Further Reading: Running on Real Hardware

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

The primary sources

• The IBM Quantum Platform documentation on backends and calibration data. What every field in backend.properties() and backend.target means, including the prob_meas1_prep0 / prob_meas0_prep1 pair that Case Study 1 turns on. Read the field definitions before you build any tooling on top of them — Case Study 1 exists because a field's definition mattered more than its value. Tier 1.
• The qiskit-ibm-runtime documentation on jobs, sessions, and batch mode. The authoritative statement of what each execution mode guarantees and how it is billed. §12.5 is a summary; the billing details change and the docs are where the current answer lives. Tier 1.
• The qiskit_ibm_runtime.fake_provider documentation. Every fake backend, and which real device snapshot each one carries. The whole credential-free path in this book rests on these. Tier 1.
• Your provider's device status page. Whichever hardware you actually use, find where it publishes live calibration data and queue depth, and get in the habit of reading it before you submit rather than after you are confused. Tier 1.

On the numbers that describe a device

• The Qiskit Experiments documentation on $T_1$, $T_2$, and randomized benchmarking. Where the calibration numbers come from — how a $T_1$ or a gate error is measured, not just quoted. Reading this changes how much confidence you place in the third decimal place. Chapter 30 builds on it directly. Tier 1.
• Any careful discussion of quantum volume and its successors (CLOPS, layer fidelity, EPLG). The attempt to compress a device into one comparable number, and the standing argument about whether that is useful or misleading. §12.1's position — compare on the metrics that match your circuit — is one side of that argument, and it is worth reading the other. Tier 2 — the metrics landscape moves.
• Vendor calibration-data papers and device announcements. Read one end to end and notice which numbers are quoted as medians, which as bests, and which are not quoted at all. §12.2's finding — twelve unusable qubits on a "127-qubit" device — is not hidden anywhere; it simply does not appear in the headline. Tier 2.

On the theme of Case Study 1

• Nielsen and Chuang §8.5, on distance measures and what a single fidelity number does and does not capture. The mathematical version of "a summary statistic is a lossy compression." Tier 1.
• Anything on measurement error mitigation and the assignment matrix. The full $2^n \times 2^n$ matrix — of which readout_error is a doubly-averaged scalar summary — and how to invert it. Chapter 13 §13.2 does this properly. Reading ahead here makes §12.2.1 land harder. Tier 1.
• Any treatment of Simpson's paradox or of the perils of averaging. Not a quantum topic, and exactly the failure in Case Study 1: two rates averaged into one, and the average describing neither. Worth an hour if you have never met it formally. Tier 2.

On qubit selection and layout

• The Qiskit transpiler documentation on VF2Layout and VF2PostLayout. How the noise-aware layout passes score candidates internally — a more principled version of §12.3's scoring function. Compare their objective to yours. Tier 1.
• The mapomatic package (Paul Nation and collaborators). A dedicated tool for exactly what §12.3 does by hand: enumerate valid mappings of a transpiled circuit and rank them by calibration data. If you find yourself scoring layouts often, use this instead of maintaining your own. Tier 2 — verify it is current against your Qiskit version.
• Literature on noise-adaptive compilation. The research program §12.3 is a simplified instance of. Search for noise-adaptive qubit mapping and allocation; the results are consistently large, which matches the 3.4× measured here. Tier 2.

Forward references

• Chapter 13 — mitigation, starting with readout, and §12.2.1's asymmetry is exactly what a proper assignment matrix captures and the averaged number cannot.
• Chapter 24 — the session and shot-budget arithmetic of §12.5 and §12.8, applied to a real optimizer loop.
• Chapter 26 — where step 2 of the decision procedure sends you when the answer is "it is a bug."
• Chapter 30 — measuring these numbers yourself instead of reading them off a page.
• Appendix G — the calibration-field reference, including both readout directions.

Where to go next. If one thing: open your own device's calibration data and compute $P(1|0)$ and $P(0|1)$ for every qubit. It takes ten lines, and you will learn more about the machine you are using than from any amount of reading — including, quite possibly, that one of its qubits is stuck.

Then Chapter 13 — having spent this chapter learning to route around noise, we start fighting back against it.