Running Quantum Simulators Locally on Mobile Devices: Feasibility Study Using On-device AI Techniques

Hook: Why run a quantum simulator on a phone or Pi?

Technology professionals and developers often tell us the same pain points: limited hands-on access to real quantum hardware, high cloud costs for prototyping, and a scarcity of compact, practical learning environments. What if you could run a lightweight quantum simulator entirely on a phone or a Raspberry Pi-class device — offline, private, and instant — the way modern apps run local AI models in-browser or on-device?

The short answer (inverted pyramid first)

Yes — but with important caveats. Using the same on-device AI techniques that powered the 2024–2026 wave of local LLMs and browser AI (think Puma's in-browser local AI model support and Pi AI HATs), you can build educational and prototyping-grade quantum simulators that run on modern phones and Pi-class devices. Expect practical statevector simulations up to low-to-mid 20s of qubits (with single-precision and careful memory management), far larger Clifford-only simulations via stabilizer methods, and even larger, problem-specific runs using tensor-network approximations and sparsity tricks. But full general-purpose simulation at cloud-class scales remains out of reach for on-device execution.

Why this is timely in 2026

Two trends converged in late 2025 and early 2026 to make this question ripe: first, the proliferation of on-device AI frameworks and NPUs — including lightweight local LLMs in mobile browsers like Puma and hardware add-ons such as the AI HAT+ 2 for Raspberry Pi 5 — and second, optimised compute toolchains (WASM SIMD, WebGPU, ONNX Runtime Mobile, TF Lite / Core ML improvements) that make efficient linear algebra on edge devices practical. These developments change what's plausible for quantum simulation at the edge.

Puma's model-for-browser approach and the Raspberry Pi 5 AI HAT+ 2 (2025) are examples of a larger shift: if you can run matrix-heavy ML locally, you can adapt many of the same techniques to linear-algebra-dominant quantum simulators.

The basic constraints: memory, compute, and IO

Any feasibility analysis starts with the physics of statevector simulation: memory grows as O(2^n) for n qubits. Practical trade-offs are therefore driven by three constraints:

• Memory — storing the wavefunction amplitudes (complex numbers) is the fundamental limit.
• Compute — applying gates requires large, often sparse, linear algebra operations; NPUs/GPUs can accelerate much of this when exposed as matrix-vector or shader compute.
• Latency and interactivity — educational use demands low latency and quick iteration, not maximum throughput.

Practical memory math (rule of thumb)

Using single-precision complex numbers (complex64, 8 bytes per amplitude): memory ≈ 8 * 2^n bytes. So:

• n = 20 → ~8 MB * 2^0? (2^20 * 8 bytes = ~8 MB) — trivial
• n = 25 → ~256 MB
• n = 29 → ~4 GB

That arithmetic means modern phones (with 8+ GB RAM) and Raspberry Pi 5 boards (especially with swap and NPU offload) can comfortably handle mid-20s qubit statevectors in single-precision for short sessions. Lower-power Pi variants are limited to fewer qubits.

Which simulator backends make sense on-device?

Not all simulator architectures are equally portable to edge devices. For an on-device target, consider three classes:

1. Statevector simulators — simplest and general, best for small qubit counts (education & small prototyping).
2. Stabilizer simulators — extremely fast for Clifford circuits (e.g., teaching error correction, circuits dominated by H/S/CNOT), can simulate thousands of qubits in small memory.
3. Tensor-network / contraction simulators — good for low-entanglement circuits, can extend effective qubit count for specific circuits.

Which quantum SDKs or engines can be adapted?

Most popular SDKs (Qiskit, Cirq, PennyLane, Braket) are Python-first and heavy, but their core C/C++ simulators — Qiskit Aer, Qulacs (C++/CUDA), and lightweight engines — are portable. The path is:

1. Pick a C++/Rust core (smaller, easier to cross-compile).
2. Strip or reimplement Python bindings for a minimal JS/wasm or native mobile API.
3. Expose a small, well-defined runtime (apply_gate, measure, sample) to the UI layer.

Techniques from on-device AI that transfer well

Here are concrete on-device AI techniques and how they map to quantum simulation:

• Model quantization → float16/bfloat16 wavefunctions: Using reduced-precision (16-bit) complex numbers halves memory. Many NPUs support bfloat16, which preserves dynamic range. For educational circuits, float16 is often acceptable.
• Operator fusion: As with fused kernels in local ML, fuse multiple gate ops into one kernel to reduce memory traffic and latency.
• WASM + SIMD: Compile C++/Rust simulator cores to WebAssembly with SIMD extensions for browser-based simulators (inspired by Puma's in-browser LLMs). WASM + WebGPU enables GPU acceleration in the browser.
• GPU/Compute-shader offload: Use Metal/Vulkan/WebGPU compute shaders on phones and Pi GPUs for the heavy linear-algebra kernels.
• Pruning and sparsity: For circuits that generate sparse wavefunctions, maintain compressed representations and sparse updates (mirrors ML sparse techniques).
• Distillation of workloads: Precompute expensive subcircuits or use surrogate models (classical neural approximators) for pedagogical visualizations—borrowed from surrogate-modeling used in on-device ML.

