Further Reading: The Quantum Programming Career

Tagged Tier 1 (confident it exists and recommended) and Tier 2 (real and worth seeking, but verify the current version or URL).

The last reading list. It is shorter than most, because at this point the useful move is not more reading — it is picking one of §40.5's three areas and going deep.

The honest surveys

Read these now rather than at the start. They land differently after forty chapters of measurement.

• Preskill, "Quantum Computing in the NISQ era and beyond" (2018), Quantum 2, 79. The paper that named the era. Read it for how carefully it hedges — the caution that got stripped out downstream is all present in the original. Tier 1.
• Aaronson, "Read the fine print" (2015), Nature Physics 11, 291. Two pages, and it anticipates most of Part VI. Tier 1.
• Schuld and Killoran, "Is quantum advantage the right goal for quantum machine learning?" (2022), PRX Quantum 3, 030101. The most thoughtful response to exactly the scorecard §40.3 assembles. Tier 1.
• Hoefler, Häner, and Troyer, "Disentangling hype from practicality: on realistically achieving quantum advantage" (2023), CACM 66, 82. The single best article for the question §40.6 asks — it does the resource arithmetic in public and concludes that quadratic speedups are almost certainly not enough. If you read one thing from this chapter, read this. Tier 1.

Error correction — §40.5's first area

• Fowler, Mariantoni, Martinis, and Cleland, "Surface codes: Towards practical large-scale quantum computation" (2012), PRA 86, 032324. The standard reference, and the source of the overhead numbers Chapter 15 measured against. Tier 1.
• Google Quantum AI's below-threshold surface code results (2023–2024). The first demonstrations where adding qubits made the logical error rate go down. This is the most important experimental progress in the field, and it is progress on the thing that actually gates everything else. Tier 1.
• Literature on real-time decoding — union-find, sliding-window, and neural decoders, under microsecond latency budgets. The highest-demand engineering skill in §40.1's table. Tier 2 — moving fast.

Simulation of quantum systems — §40.5's second area

• Chapter 36's reading list in full, especially Gonthier et al. on measurements as a roadblock and the Simons Collaboration benchmark papers. Tier 1.
• Work on quantum simulation of dynamics rather than ground states — arguably a better fit for near-term hardware than VQE, and less well covered by this book. Tier 2.

Learning from quantum data — §40.5's third area

• Huang et al., "Quantum advantage in learning from experiments" (2022), Science 376, 1182. The strongest surviving separation, with hardware demonstrations. Chapter 35's recommendation and still the right one. Tier 1.
• Classical shadows (Huang, Kueng, Preskill 2020) — the one technique in this book that attacks the shot budget from the estimates-per-shot side. Tier 1.

On evidence and how fields fool themselves

Not quantum at all, and the most transferable section of this list.

• Ioannidis, "Why most published research findings are false" (2005), PLoS Medicine 2, e124. The structural argument. §40.4's seven-instance table is a small-scale instance of exactly what it describes. Tier 1.
• The reproducibility literature in machine learning — seed variance, single-run reporting, and benchmark selection. Case Study 39.2's failure is a solved problem there, and quantum computing has not adopted the solution. Tier 1.
• Feynman's "Cargo Cult Science" (1974 Caltech commencement address). Short, free, and the clearest statement of the standard §40.7 is describing: the first principle is that you must not fool yourself, and you are the easiest person to fool. Tier 1.

Practical

• The Qiskit, PennyLane, and Cirq documentation. Whichever you use, read the release notes — they are how you find out that qiskit.pulse was removed. Tier 1.
• arXiv quant-ph, filtered rather than browsed. Set an alert on the negative-results authors — the people who published barren plateaus, kernel concentration, and dequantization. They are the ones whose next paper will change what you believe. Tier 1.

Backward references

The chapters whose measurements this one assembled:

• Chapter 15 — the T-gate cliff, 450 → 2,882 physical qubits.
• Chapter 21 §21.7 — benchmarking against the method nobody uses.
• Chapter 30 §30.5 — never quote a number without naming its statistic.
• Chapter 35 — Part VI's scorecard, and the surviving case.
• Chapter 36 — the approximation that dominated everything downstream.
• Chapter 37 §37.7 — what a method produces, not how it scores.
• Chapter 38 — a guarantee that covers the wrong component.
• Chapter 39 — the queue, the bill, and reproducibility.

Where to go next. If one thing: Hoefler, Häner, and Troyer on disentangling hype from practicality. It does in one article what this book did across seven parts — take the claims seriously enough to do the arithmetic — and it reaches conclusions you are now equipped to check rather than accept.

If two: add Feynman's "Cargo Cult Science." It is about neither quantum computing nor careers, and it is the thing this book was actually teaching.

And then stop reading and go measure something. The field does not need another person who has read about quantum computing. It needs people who will run the comparison, report what came back, and say so plainly when the answer is not the one anybody wanted.

That is the whole job.