An Abstraction Hierarchy Toward Productive Quantum Programming
Bibliographic record
Abstract
The computer industry, with over seven decades of experience to draw on, has shown that to thrive, there must be a community of software engineers that produce the applications users depend on. Supporting those software engineers so they can write code that performs well across multiple generations of hardware (since application software generally lasts longer than hardware) is fundamental to sustaining the computer industry. Today's quantum software developers must reason at a low level, close to the hardware, which is not sustainable. In this paper, we assert that quantum computing needs high-level abstractions that support a quantum computing applications ecosystem. A single abstraction that bridges from the mental models used by a programmer to the details of how qubits are realized in hardware is unlikely to work. We need a hierarchy of tightly coupled models that define a framework for reasoning about the software development process in quantum computing. We propose an abstraction hierarchy and then explore its utility with two approaches to the eigenvalue estimation problem: (1) a variational algorithm with error mitigation, and (2) phase estimation with error correction. We use our proposed abstraction hierarchy to pinpoint key differences between these approaches and demonstrate how an abstraction hierarchy helps us understand software development. This supports our central conclusion; that it is not enough to understand individual components of a software stack. To make progress, we need to think about an abstraction hierarchy holistically.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| 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".