Detailed architecture options

Pick one of three architecture profiles depending on your target device and goals:

1) Browser-first (puma-like) — best for cross-platform education

Core elements:

• Compile a C/Rust simulator to WebAssembly (WASM) with SIMD and threads (where available).
• Use WebGPU for compute-heavy kernels (gate application, FFT-like permutations).
• JavaScript UI for circuit building and visualization.

Why it works: modern browsers now allow local models and NPUs; Puma demonstrates that full local AI in-browser is practical across iOS and Android. The same technique — shipping a small wasm artifact and leveraging the browser's compute — gives the best cross-platform coverage for students.

2) Native mobile app using NNAPI / CoreML / Metal

Core elements:

• Native core in C++/Rust using platform intrinsics (NEON for ARM, Accelerate/BLAS for iOS).
• Use compute frameworks: NNAPI on Android, CoreML / Metal on iOS. Map batched linear algebra to these backends for acceleration.
• Provide an interactive UI and offline mode for classroom settings.

Why it works: native access to device NPUs allows lower-level optimisations and better performance than browser/WASM in some scenarios.

3) Pi-class device with AI HAT / NPU — prototyping and local compute node

Core elements:

• Raspberry Pi 5 paired with AI HAT+ 2 (2025) or Coral-style accelerators to offload matrix ops.
• Compile a native binary that binds to the NPU runtime (e.g., vendor SDK), or use ONNX Runtime Mobile if you translate kernels to ONNX.
• Use swap and external SSD for working sets beyond onboard RAM; use float16 to reduce footprint.

Why it works: Pi-class devices are cheap, widely available in classrooms, and can be configured to run persistent, offline quantum labs. The AI HAT+ 2 unlocks generative AI for the Pi 5 and — importantly — the same memory/computation interfaces can be repurposed for quantum kernels.

Concrete example: a minimal browser statevector kernel (concept snippet)

Below is a high-level snippet that shows how a single-qubit gate application looks when written for a WASM core called from JS. This is educational pseudocode — a starting point for a production implementation.

// JS calls into wasm core
const wasm = await import('./quantum_core.wasm');
// apply single-qubit gate to statevector
// idx = target qubit, gate = [a,b,c,d] complex64 flattened
wasm.apply_single_qubit_gate(idx, gate);

// In the wasm core (C++/Rust) the kernel loops over statevector and applies
// fused complex multiplies. Use SIMD to process multiple amplitudes per cycle.

Key implementation notes:

• Use WebAssembly threads and memory growth judiciously.
• Pack complex numbers in float16 when possible on NPUs to halve memory.
• Fuse neighboring gates into single kernels to decrease read/write passes.

When to use stabilizer and tensor-network simulators

For classroom demonstrations of error-correcting circuits or large numbers of qubits with Clifford gates, the stabilizer formalism is perfect. It trades generality for extreme scalability — thousands of qubits are feasible because you represent state with stabilizer generators instead of 2^n amplitudes.

Tensor networks are ideal for circuits with low entanglement (shallow circuits or structured variational ansätze). When entanglement is low, contraction orders yield a much smaller intermediate state size, letting Pi-class devices handle circuits with effective qubit counts above naive statevector limits.

Estimating performance: realistic targets in 2026

Set expectations early. For typical modern smartphones (2024–2026 SoCs) and Raspberry Pi 5 with an AI HAT+ 2:

• Interactive demos (low-latency, <1s per gate): up to 18–24 qubits statevector with float16 and GPU/compute shader offload.
• Batch or offline runs (higher memory tolerance): mid-to-high 20s qubits with swap; not recommended for UX-sensitive teaching sessions.
• Clifford circuits: thousands of qubits are feasible with stabilizer simulators.
• Problem-specific tensor-network runs: variable, but often can exceed pure statevector limits for low-entanglement circuits.

These ranges depend heavily on memory model, precision, and whether the implementation offloads to a GPU/NPU.

