Bibliographic record
Abstract
Distributed quantum processing over local optical networks is a route to fault-tolerant quantum computing at scale and practical quantum advantage. The performance of modular, networked quantum technologies will, however, be contingent upon the quality of their light-matter interconnects. Silicon colour centres offer optically-coupled spin qubit registers as the basis for quantum networks and distributed quantum computing. Silicon is an ideal platform for commercial quantum technologies: it unites advanced photonics and the microelectronics industry, as well as hosting long-lived spin qubits. The silicon T centre was recently discovered to combine direct telecommunications-band photonic emission, long-coherence electron and nuclear spins [1,2], and proven integration into industry-standard, CMOS-compatible, silicon-on-insulator (SOI) photonic chips at scale. In this talk I present recent advances networking T centres with nanophotonics. We enhance the optical emission rate by an order of magnitude with integrated nanocavities to create coherent optical interfaces. We determine the T centre’s hyperfine spin qubit coupling and introduce schemes for operating each T centre as a deterministic four-qubit spin register. T centre devices producing spin-entangled photons can make immediate use of integrated silicon photonic networks boasting low-loss active components, efficient coupling to standard telecommunications fibres, and efficient on-chip photon detectors. These elements may be assembled to create an on-chip spin-photon quantum processor that interfaces with optical fibres for long-range communication over the quantum internet.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".