Optimization of Approximate Quantum Random Access Memory for NISQ Devices
Bibliographic record
Abstract
An integral aspect of quantum supremacy is the implementation of quantum queries which enable quantum circuits to process quantum data. Quantum random access memory (QRAM) is a promising architecture for realizing these queries. However, implementing QRAM on contemporary quantum computers presents significant challenges due to the substantial noise it introduces. In this paper, we propose a technique that identifies gates with small rotation angles and removes them to simplify QRAM circuits, addressing the challenge of quantum noise on NISQ devices. Among various QRAM architectures, approximate QRAMs exhibit unique characteristics that make them particularly suitable for quantum neural networks (QNNs). Approximate QRAMs leverage trainable QNNs to provide approximate data for a given address that closely matches the exact data. Since QNNs are inherently resilient to errors, the approximate nature of these QRAMs enables them to achieve high accuracy. Additionally, the approximate nature of QRAMs creates opportunities for reducing the number of gates and overall circuit depth. To mitigate potential accuracy loss, we retrain the pruned QRAM to maintain the performance of the original QRAM. Our evaluation on a real quantum computer demonstrates that pruning enhances the robustness of approximate QRAMs, allowing them to function effectively on NISQ devices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".