Add subtle donations footer to README and all chapter docs

README.md

@@ -187,3 +187,7 @@ This work is licensed under a Creative Commons Attribution‑NonCommercial‑Sha
 - **API Access:** quantumapi@dascient.com
 - **Textbook & Academic Inquiries:** press@dascient.com
 - **LinkedIn:** https://linkedin.com/company/DaScient
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docs/chapters/ch01.md

@@ -342,3 +342,7 @@ sampled, noisy evidence returned by physical devices.
 - C. H. Bennett and D. P. DiVincenzo, "Quantum information and computation,"
   *Nature* **404**, 247 (2000).
 - M. M. Wilde, *Quantum Information Theory*.
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docs/chapters/ch02.md

@@ -298,3 +298,7 @@ sampled, noisy, and operationally constrained.
 - C. M. Bishop, *Pattern Recognition and Machine Learning*.
 - T. S. Cohen and M. Welling, "Group equivariant convolutional networks,"
   *Proceedings of ICML* (2016).
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docs/chapters/ch03.md

@@ -356,3 +356,7 @@ separating physical conclusions from computational artefacts.
 - J. Nocedal and S. Wright, *Numerical Optimization*.
 - R. Bhatia, *Matrix Analysis*.
 - M. Schuld and F. Petruccione, *Machine Learning with Quantum Computers*.
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docs/chapters/ch04.md

@@ -221,3 +221,7 @@ Tomography turns measurements into matrices and exposes the exponential wall tha
 - D. Gross et al., "Quantum state tomography via compressed sensing," *Phys. Rev. Lett.* **105**, 150401 (2010).
 - H.-Y. Huang, R. Kueng, and J. Preskill, "Predicting many properties of a quantum system from very few measurements," *Nat. Phys.* **16**, 1050 (2020).
 - I. L. Chuang and M. A. Nielsen, "Prescription for experimental determination of the dynamics of a quantum black box," *J. Mod. Opt.* **44**, 2455 (1997).
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docs/chapters/ch05.md

@@ -202,3 +202,7 @@ Data are the substrate of quantum AI. Measurement records, shot tables, calibrat
 - T. Gebru et al., "Datasheets for datasets," *Commun. ACM* **64**, 86 (2021).
 - N. Killoran et al., "Strawberry Fields" and the broader open-data movement in quantum software.
 - J. Biamonte et al., "Quantum machine learning," *Nature* **549**, 195 (2017).
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docs/chapters/ch06.md

@@ -202,3 +202,7 @@ Noise is the defining constraint of present-day quantum hardware. Open-system ev
 - E. Magesan, J. M. Gambetta, and J. Emerson, "Scalable and robust randomized benchmarking," *Phys. Rev. Lett.* **106**, 180504 (2011).
 - J. J. Wallman and J. Emerson, "Noise tailoring for scalable quantum computation via randomized compiling," *Phys. Rev. A* **94**, 052325 (2016).
 - M. A. Nielsen and I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
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docs/chapters/ch07.md

@@ -245,3 +245,7 @@ Optimal control turns gate design into constrained optimisation over time-depend
 - M. Y. Niu et al., "Universal quantum control through deep reinforcement learning," *npj Quantum Inf.* **5**, 33 (2019).
 - D. J. Tannor, *Introduction to Quantum Mechanics: A Time-Dependent Perspective*.
 - F. Motzoi et al., "Simple pulses for elimination of leakage in weakly nonlinear qubits," *Phys. Rev. Lett.* **103**, 110501 (2009).
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docs/chapters/ch08.md

@@ -189,3 +189,7 @@ Error correction protects quantum information by encoding it into a stabiliser c
 - A. G. Fowler et al., "Surface codes: towards practical large-scale quantum computation," *Phys. Rev. A* **86**, 032324 (2012).
 - E. Dennis et al., "Topological quantum memory," *J. Math. Phys.* **43**, 4452 (2002).
 - K. Temme, S. Bravyi, and J. M. Gambetta, "Error mitigation for short-depth quantum circuits," *Phys. Rev. Lett.* **119**, 180509 (2017).
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docs/chapters/ch09.md

@@ -225,3 +225,7 @@ Metrology quantifies how quantum resources improve parameter estimation. The SQL
 - C. W. Helstrom, *Quantum Detection and Estimation Theory*.
 - M. G. A. Paris, "Quantum estimation for quantum technology," *Int. J. Quantum Inf.* **7**, 125 (2009).
 - L. Pezzè et al., "Quantum metrology with nonclassical states of atomic ensembles," *Rev. Mod. Phys.* **90**, 035005 (2018).
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docs/chapters/ch10.md

@@ -150,3 +150,7 @@ The structure-to-property map is smooth and learnable when molecular symmetries
 - A. Peruzzo et al., "A variational eigenvalue solver on a photonic quantum processor," *Nat. Commun.* **5**, 4213 (2014).
 - J. McClean et al., "The theory of variational hybrid quantum-classical algorithms," *New J. Phys.* **18**, 023023 (2016).
 - K. T. Schütt et al., "SchNet: A continuous-filter convolutional neural network for modeling quantum interactions," *NeurIPS* (2017).
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docs/chapters/ch11.md

@@ -130,3 +130,7 @@ Materials discovery couples forward prediction with inverse design. Physics-base
 - T. Xie et al., "Crystal diffusion variational autoencoder for periodic material generation," *ICLR* (2022).
 - A. Jain et al., "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation," *APL Materials* **1**, 011002 (2013).
 - K. T. Butler et al., "Machine learning for molecular and materials science," *Nature* **559**, 547 (2018).
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docs/chapters/ch12.md

