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Contents

About the Authors

List of Figures

List of Tables

Chapter 1Introduction

1.1Quantum Futures?

1.2Technophysics

1.2.1Conceptual toolkit of ideas

1.2.2New slate of all-purpose smart technology features

1.3Chapter Highlights

References

Part 1 Smart Networks and Quantum Computing

Chapter 2Smart Networks: Classical and Quantum Field Theory

2.1Smart Networks

2.2Smart Network Theory

2.2.1Conventional (SNFT) and (SNQFT)

2.2.2Smart network technologies are quantum-ready

2.3Two Eras of Network Computing

2.3.1Smart Networks 1.0

2.3.2Smart Networks 2.0

2.3.3Smart Networks 3.0: Quantum smart networks

2.3.4Smart network convergence

2.4Smart Network Field Theory: Classical and Quantum

2.4.1Theory requirements: Characterize, monitor, and control

2.5Smart Network Field Theory Development

2.5.1The “field” in field theory

2.5.2Statistical physics

2.6Field Theory

2.6.1The field is the fundamental building block of reality

2.6.2Field theories: Fundamental or effective

2.6.3The smart network theories are effective field theories

2.6.4Complex multi-level systems

2.7Five Steps to Defining an Effective Field Theory

References

Chapter 3Quantum Computing: Basic Concepts

3.1Introduction

3.1.1Breaking RSA encryption

3.2Basic Concepts: Bit and Qubit

3.2.1Quantum computing and classical computing

3.2.2Bit and qubit

3.2.3Creating qubits

3.3Quantum Hardware Approaches

3.3.1The DiVincenzo criteria

3.3.2Superconducting circuits: Standard gate model

3.3.3Superconducting circuits: Quantum annealing machines

3.3.4Ion trapping

3.3.5Majorana fermions and topological quantum computing

3.3.6Quantum photonics

3.3.7Neutral atoms, diamond defects, quantum dots, and nuclear magnetic resonance