Practical, actionable implementation plan (step-by-step)

1. Define the scope: Education (UI, small circuits, visualizations) or prototyping (parametric circuits, VQE-style runs)?
2. Pick simulator type: statevector for broad support, stabilizer for error-correction labs, or tensor network for targeted experiments.
3. Choose an engine: start from a small C++ or Rust core — Qulacs or a custom micro-kernel are good candidates; avoid full Python stacks for on-device delivery.
4. Cross-compile: target WASM (for browser) and native ARM builds (for Raspberry Pi / Android). Use Emscripten or wasm-bindgen for Rust.
5. Accelerate: implement compute kernels in WebGPU or Metal/Vulkan shaders. Add optional NPU paths for Pi HAT or mobile NPUs.
6. Precision strategy: default to complex64 for fidelity; provide a float16/bfloat16 mode for larger circuits and explicit user warnings.
7. UI & UX: design the interface for instant feedback; preload small demo circuits; allow export to cloud for heavy runs.
8. Benchmark & document: publish a simple benchmark matrix (device, qubits, precision, ms/gate) so others can reproduce.

Security, privacy and educational value

One of the main advantages of on-device execution is privacy and offline capability — imagine classroom labs where students prototype quantum circuits without a network connection or cloud account. Security is simpler because there is no cloud dependency, but ensure the app protects local files and sandboxing rules on mobile platforms.

Limitations and caveats

• General-purpose scalability remains limited — you will not replace cloud HPC or real quantum hardware for large-scale experiments.
• Precision vs. memory trade-offs can affect algorithmic fidelity; always validate float16 results against double precision for critical workloads.
• Hardware fragmentation (Android NPUs, iOS Metal flavours, Pi HAT SDKs) increases engineering overhead.
• Energy and thermal limits — prolonged heavy simulation will thermally throttle phones and Pi boards, so design for short interactive sessions or external cooling if needed.

Case study proposals you can run this week

Two compact experiments to validate feasibility on real devices (replicable by developers and educators):

1. Browser demo (Pixel or iPhone)
   • Target: 10–18 qubits statevector using WASM+WebGPU and float32.
   • Steps: compile minimal statevector core to WASM, implement gate kernels in WebGPU shaders, build a JS UI for circuit edit and single-step execution.
   • Outcome: interactive visual demonstration of interference and entanglement, offline, without backend.
2. Raspberry Pi 5 lab
   • Target: 20–24 qubits with float16 using AI HAT+ 2 for linear-algebra acceleration.
   • Steps: native C++/Rust binary that binds to the HAT SDK; measure ms/gate and memory; provide pre-built SD image for classroom use.
   • Outcome: a cheap, local quantum lab for group workshops and prototyping variational algorithms.

Future directions and predictions (2026–2028)

Expect steady improvements in three areas:

• Edge NPUs and better float16/bfloat support will make higher-qubit demos practical on Pi-class nodes.
• WebGPU and WASM ecosystem maturity will reduce the friction of delivering cross-platform browser-based simulators.
• Hybrid workflows will standardise: run interactive exploration on-device, export heavy shots or full-scale simulations to cloud HPC seamlessly. See work on micro-regions & edge-first hosting for broader infrastructure trends that support these hybrid paths.

Concretely, by 2028 we expect classroom-grade simulators that run 25–30 qubits in single-precision on edge devices as a common tool, plus robust tooling to validate float16 runs against cloud double-precision references.

Actionable takeaway checklist

• Start with a small C++/Rust core — avoid Python for device delivery.
• Target WASM + WebGPU for maximum reach; add native builds for Pi and iOS when needed.
• Use float16/bfloat16 mode for memory-bound runs; always offer a double-precision validation path in CI.
• Leverage stabilizer and tensor-network tactics for larger effective qubit counts in teaching scenarios.
• Document device-specific benchmarks and publish an SD image for Pi labs.

Where to start (resources & suggested repos)

Build from these building blocks:

• A minimal statevector engine in Rust or C++ compiled to WASM (look at wasm-bindgen and Emscripten samples).
• WebGPU compute shader examples for matrix-vector work.
• Vendor NPU SDKs for Raspberry Pi HATs (AI HAT+ 2) for offload bindings.
• Small educational UIs that focus on visuals (Bloch spheres, probability bars, gate stepper).

Final thoughts

Running quantum simulators locally on phones and Pi-class devices is not only feasible in 2026 — it is practical for education and constrained prototyping. The same engineering tactics that enabled local LLMs in browsers and edge generative AI (as seen with Puma and Pi AI HATs) translate well when you reframe quantum simulation as a memory- and linear-algebra-first problem. Expect quick wins for educators and developers who prioritise efficient kernels, reduced precision, and hybrid workflows.

Call to action

Ready to prototype a mobile quantum demo? Start a small repo today: pick a 2–6 qubit demo, compile the core to WASM, and ship a one-page browser UI. Share your benchmark results or reach out to the qbit365 community to collaborate on Pi SD images and teaching materials — let's make hands-on quantum learning truly local and accessible.

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