osxQ — Apple Silicon’s First Native Quantum Simulator

Built for Apple Silicon

QNeura created osxQ — the first native quantum simulator for Apple Silicon. Fully Python‑integrated and MLX‑accelerated, it turns M‑series hardware into a single‑node, research‑grade state‑vector platform — without CUDA. Designed for scientists in materials and life sciences, osxQ exploits unified memory (128–512 GB) to deliver parity with cuQuantum and Qulacs. With an open QASMBench‑style suite, reproducible results, and 200+ curriculum‑ready examples, osxQ bridges education, experimentation, and discovery.

The Product: osxQ

Highlights

Python‑first • MLX‑accelerated Unified memory (Apple Silicon) OpenQASM 2.0 suite VQE • QAOA • QFT • PE • GHZ • Grover Parity vs PennyLane/Qulacs/cuQuantum

GHZ(3) — live circuit animation

Shots: 0

Last: —

P(000): 0.0%

P(111): 0.0%

000

111

osxQ is a comprehensive, Apple‑Silicon‑native state‑vector simulator built on MLX. It targets Metal with unified memory to execute quantum circuits efficiently on M‑series hardware — no CUDA required.

It reproduces community benchmarks (Yao.jl, PennyLane, Qulacs), performs parity checks against cuQuantum (cuStateVec), and executes a QASMBench‑style OpenQASM suite. The library includes circuit drawing (ASCII, Matplotlib, Quantikz), exact and shot‑based measurement, and 200+ executable tests with analytical checks.

Designed for single‑machine deployments with 128–512 GB unified memory, osxQ places research‑grade workloads within reach of universities, small labs, and individual researchers.

Quick Start

Get Started on Apple Silicon

macOS 13.3+, Apple Silicon (M1–M4), Python 3.11+, MLX

Validate your setup, run the test suite, and generate benchmark plots in minutes.

# Run validation tests (pretty output)
export PYTHONPATH=src
export MLXQ_PRINT_ASCII=1   # optional: print ASCII circuits
python3 src/tests/run_core_tests.py

# Run algorithm benchmarks (saves plots)
export PYTHONPATH=src
python3 src/benchmark/bench.py

# Textual TUI (optional)
python3 -m pip install textual
python3 src/benchmark/bench_tui.py

OpenQASM notes. Place QASMBench‑style circuits under datasets/qasm/local/ and adjust caps with QASM_MAX_QUBITS and QASM_TIMEOUT_MS as needed.

Docs More Examples

Services

We partner end‑to‑end—from strategy and roadmaps to prototypes and production—to implement GenAI and quantum capabilities that move the needle.

AI Strategy Consulting

Strategic guidance on AI implementation, technology selection, and organizational transformation for quantum-ready enterprises.

Quantum ML Development

Custom quantum machine learning solutions, algorithm development, and hybrid classical-quantum system design.

Technology Integration

Seamless integration of quantum-enhanced AI capabilities into existing business workflows and technical infrastructure.

EXPLORE ALL SERVICES

Get in touch

Ready to try osxQ or discuss integration and research use cases? Reach out and we’ll help you get started.

QNeura.ai

osxQ — Apple Silicon Quantum Simulator

shlomo@qneura.ai

About Shlomo Kashani

Founder, QNeura.ai

Creator of osxQ — Apple Silicon’s first native quantum simulator

Shlomo Kashani is an AIMO‑2 Gold Medalist and published author (Deep Learning Interviews). He leads QNeura.ai and authored osxQ — a unified‑memory, MLX‑accelerated simulator built for Apple Silicon.

Academic training includes Johns Hopkins (M.Sc. Quantum Physics/Computing), Queen Mary University of London (M.Sc. DSP), and Ben‑Gurion University (B.Sc. Engineering). His work spans quantum information, algorithms, and AI systems.

For collaborations or access, email shlomo@qneura.ai.