From Spintronic Memristors to Quantum Computing
Bibliographic record
Abstract
The high-speed development of the Internet of Things and artificial intelligence is revolutionizing the world in terms of industrial production, environmental protection, medical treatment, education, daily life, and so on. The powerful and fast computing methods are crucial for the advanced computing technology toward the next generation artificial intelligence. Traditional computing systems have separated logical and storage units, which cause computation time delays and increase power consumption. Spintronic memristors combine the nonvolatile characteristics of memristors with the scalability of a spin-transfer torque device, which can meet the high-speed, low-power, and scalability requirements of quantum computing (QC) for quantitative information processing. This paper reviews the research progress of spintronic memristors based on magnetic tunnel junction (MTJ), domain wall (DW) motion, and spin wave (SW), respectively, focusing on the development and challenges of spintronic memristors for QC. Finally, some problems that need to be solved urgently in the current research are summarized, and the potential applications of spintronic memristors are discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".