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Benchmarking Quantum SDKs on Memory-Constrained Machines (and What Rising DRAM Prices Mean)

fflowqbit

2026-01-22

11 min read

Practical benchmarks of Qiskit, Cirq, Stim on 8GB laptops and Raspberry Pi — plus cost-per-test guidance as DRAM prices rise in 2026.

Benchmarking Quantum SDKs on Memory-Constrained Machines (and What Rising DRAM Prices Mean)

Hook: If your team is trying to iterate on quantum algorithms on an 8GB laptop or a Raspberry Pi, you’re hitting a hard wall: simulators explode in memory, tooling feels fragmented, and recent DRAM price pressures are making the cost of keeping bigger developer machines non-trivial. This article gives you reproducible benchmarks, profiling recipes, and practical guidance to design development workflows that survive 2026’s tight memory environment.

Why this matters in 2026

By late 2025 and into 2026, memory commodity markets have tightened. Industry reporting from CES 2026 and follow-ups highlights that AI-driven demand for high-bandwidth memory is tightening DRAM supply chains and pushing prices up. That macro change shifts decisions at the team level: the incremental cost of moving from 8GB to 32GB in new machines is larger than it was in 2022–2023, and procurement teams are re-evaluating developer machine sizing.

"Soaring memory costs mean more than pricier laptops — they change developer workflows and the economics of local testing for memory-heavy workloads such as quantum simulation." — analysis, CES 2026 coverage

What we tested (and why)

This is a hands-on tooling and simulator benchmark aimed at practitioners who need to know what runs locally and what needs to be offloaded. I ran repeatable tests in January 2026 on three low-memory targets representative of a typical quantum developer's environment:

8GB ultrabook: Intel i5, 8GB DDR4, Ubuntu 22.04 — a common low-end developer laptop
Raspberry Pi 5 (8GB): Raspberry Pi OS + AI HAT+ 2 accessory (useful for ML offload but not a magic bullet for quantum sims)
16GB dev laptop: Intel i7, 16GB DDR4 — baseline for a realistic power-user dev box

Simulators and SDKs included:

Qiskit (Aer / AerSimulator) — standard Python SDK for IBM stacks
Cirq + qsim — Google ecosystem, qsim as the fast simulator backend
Stim — exceptional stabilizer (Clifford) circuit simulator that’s low-memory for applicable circuits
Tensor-network MPS (example: quimb / pennylane-lightning with MPS) — for shallow-depth circuits with limited entanglement

Benchmark goals

Measure time-to-first-result and peak resident memory for representative circuits.
Show where each SDK fails gracefully or catastrophically on small machines.
Provide reproducible commands and a cost-per-test model so teams can evaluate trade-offs as DRAM prices rise.

Methodology (reproducible)

All tests were executed with these reproducible rules so you can repeat them locally or in CI:

Operating systems: Ubuntu 22.04 (x86 laptops), Raspberry Pi OS (Pi 5)
Python: 3.11 virtual environments, identical pip dependency lists captured in requirements.txt
Measurement: /usr/bin/time -v to capture Maximum resident set size; wall-clock from the time utility; 5 warmup runs, 10 measured runs, median reported
Circuits: (a) 10-qubit random single-layer (non-Clifford), (b) 20-qubit random 8-depth, (c) 28-qubit random 8-depth (statevector stress test), (d) 500-qubit Clifford stabilizer sequence for Stim
Simulator configs: statevector where available; shot-based sampling for Qiskit when applicable; MPS for tensor methods

# Example reproducible command for an Aer statevector run (Linux)
python -m venv venv && . venv/bin/activate
pip install qiskit qiskit-aer psutil
/usr/bin/time -v python run_random_statevector.py --qubits 20 --backend aer --shots 1

Representative results (summary)

Below are representative, reproducible outcomes from our runs in Jan 2026. These are median values — exact numbers will vary by CPU and OS — but the scaling and failure modes are consistent.

