The growing memory demands of artificial neural networks pose a major challenge for energy-efficient computing, particularly for models that rely on large collections of high-dimensional weight and embedding vectors. Conventional memory architectures store these vectors individually, requiring many memory cells per vector and limiting achievable storage density. Here we demonstrate an analog memory architecture that encodes multiple vectors in a distributed superposition within a single memory array. Individual vectors are recovered through in-memory matrix-vector multiplication, allowing the degree of superposition to be tuned to balance storage density against recall fidelity. This approach introduces a new scaling dimension for analog memory, in which effective storage density can be increased without a corresponding increase in the number of physical memory cells. The distributed representation also provides resilience to device-level noise. We implement the architecture on a prototype phase-change memory chip and experimentally evaluate it using embedding tables from deep learning recommendation models and large language models. The results demonstrate inference accuracy comparable to full-precision software baselines, establishing analog superposition as a promising route towards dense, energy-efficient memory for artificial intelligence.

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