Secret Addressing Scheme using Distributed Quantum Computing
Bibliographic record
Abstract
Within the evolving landscape of confidential and trustworthy computing, this paper delves into the prevalent challenges such as secrecy or privacy associated with existing addressing schemes. Taking these issues into consideration, we propose a novel solution, drawing inspiration from distributed quantum computing algorithms, to generate a secret addressing scheme. The objective is to enhance the confidentiality and reliability of computing systems. This proposed solution, inspired from quantum phase estimation (QPE), assigns a unique and confidential address to each node in a network. It also dynamically adapts to changes in the network or system configurations by using the proposed QPE-inspired approach for every new incoming node in the network. We have implemented our solution on a quantum network simulator, namely NetSquid, to assess the effectiveness of our proposed solution under varying conditions, including scenarios with and without noise models. The simulation results of our proposed solution demonstrate both scalability (e.g ., it takes 34.5ns to address a single computing node with a 3-bit address and 230ns with up to 20-bit addresses) and accuracy (e.g ., success probability of intended address distribution without noises is 100% and with mild noises it is roughly 80%). Our proposed solution effectively handles the growth of the network while ensuring a consistently high level of accuracy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".