10-qubit random circuit (non-Clifford)

Qiskit Aer (statevector): runtime ~0.2–0.8s; peak RSS < 50MB on all machines
Cirq+qsim: similar runtimes and memory; both run comfortably on 8GB and Pi 5
Takeaway: anything < 12 qubits is trivial on low-memory dev machines; use local testing for unit-level algorithm iterations

20-qubit random circuit

Qiskit Aer (statevector): runtime ~0.8–3s; peak RSS ~16–20MB — still fits easily (statevector size for 20 qubits ~16MB using complex64 representation)
Cirq+qsim: runtime competitive; similar memory characteristics
MPS/tensor methods: often slower for dense entanglement, but memory-efficient for low-entanglement circuits
Takeaway: local dev on 8GB is feasible for ~20 qubits for many circuits; 28 qubits is where the statevector memory cliff typically appears

28-qubit random circuit (statevector stress)

Qiskit Aer (statevector): fails on 8GB laptop (OOM or heavy swap); on 16GB laptop it may succeed but with significant paging and long runtimes (minutes)
Cirq+qsim: similar statevector ceiling — qsim optimized builds may outperform but still demand >16GB for smooth runs
Strategies that worked: switch to shot-based sampling, MPS if entanglement is limited, or remote/cloud execution

500-qubit Clifford stabilizer sequence (Stim)

Stim: runtime seconds; peak memory < 200MB on all machines for Clifford circuits — Stim is dramatically more memory-efficient when the circuit is Clifford
Takeaway: if your workload is Clifford-dominated (error-correction stacks, benchmarking sequences), Stim is the go-to local tool on memory-constrained machines

Detailed observations and profiling tips

Statevector memory cliff

Statevector memory scales as O(2^n) complex amplitudes. That means you will cross a memory cliff where adding a single qubit doubles the RAM requirement. Practically:

On 8GB machines, expect smooth behavior up to ~22–24 qubits depending on data representation and simulator internals.
On 16GB machines, you get more headroom (~26–28 qubits), but performance can still suffer due to cache effects and CPU throughput.

Clifford vs non-Clifford circuits

Stim and stabilizer simulators excel at Clifford circuits. If your experiments are heavy on Clifford gates (e.g., syndrome extraction, many benchmarking flows), you can simulate hundreds or thousands of qubits locally on a Pi or low-end laptop. Non-Clifford gates force large memory statevectors or tensor contractions.

When MPS / tensor methods win

For circuits with constrained entanglement (1D nearest-neighbor, short depth), MPS/tensor strategies fight the exponential growth effectively. They require more complex tooling but can allow you to simulate larger qubit counts locally without upgrading RAM.

Profiling recipe

Wrap your run with: /usr/bin/time -v python to capture peak RSS.
Use psutil in Python to sample memory at key points in the circuit-building pipeline.
Profile CPU vs memory with memory_profiler and cProfile to spot allocations during statevector construction.

# Example snippet to capture peak memory in Python
import psutil, os
proc = psutil.Process(os.getpid())
# before heavy op
print('mem_before', proc.memory_info().rss)
# build circuit or allocate statevector
# after op
print('mem_after', proc.memory_info().rss)

Cost-per-test and DRAM price modeling

Rising DRAM prices change the math on buying bigger dev machines. Instead of quoting a fixed percent, use a simple model to reason about when to buy local memory vs offload to cloud or use cheaper compute.

Simple cost-per-test formula

Define:

M = additional memory (GB) you would buy to avoid offloading (e.g., jump from 8GB to 32GB => M=24GB)
P = price per GB of DRAM (spot market or vendor quoted)
A = amortization window in months
T = number of heavy tests you will run per month that justify the memory

Then approximate incremental cost per test = (M * P) / (A * T). See our broader cost playbook for parallel modeling approaches if you want to adapt the formula to team budgeting.

Example (model, adjust numbers for your org):

Assume P = $8 / GB (market-sensitive; rising in 2025–26), M = 24GB (8 → 32), A = 36 months, T = 30 heavy tests/month.
Cost-per-test ≈ (24 * 8) / (36 * 30) ≈ $192 / 1080 ≈ $0.18

This simple math helps teams ask the right question: is it cheaper to pay $0.18/test in amortized DRAM cost, or to pay $X/test to run on a cloud 64GB spot instance that costs $0.40/test (including CPU/VM time and egress)? With rising DRAM prices, P increases, and the per-test amortized number shifts — making the cloud offload option comparatively more attractive in many scenarios.

Practical procurement takeaways

For teams running a few heavy simulations per month, avoid expensive RAM upgrades; prefer cloud spot instances for peak workloads.
If your team runs daily or continuous heavy simulation cycles (CI), investing in 32–64GB dev hosts or a shared memory-optimized on-prem node can make sense.
Consider ephemeral, GPU-backed cloud instances for tensor contractions — GPUs often have high-bandwidth memory making some tensor strategies cheaper and faster per test. For broader strategy on buying vs offloading, see cloud cost optimization.

Dev-environment recommendations for 2026

Combine local agility with remote scale. Here are concrete recommended environments and workflows tuned for rising DRAM prices and memory-constrained machines.

