arXiv:2607.02444v1 Announce Type: cross Abstract: We study stabilizer state testing and learning with limited coherent quantum memory. Here an algorithm sequentially receives copies of an unknown $n$-qubit state, but may keep only $k$ qubits of coherent quantum memory between measurements. With unrestricted memory, seminal work of Gross, Nezami and Walter showed how to test $n$-qubit stabilizer states using $6$ copies, which is dimension independent, unlike the learning complexity of $\Theta(n)$. We show that this testing-vs-learning separation is lost under memory constraints. More concretely
This paper in 2026 explores fundamental limitations of quantum computing with restricted memory, a crucial engineering challenge as quantum hardware scales.
It reveals that practical constraints like limited quantum memory can significantly alter theoretical efficiency gains in quantum algorithms, directly impacting feasibility and development timelines.
The prior assumption of a 'dimension independent' complexity for certain quantum tasks is now challenged under realistic memory constraints, showing a new trade-off between memory and computational efficiency.
- · Researchers in quantum memory and error correction
- · Developers of quantum architectures with high memory coherence
- · Quantum computing projects relying on unbounded memory assumptions
Limited quantum memory is identified as a critical bottleneck for certain quantum algorithms, preventing previously theorized efficiency gains.
This necessitates greater investment in quantum memory research or development of new algorithms that are robust to memory constraints.
It could extend the timeline for the commercial viability of certain quantum computing applications, requiring more fundamental breakthroughs.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG