Pragmatic Obfuscation of Factoring in Hamiltonian Simulation and Ground State Estimation
Bibliographic record
Abstract
The advent of large-scale quantum computers promises transformative advances across various fields including optimization, materials science, and cryptography. However, this also poses a threat to traditional cryptography, due to Shor’s algorithm, which efficiently factors large integers, and finds discrete logarithms. The existence of this algorithm undermines widely-used cryptographic protocols based on the hardness of these problems. Even with Post-Quantum Cryptography, attacks of the "save now, decrypt later" type can compromise the confidentiality of long-term secrets. Keeping this mind, we would like to develop quantum systems that are designed specifically for benign applications such as Hamiltonian Simulation or Ground State Estimation – which could be of importance to the industries such as the pharmaceutical industry. However, it cannot be taken for granted that even such a system is secure from malicious users attempting to run Shor’s Algorithm. In this note we propose the idea of using known circuit-to-Hamiltonian mappings to hide Shor’s algorithm in Hamiltonian simulation and Ground state estimation circuits. We provide the resource estimates for these mappings, and also propose some methods to potentially reduce these overhead costs.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".