Loading
Life sciences · Preprint
arXiv · September 9, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint derives theoretical bounds on the sample complexity of quantum entanglement allocation under Pauli queries, characterizing excess error as proportional to k^−1·min{1,√(d·log(k+1)/m)} for k-qubit groups over m requests. Experimental validation on a 15-qubit device and retail data is reported, but peer review, detailed sample sizes, and comparison to established baselines are absent from the abstract.
Theoretical analysis with empirical validation on quantum hardware and retail data. Quantum systems with d-qubit paths and groups of ≤k qubits; retail transaction baskets.. Intervention: Entanglement allocation strategies: full chain encoding and frequency grouping.. Compared with: Basket search and unspecified baseline methods..
Minimax excess error for d-qubit path with groups of ≤k qubits after m requests is proportional to k^−1·min{1,√(d·log(k+1)/m)}, uniformly for 2≤k<d Connected biclique regions can grow without increasing sample demand when depth, region count and connections per region remain bounded Preparation noise introduces separate calibration requirement quantified by tradeoff with detector calls
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is an unreviewed preprint presenting theoretical quantum information analysis with experimental validation on quantum hardware and retail data; peer review status unknown.
As stated by the source record.
Quoted from the source exactly as published.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
How many past requests are needed to decide which qubits should share entanglement? We show that the answer depends on the allocation choices created by the queries: a larger memory can require no more data. The memory stores a classical bit and answers requests through a fixed detector that preserves coherence within each measured sector. For independent commuting $X$- and $Z$-type Pauli queries, we characterize the full attainable prediction-contrast region and construct encodings that preserve the bit at every nonzero vertex. With sharp reports, a $d$-qubit path and groups of at most $k$ qubits have minimax excess error after $m$ requests proportional to $k^{-1}\min\{1,\sqrt{d\log(k+1)/m}\}$, uniformly for $2\leq k<d$. Connected biclique regions can grow without increasing sample demand when depth, region count and connections per region stay bounded. Preparation noise introduces a separate calibration requirement. We derive an exact tradeoff with extra fresh detector calls and transfer the learning law to structured transaction co-location. Population-risk experiments test the statistical predictions. We also compare encodings on a native 15-qubit device and learned partitions on public purchase baskets. The full chain wins on the device; frequency grouping outperforms basket search in the largest-capacity retail setting.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.