Frequency- and dissipation-dependent entanglement advantage in spin-network quantum reservoir computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We study the performance of an Ising spin network for quantum reservoir computing in linear and nonlinear memory tasks. We investigate the extent to which quantumness enhances performance by monitoring the behavior of quantum entanglement, which we quantify by the partial transpose of the density matrix. In the most general case where the effects of dissipation are incorporated, our results indicate that the strength of the entanglement advantage depends on the frequency of the input signal; the benefit of entanglement is greater with more rapidly fluctuating signals, whereas a low-frequency input is better suited to a nonentangled reservoir. This may be understood as a condition for an entanglement advantage to manifest itself: the system's quantum memory must survive long enough for the temporal structure of the input signal to reveal itself. We also find that quantum entanglement empowers a spin-network quantum reservoir to remember a greater number of temporal features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it