Quantum Computing. Melanie Swan

7.5.1SNARKs and multi-party computation

       7.5.2Bulletproofs and STARKs

       7.6State-of-the-Art: Zether for Account-Based Blockchains

       7.6.1Bulletproofs: Confidential transactions for UTXO chains

       7.6.2Zether: Confidential transactions for account chains

       7.6.3Confidential smart contract transactions

       7.6.4IPFS interactive proof-of-time and proof-of-space

       References

       Chapter 8Post-quantum Cryptography and Quantum Proofs

       8.1STARKs

       8.1.1Proof technology: The math behind STARKs

       8.1.2Probabilistically checkable proofs

       8.1.3PCPs of proximity and IOPs: Making PCPs more efficient

       8.1.4IOPs: Multi-round probabilistically checkable proofs

       8.1.5Holographic proofs and error-correcting codes

       8.2Holographic Codes

       8.2.1Holographic algorithms

       8.3Post-quantum Cryptography: Lattices and Hash Functions

       8.3.1Lattice-based cryptography

       8.3.2What is a lattice?

       8.3.3Lattice-based cryptography and zero-knowledge proofs

       8.3.4Lattice-based cryptography and blockchains

       8.3.5Hash function-based cryptography

       8.4Quantum Proofs

       8.4.1Non-interactive and interactive proofs

       8.4.2Conclusion on quantum proofs

       8.5Post-quantum Random Oracle Model

       8.6Quantum Cryptography Futures

       8.6.1Non-Euclidean lattice-based cryptosystems

       References

       Part 3 Machine Learning and Artificial Intelligence

       Chapter 9Classical Machine Learning

       9.1Machine Learning and Deep Learning Neural Networks

       9.1.1Why is deep learning called “deep”?

       9.1.2Why is deep learning called “learning”?

       9.1.3Big data is not smart data

       9.1.4Types of deep learning networks

       9.2Perceptron Processing Units

       9.2.1Jaw line or square of color is a relevant feature?

       9.3Technical Principles of Deep Learning Networks

       9.3.1Logistic regression: s-curve functions

       9.3.2Modular processing network node structure

       9.3.3Optimization: Backpropagation and gradient descent

       9.4Challenges and Advances

       9.4.1Generalized learning

       9.4.2Spin glass: Dark knowledge and adversarial networks

       9.4.3Software: Nonlinear dimensionality reduction

       9.4.4Software: Loss optimization and activation functions

       9.4.5Hardware: Network structure and autonomous networks

       9.5Deep Learning Applications

       9.5.1Object recognition (IDtech) (Deep learning 1.0)

       9.5.2Pattern recognition (Deep learning 2.0)

       9.5.3Forecasting, prediction, simulation (Deep learning 3.0)

       References

       Chapter 10Quantum Machine Learning

       10.1Machine Learning, Information Geometry, and Geometric Deep Learning

       10.1.1Machine learning as an n-dimensional computation graph

       10.1.2Information geometry: Geometry as a selectable parameter

       10.1.3Geometric deep learning

       10.2Standardized Methods