mlxQ: Unified Memory Quantum Simulation on Apple Silicon via the MLX Framework
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Excited to share that my paper has been accepted as a Full Paper at QUANTICS 2026, with a 25-minute oral presentation slot. https://preview.redd.it/ik5l3gufzjzg1.png?width=2988&format=png&auto=webp&s=5343094b08396d2beed97f0e4d75bea7e28fe8a5 Title: "mlxQ: Unified Memory Quantum Simulation on Apple Silicon via the MLX Framework". Open source: https://github.com/BoltzmannEntropy/osxQ The goal was simple: make quantum algorithm development accessible to anyone with a Mac, without compromising on rigor. For years, serious quantum circuit simulation has required NVIDIA CUDA infrastructure, discrete GPUs, explicit memory transfers, and specialized hardware that puts it out of reach for most researchers and educators. Built on Apple's MLX framework, mlxQ is the first comprehensive quantum circuit simulator designed from the ground up for unified memory, where the CPU and GPU share the same physical memory pool, eliminating the PCIe transfer overhead that dominates shallow-circuit execution on traditional GPU setups. What it does: - Full state-vector simulation up to 25+ qubits on consumer hardware - Automatic Metal GPU acceleration — no shader programming required - Complete algorithm suite: VQE, QAOA, QCBM, QFT, Grover, Hamiltonian simulation - OpenQASM 2.0 import for standardized benchmarking - 235 regression tests with analytical pen-and-paper verification - 12 complete tutorials with mathematical derivations - A fully working terminal UI (QuantumStudio) to run the entire benchmark suite interactively - Benchmarked against PennyLane, Yao.jl, Qulacs, and cuQuantum On M1 Max hardware: 25-qubit QFT in 7.03s, QAOA in 11.07s, Hamiltonian simulation in 40.73s — all on a MacBook. https://boltzmannentropy.github.io/osxQuantumWEB/ submitted by /u/QuanstScientist [link] [comments]