Quantum ML always beats classical ML

This reasoning has two flaws. First, quantum computers are not universally more powerful — they have specific advantages for specific problem structures. Second, classical ML has decades of optimization, hardware acceleration (GPUs, TPUs), and mature tooling. Huang et al. (2021) showed that for most classical datasets, classical kernel methods can match quantum kernel methods by constructing a classical kernel that mimics the quantum feature map. Quantum ML advantage requires problem structure that is genuinely hard to capture classically — and such structure is rare.

This concept requires either mathematical proof or hardware-scale experiments beyond what a browser simulator can demonstrate. See the research notes for the canonical references.