Quantum computing SDK fragmentation forces developers to rewrite algorithms for each hardware vendor's proprietary framework

A quantum algorithm developer who writes code in IBM's Qiskit cannot run it on Google's hardware (which uses Cirq), Rigetti's systems (which use PyQuil/Forest), Microsoft's platform (which uses Q#), or D-Wave's annealers (which use Ocean SDK). Each SDK has different abstractions for quantum circuits, different optimization passes, different noise models, and different APIs for job submission. There is no portable quantum intermediate representation that works across all major hardware platforms the way LLVM IR works for classical compilers. Why it matters: Because algorithms must be rewritten for each platform, researchers and enterprise developers must invest 2-4x the engineering effort to benchmark their application across multiple hardware vendors, so most users just pick one vendor and stay locked in without knowing if another platform would perform better for their workload, so vendor lock-in becomes the default rather than a choice, so the quantum computing market develops winner-take-all dynamics that favor marketing spend over technical merit, so smaller quantum hardware startups with potentially superior technology cannot compete because they lack the developer ecosystem. The structural root cause is that quantum computing hardware architectures are so fundamentally different from each other (gate-based superconducting, gate-based trapped ion, measurement-based photonic, quantum annealing) that a truly universal abstraction layer would necessarily sacrifice the hardware-specific optimizations that are critical for getting useful results from today's noisy, error-prone machines. The QIR (Quantum Intermediate Representation) initiative and OpenQASM 3.0 standard exist but have incomplete adoption. Each vendor has a strategic incentive to maintain proprietary SDKs because developer lock-in is their primary competitive moat in a market with almost no paying customers.