@@ -164,3 +164,7 @@ The area law is the structural fact that rescues large parts of the many-body pr
 - G. Carleo and M. Troyer, "Solving the quantum many-body problem with artificial neural networks," *Science* **355**, 602 (2017).
 - R. Orús, "A practical introduction to tensor networks: Matrix product states and projected entangled pair states," *Ann. Phys.* **349**, 117 (2014).
 - J. Biamonte and V. Bergholm, "Tensor networks in a nutshell," arXiv:1708.00006 (2017).
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docs/chapters/ch13.md

@@ -127,3 +127,7 @@ QKD turns physical law into a secrecy guarantee by converting disturbance into o
 - P. W. Shor and J. Preskill, "Simple proof of security of the BB84 quantum key distribution protocol," *Phys. Rev. Lett.* **85**, 441 (2000).
 - V. Scarani et al., "The security of practical quantum key distribution," *Rev. Mod. Phys.* **81**, 1301 (2009).
 - H.-K. Lo, M. Curty, and K. Tamaki, "Secure quantum key distribution," *Nature Photonics* **8**, 595 (2014).
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docs/chapters/ch14.md

@@ -147,3 +147,7 @@ Quantum networks distribute entanglement rather than classical packets. Direct o
 - S. Wehner, D. Elkouss, and R. Hanson, "Quantum internet: A vision for the road ahead," *Science* **362**, eaam9288 (2018).
 - N. Sangouard et al., "Quantum repeaters based on atomic ensembles and linear optics," *Rev. Mod. Phys.* **83**, 33 (2011).
 - W. J. Munro, K. Azuma, K. Tamaki, and K. Nemoto, "Inside quantum repeaters," *IEEE Journal of Selected Topics in Quantum Electronics* **21**, 78 (2015).
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docs/chapters/ch15.md

@@ -164,3 +164,7 @@ Quantum algorithms obtain speedups by exploiting structure: periodicity in Shor'
 - L. K. Grover, "A fast quantum mechanical algorithm for database search," *STOC* (1996).
 - M. A. Nielsen and I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press (2010).
 - A. W. Harrow and A. Montanaro, "Quantum computational supremacy," *Nature* **549**, 203 (2017).
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docs/chapters/ch16.md

@@ -193,3 +193,7 @@ Quantum kernels recast QML as kernel learning with quantum-computed similarities
   spaces," *Phys. Rev. Lett.* **122**, 040504 (2019).
 - E. Tang, "A quantum-inspired classical algorithm for recommendation systems,"
   *STOC* (2019).
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docs/chapters/ch17.md

@@ -264,3 +264,7 @@ VQAs pair a parameterised circuit with a classical optimiser, the dominant NISQ
 - J. R. McClean et al., "Barren plateaus in quantum neural network training
   landscapes," *Nat. Commun.* **9**, 4812 (2018).
 - E. Farhi, J. Goldstone, and S. Gutmann, "A quantum approximate optimization algorithm," arXiv:1411.4028 (2014).
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docs/chapters/ch18.md

@@ -197,3 +197,7 @@ Quantum processors are accelerators within classical systems. The interface and
 - N. Killoran et al., "Continuous-variable quantum neural networks,"
   *Phys. Rev. Research* **1**, 033063 (2019).
 - A. Peruzzo et al., "A variational eigenvalue solver on a photonic quantum processor," *Nat. Commun.* **5**, 4213 (2014).
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docs/chapters/ch19.md

@@ -306,3 +306,7 @@ into operational duties.
 - National Academies, *Quantum Computing: Progress and Prospects* (2019).
 - NIST, *Post-Quantum Cryptography* project documentation.
 - OECD, *Recommendation of the Council on Artificial Intelligence*.
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docs/chapters/ch20.md

@@ -283,3 +283,7 @@ shared.
 - IEEE Quantum standards and terminology resources.
 - ISO/IEC information-technology standards catalogues relevant to security and
   interoperability.
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docs/chapters/ch21.md

@@ -308,3 +308,7 @@ it.
 - National Academies, *Quantum Computing: Progress and Prospects* (2019).
 - OECD, *Recommendation on Responsible Innovation in Neurotechnology* and related
   responsible-innovation resources as comparative governance models.
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docs/chapters/ch22.md

@@ -143,3 +143,7 @@ AI and quantum hardware advance each other in a tightening loop, from learned ca
 - G. Carleo et al., "Machine learning and the physical sciences," *Rev. Mod. Phys.* **91**, 045002 (2019).
 - A. G. Fowler, M. Mariantoni, J. M. Martinis, and A. N. Cleland, "Surface codes: Towards practical large-scale quantum computation," *Phys. Rev. A* **86**, 032324 (2012).
 - M. Cerezo et al., "Variational quantum algorithms," *Nature Reviews Physics* **3**, 625–644 (2021).
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docs/chapters/ch23.md

@@ -143,3 +143,7 @@ Quantum AI's open questions span the reality of advantage, the scalability of va
 - M. Cerezo et al., "Cost function dependent barren plateaus in shallow parametrized quantum circuits," *Nature Communications* **12**, 1791 (2021).
 - J. Preskill, "Quantum computing 40 years later," *Feynman Lectures on Computation* anniversary lecture notes and related essays.
 - H.-Y. Huang, R. Kueng, and J. Preskill, "Information-theoretic bounds on quantum advantage in machine learning," *Phys. Rev. Lett.* **126**, 190505 (2021).
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docs/chapters/ch24.md

@@ -164,3 +164,7 @@ Quantum-native intelligence extends the book's central pattern — learned, stru
 - M. A. Nielsen and I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press (2010).
 - J. Preskill, "Quantum computing in the NISQ era and beyond," *Quantum* **2**, 79 (2018).
 - S. Aaronson, "The limits of quantum computers," *Sci. Am.* **298**, 62 (2008).
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