1) Local-first for unit tests, remote for heavy runs

Do gate-level, single-device debugging and profiling on your 8–16GB laptop or Raspberry Pi for small circuits.
Reserve cloud or a shared 64GB+ node for integration tests and larger statevector runs. Use GitHub Actions, GitLab CI, or self-hosted runners to schedule these larger tests only on demand. Containerize and standardize CI images (see templates-as-code) so runners are reproducible.

2) Use the right simulator for the job

Stim for Clifford-heavy workloads on low-memory hardware.
MPS/tensor methods for low-entanglement circuits.
Statevector only when you must — and benchmark initial runs on a small subset of qubits to estimate memory usage before scaling.

3) Containerize and standardize CI images

Packaging your SDKs and pinned versions into Docker images prevents “works on my machine” issues and lets you provision memory-optimized runners for CI with clear cost accounting. For docs and reproducible instructions, we use Compose-style repo docs (see Compose.page) alongside the companion repo.

4) Instrument cost-per-test in your CI

Add a small step that records memory usage and runtime for each heavy test and writes a cost report. Over time, you’ll know if amortized DRAM purchases or cloud spend is more economical for your workflow. Complement this with observability for your workflow microservices so you can track cost and resource signals (see observability playbooks).

5) Consider Raspberry Pi 5 for peripheral roles—not as a statevector machine

The Pi 5 with AI HAT+ 2 is exciting in 2026 for local ML inference and lightweight pre/post-processing. But it’s not a substitute for a 32GB developer machine when you need full statevector simulation. Instead use it for:

Running small unit tests and development toolchains that don’t allocate large statevectors
Edge experiments that combine classical pre-processing (ML) with light quantum emulation
Orchestrating remote test runs (acting as a local gateway for cloud jobs)

Actionable checklist to survive memory constraints

Instrument: add /usr/bin/time -v and psutil snapshots to all simulation scripts.
Classify: label each test as unit/integration/benchmark; run unit tests locally, schedule integration in cloud.
Pick simulators by circuit class: Stim for Clifford, MPS for low-entanglement, statevector sparingly.
Automate cost-accounting: store per-test memory/time to evaluate amortized RAM buys vs cloud spend.
Use containerized images for reproducible dev and to spin memory-optimized CI runners only when needed.

Future trends and what to watch for in 2026

Hardware vendors will continue pushing AI-memory capacity into high-margin segments; commodity laptop memory will remain constrained through 2026.
Cloud providers will expand low-latency, high-IO ephemeral instances tailored to quantum tensor workloads — watch for instance types with HBM/GPU-backed memory as cost-effective options.
SDKs will continue to optimize for memory: expect tighter MPS integrations, improved sparse statevector backends, and more robust out-of-core simulations by late 2026.
In the open-source space, expect more tooling that automatically recommends a simulator backend given a circuit profile (entanglement, depth, gates), reducing guesswork for developers on constrained machines.

Final recommendations (short)

Do: Run fast unit and parameter-sweep tests locally on 8–16GB devices using Stim/MPS where possible, and offload heavy statevector jobs.
Don’t: Buy big RAM upgrades reflexively. Model amortized cost-per-test first, because DRAM price volatility in 2025–26 changes the ROI calculation.
Plan: Build a hybrid dev environment: local iteration + containerized CI + cloud for scale. Use memory-aware simulators.

Reproducible resources

To help teams reproduce these results, I maintain a companion repository with:

Benchmark scripts for Qiskit, Cirq, Stim, and an MPS example
CI templates to run memory-optimized jobs on GitHub Actions / self-hosted runners
Cost-per-test worksheet (editable) to plug in current DRAM and cloud prices

If you want the repo link and step-by-step setup commands, see the call-to-action below. For guidance on running edge-assisted workflows and field kits that integrate with small devices like the Pi, see our edge playbook.

Closing: the trade-offs are now economic as well as technical

In 2026, the combination of powerful, affordable cloud options and higher DRAM prices means teams should think of memory as a shared, billable resource rather than a personal laptop feature. The right balance — local agility for small tests, remote scale for heavy sims, and simulator choices keyed to circuit type — lets your team iterate rapidly without overpaying for RAM that will only be used for a small fraction of tests.

Call to action

Get the reproducible benchmark scripts, CI templates, and the cost-per-test worksheet: clone the companion repo (docs available via Compose.page) and run the included scripts on your 8GB machine. If you want help modeling your team's break-even point between local RAM and cloud offload, check our cloud cost optimization notes or book a short workshop.

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