Bibliographic record
Abstract
We present a conceptually clear introduction to quantum theory, deriving the theory from scratch from the point of view of quantum information. Different subsets of these lectures were taught to a wide variety of audiences, including exceptional high-school students in the International Summer School for Young Physicists (ISSYP) at Perimeter Institute, 2nd-year physics undergraduates at the University of Toronto, and 4th-year physics and math undergraduate and graduate students at Brock University. The lectures are completely self-contained, including all the necessary mathematical background: complex numbers, linear algebra, and probability theory. They cover topics such as the axioms of quantum theory, qubits, superposition, entanglement, the uncertainty principle, quantum gates, unitary transformations and evolution, interpretations of quantum mechanics, the no-cloning theorem, quantum teleportation, quantum algorithms, density operators and mixed states, von Neumann entropy, the Bloch sphere, quantum decoherence, Hamiltonians, the Schrödinger equation, canonical and path integral quantization, quantum harmonic oscillators, wavefunctions, and much more. The lectures also contain 118 proof-based problems and 52 computational exercises, including several programming projects.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".