired solvers can optimize pipeline DAG co-location and caching to reduce end-to-end latency for ML inference.

5) Stress test: concurrency and cost-per-query at scale

Why test it: Even small per-query gains compound at high concurrency. Measure cost-per-query at 100, 500, 1,000 concurrent analytical sessions.

Experimental design: reproducibility checklist

1. Baseline tuning: Tune ClickHouse using best practices — ZSTD compression, MergeTree settings, appropriate index_granularity, vectorized settings, and parallelism pinned to NUMA topology.
2. Hardware parity: Run both baseline and hybrid tests on identical cluster hardware (same CPU families, NVMe tiers, network fabric). If using vendor digital annealers or hybrid cloud solvers, keep solver execution outside the ClickHouse cluster and measure end-to-end time including network RTTs.
3. Version control: Pin ClickHouse version and optimizer version. Log all configuration files and commit to a benchmark repo.
4. Warm-up and statistical rigor: Run warm-up phases, collect distributions, and compute confidence intervals. Use at least 30 runs per query for percentiles.
5. Instrumentation: Collect query plans, query execution profiles, OS-level metrics (CPU, RAM, IO, network), and cost of optimization runs (time and $). Follow modern observability guidance like Modern Observability in Preprod Microservices to keep this reproducible.
6. Failure modes: Add fallbacks so that if the optimizer returns a plan slower than the baseline, the system falls back to the baseline plan for safety.

Integration pattern: ClickHouse + quantum-inspired optimizer

Integrating a quantum-inspired optimizer is typically non-invasive and follows a planner-in-the-loop architecture:

1. Intercept query or query batch during planning.
2. Extract the logical query graph and candidate physical operators.
3. Map combinatorial subproblem (join ordering, MV selection) to a QUBO/Ising formulation or to the solver’s native API.
4. Send the QUBO to the solver service (local digital annealer or hybrid cloud service).
5. Translate solver output to a physical plan variant (join order, partitioning map, MV set) and compute estimated cost deltas.
6. Choose the plan if it passes a cost delta threshold and latency budget; otherwise keep baseline.

Conceptual pseudocode:

// simplified planner loop
plan = clickhouse_planner(query)
if is_combinatorial(plan):
  qubo = make_qubo(plan)
  solution, solve_time, solve_cost = solver.solve(qubo)
  if solution and expected_gain(solution) > overhead(solve_time, solve_cost):
    plan = apply_solution(plan, solution)
return execute(plan)
  

Cost analysis: concrete numbers and sensitivity

Costs are organization-specific, but here is a realistic model for decision-making:

• Baseline costs: ClickHouse cluster cost = nodes * hourly_rate. Include storage and network egress. For on-premise, convert CapEx to hourly Opex equivalent.
• Optimizer costs: per-run cost = solver runtime * solver rate + data transfer. If using a hybrid cloud solver, include queue wait and call overhead.
• Benefit types: reduced query latency (user impact), lower cluster size (direct Opex/CapEx), fewer MVs (storage savings), lower operator CPU time (energy/cost savings).

Example sensitivity case (illustrative numbers):

• Cluster hourly cost: $50/node. 10-node cluster = $500/hr.
• Optimizer per-run SaaS cost: $0.50–$10 depending on problem size; on-prem digital annealer amortized cost may be comparable.
• If optimizer reduces average query latency by 25% enabling 20% fewer nodes for same SLA, savings = 2 nodes * $50 = $100/hr (>$2,400/day). If optimizer is invoked 10 times/hour at $5 each = $50/hr; net positive.

The break-even depends on how often the optimizer can be reused (e.g., MV selection once per day vs join ordering per query). The highest ROI is for daily or hourly optimization problems (MV sets, placement) and for heavy, repeated query templates where solved plans are cached.

Realistic win-cases (and anti-patterns)

Win-cases

• Large enterprise analytics with many correlated queries: When an optimizer can select a shared set of MVs that dramatically reduce aggregate CPU hours and node count — pair this with a good data catalog and query inventory to measure impact.
• Complex reporting queries with many joins and uncertain cardinalities: When join ordering improvements reduce intermediate row blow-up and network shuffles.
• Cost-limited edge deployments: When you can trade a small cloud optimizer bill for smaller edge clusters or fewer heavy nodes on-prem.

