Qwallet: A Hybrid Cryptocurrency Wallet using Quantum RNG
Bibliographic record
Abstract
Blockchain wallets use two primary key generation schemes: non-deterministic (ND) and hierarchical deterministic (HD). ND key generation scheme provides better fund distribution but have issues with backup complexity and memory utilization. HD key generation scheme simplifies the backup process but is vulnerable to privilege escalation and brute-force attacks. In addition, deterministic pseudo-random algorithms used in these key generation schemes are predictable, which makes Quantum Random Number Generators (QRNGs) a promising alternative. This paper proposes Qwallet: a hybrid wallet based on the user's behavior that utilizes both HD and ND key generation architecture while leveraging QRNG to generate the keys. The wallet is optimized through deep learning, which trains on user behavior to select the optimal key generation scheme for maximum efficiency in blockchain wallet usage. We implemented and evaluated our proposed solution to support Ethereum transactions. Our results show that Qwallet reduces risk by up to 98% compared to traditional HD wallets, while consuming similar memory resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".