Anti-patterns (where you should not deploy yet)

• Single-table scan workloads dominated by IO — algorithmic planning can't beat hardware IO bottlenecks.
• Ultra-low-latency per-row analytics where optimization overhead exceeds query runtime (microsecond-level).
• Environments with weak cost observability — if you can't reliably measure savings, you can't reason about ROI.

Operational considerations and safety

• Determinism and reproducibility: Ensure your optimizer stores versioned solver runs, seeds, and plan mappings to reproduce decisions for audits.
• Fallbacks: Maintain robust cost thresholds so the system falls back to ClickHouse’s native planner if the solver's plan underperforms.
• Explainability: Log how the solver mapped to QUBO and why it selected a solution; this is critical for trust in production.
• Security and data privacy: If you send topology or cardinality data to a cloud solver, avoid exposing raw PII or use synthetic fingerprints and differential privacy techniques — closely related to approaches in privacy-first personalization.

2026 trends to watch (late 2025 signals)

• Hybrid solver maturity: 2025–2026 saw major advances in hybrid quantum-classical offerings and more deterministic solver performance for medium-sized QUBOs.
• Learned cost models: ML-based cardinality estimators and GNN-based plan cost predictors are maturing — pairing these with quantum-inspired solvers improves plan accuracy; track these with engineering workflows like those suggested in observability-enabled workflows.
• Infrastructure commoditization: ClickHouse and other columnar OLAP vendors are offering managed, autoscaling tiers; this raises the bar for out-of-the-box performance and shifts focus to optimizer-driven cluster reduction.
• Edge and geo-distributed analytics: Optimizers that solve placement problems give measurable wins as companies distribute analytics closer to users.

Actionable next steps: run this 8-step pilot

1. Pick a representative workload (multi-way joins or daily MV selection) from production and capture a 2-week trace.
2. Spin up a tuned ClickHouse baseline cluster and run the workload to collect baseline metrics — you can use the guidance in the NextStream cloud platform review for cost and performance expectations when sizing test clusters.
3. Select a quantum-inspired solver (digital annealer, hybrid cloud solver) and prototype a QUBO mapping for your subproblem.
4. Integrate the solver as a planner-in-the-loop with a safe fallback path.
5. Run controlled A/B tests for 2–4 weeks with instrumentation and logging.
6. Measure latency percentiles, node-hours, MV storage, and solver cost. Compute net-dollar ROI and break-even time.
7. Validate at higher scale (10x concurrency) and test failure/fallback scenarios.
8. Document the process, publish reproducible configs, and decide on production rollout if ROI is positive.

Final assessment: when to bet on quantum-inspired optimizers

Quantum-inspired optimizers are no longer academic curiosities — by 2026 they are practical tools for specific, high-value combinatorial tasks inside OLAP ecosystems. But they complement, not replace, the fundamentals ClickHouse investors are funding: fast IO, efficient compression, and scale management.

If your environment has significant combinatorial decision points (hundreds of MVs candidates, complex stakeholder-driven query patterns, or cross-node placement headaches) and measurable cost levers (cluster size, storage costs), you should pilot quantum-inspired optimization. If your bottleneck is raw IO or single-table scans, invest first in storage/network and ClickHouse tuning.

Bloomberg reported (Jan 2026) that ClickHouse’s $400M round highlights how classical OLAP is being industrialized — you should treat quantum-inspired tools as a targeted amplifier for decisions that classical heuristics still struggle to make.

Key takeaways

• Don’t expect magic: quantum-inspired is an accelerator for combinatorial planning — not an engine-level replacement.
• Run focused benchmarks: design experiments for join ordering, MV selection, and partition placement and measure cost delta vs solver overhead.
• Watch infrastructure trends: new hybrid solvers and learned cost models in 2025–2026 make integrations more practical than before.
• Measure ROI: cost-per-query and node-hour savings are the right business metrics; include solver costs in your calculations.

Call to action

Ready to test quantum-inspired optimization against your ClickHouse baseline? Start with a scoped pilot: pick one combinatorial subproblem, capture traces, and run the 8-step pilot above. If you want a reproducible benchmark kit (TPC-DS config, ClickHouse tuning checklist, and QUBO mapping templates for MV selection and join ordering), request the kit from our engineering team — we’ll help you design the benchmark, instrument it, and interpret the results so you can make a data-driven decision.

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