References

Chapter 4Advanced Quantum Computing: Interference and Entanglement

4.1Introduction

4.1.1Quantum statistics

4.2Interference

4.2.1Interference and amplitude

4.3Noisy Intermediate-Scale Quantum Devices

4.3.1Computability and computational complexity

4.4Quantum Error Correction

4.4.1Practical concerns and status

4.4.2Quantum state decoherence

4.4.3Entanglement property of qubits

4.4.4Quantum information processors

4.5Bell Inequalities and Quantum Computing

4.5.1Introduction to inequalities

4.5.2Bell inequalities

4.6Practical Applications of Entanglement: NIST Randomness Beacon

4.6.1Certifiably random bits

References

Part 2 Blockchain and Zero-Knowledge Proofs

Chapter 5Classical Blockchain

5.1Introduction: Functionality and Scalability Upgrades

5.2Computational Verification and Selectable Trust Models

5.3Layer 2 and the Lightning Network

5.3.1Introduction to the Lightning Network

5.3.2Basic routing on the Lightning Network

5.3.3Smart routing: Sphinx routing and rendez-vous routing

5.3.4A new layer in the Lightning Network: Channel factories

5.3.5Smart routing through atomic multi-path routing

5.4World Economic History on Replay

5.5Verifiable Markets, Marketplaces, Gaming, Stablecoins

5.5.1Verifiable markets

5.5.2Digital marketplaces

5.5.3Stablecoins

5.6Consensus

5.6.1Next-generation classical consensus

5.6.2Next-generation PBFT: Algorand and DFINITY

5.6.3Quantum Byzantine Agreement

References

Chapter 6Quantum Blockchain

6.1Quantum Blockchain

6.1.1Quantum-secure blockchains and quantum-based logic

6.1.2Proposal for quantum Bitcoin

6.1.3Quantum consensus: Grover’s algorithm, quantum annealing, light

6.1.4Quantum money

6.2Quantum Internet

6.2.1Quantum network theory

6.3Quantum Networks: A Deeper Dive

6.3.1The internet’s new infrastructure: Entanglement routing

6.3.2Quantum memory

6.4Quantum Cryptography and Quantum Key Distribution

6.4.1Quantum key distribution

6.4.2Satellite-based quantum key distribution: Global space race

6.4.3Key lifecycle management

6.5Quantum Security: Blockchain Risk of Quantum Attack

6.5.1Risk of quantum attack in authentication

6.5.2Risk of quantum attack in mining

6.6Quantum-Resistant Cryptography for Blockchains

References

Chapter 7Zero-Knowledge Proof Technology

7.1Zero-Knowledge Proofs: Basic Concept

7.2Zero-Knowledge Proofs and Public Key Infrastructure Cryptography

7.2.1Public key infrastructure

7.2.2Blockchain addresses

7.3Zero-Knowledge Proofs: Interactive Proofs

7.3.1Interactive proofs: Graph isomorphism example

7.4Zero-Knowledge Proofs in Blockchains

7.4.1Zero-knowledge proofs: Range proofs

7.4.2Unspent transaction outputs model

7.5State-of-the-Art: SNARKs, Bulletproofs, and STARKs

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 for Quantum Computing

10.2.1Standardized quantum computation tools

10.2.2Standardized quantum computation algorithms

10.2.3Quantum optimization

10.2.4Quantum simulation

10.2.5Examples of quantum machine learning

References

Part 4 Smart Network Field Theories

Chapter 11Model Field Theories: Neural Statistics and Spin Glass

11.1Summary of Statistical Neural Field Theory

11.2Neural Statistics: System Norm and Criticality

11.2.1Mean field theory describes stable equilibrium systems

11.2.2Statistical neural field theory describes system criticality

11.3Detailed Description of Statistical Neural Field Theory

11.3.1Master field equation for the neural system

11.3.2Markov random walk redefined as Markov random field

11.3.3Linear and nonlinear models of the system action

11.3.4System criticality

11.3.5Optimal control theory

11.4Summary of the Spin-Glass Model

11.5Spin-Glass Model: System Norm and Criticality

11.6Detailed Description of the Spin-Glass Model

11.6.1Spin glasses

11.6.2Advanced model: p-Spherical spin glass

11.6.3Applications of the spin-glass model: Loss optimization

References

Chapter 12Smart Network Field Theory Specification and Examples

12.1Motivation for Smart Network Field Theory

12.2Minimal Elements of Smart Network Field Theory

12.3Smart Network System Definition

12.4Smart Network System Operation

12.4.1Temperature term

12.4.2Hamiltonian term

12.4.3Scale-spanning portability

12.5Smart Network System Criticality

12.5.1Particles (nodes)

12.5.2Node states

12.5.3Node action

12.5.4State transitions

12.6Applications of Smart Network Field Theories

12.6.1Smart network service provisioning application layers

12.6.2Basic administrative services

12.6.3Value-added services

12.6.4Smart network metrics

References

Part 5 The AdS/CFT Correspondence and Holographic Codes

Chapter 13The AdS/CFT Correspondence

13.1History and Summary of the AdS/CFT Correspondence

13.2The AdS/CFT Correspondence: Basic Concepts

13.2.1The holographic principle

13.2.2Holographic principle formalized in the AdS/CFT correspondence

13.2.3Quantum error-correction code interpretation

13.3The AdS/CFT Correspondence is Information-Theoretic

13.3.1Black hole information paradox

13.3.2The information-theoretic view

13.4The AdS/CFT Correspondence as Quantum Error Correction

13.4.1The AdS/CFT correspondence: Emergent bulk locality

13.4.2Quantum error correction with the correspondence

13.4.3Emergent bulk structure through error correction

13.4.4Extending AdS–Rindler with quantum secret-sharing

13.5Holographic Methods: The AdS/CFT Correspondence

13.5.1The correspondence as a complexity technology

13.5.2Strongly coupled systems: AdS/CMT correspondence

13.5.3Strongly coupled plasmas

References

Chapter 14Holographic Quantum Error-Correcting Codes

14.1Holographic Quantum Error-Correcting Codes

14.1.1Quantum error correction

14.1.2Tensor networks and MERA tensor networks

14.1.3AdS/CFT holographic quantum error-correcting codes

14.2Other Holographic Quantum Error-Correcting Codes

14.2.1.Emergent bulk geometry from boundary entanglement

14.2.2Ryu–Takayanagi quantum error correction codes

14.2.3Extending MERA tensor network models

14.2.4Bosonic error-correction codes

14.3Quantum Coding Theory

14.4Technophysics: AdS/Deep Learning Correspondence

14.4.1Novel uses of quantum error-correction architecture

References

Part 6 Quantum Smart Networks

Chapter 15AdS/Smart Network Correspondence and Conclusion

15.1Smart Network Quantum Field Theory

15.1.1AdS/CFT correspondence-motivated SNQFT

15.1.2Minimal elements of smart network quantum field theory

15.1.3Nature’s quantum security features

15.1.4Random tensors: A graph is a field

15.2The AdS/CFT Correspondence Generalized to the SNQFT

15.2.1Bidirectional: Bulk–boundary linkage

15.2.2Unidirectional: Interrogate complexity with simplicity

15.3Adding Dynamics to the AdS/CFT Correspondence

15.3.1Spin glass interpretation of the AdS/CFT correspondence

15.3.2Holographic geometry is free

15.4Quantum Information/SNQFT Correspondence

15.4.1Strategy: Solve any theory as a field theory in one fewer dimensions

15.4.2Macroscale reality is the boundary to the quantum mechanical bulk

15.5The SNFT is the Boundary CFT to the Bulk Quantum Information Domain

15.5.1The internet as a quantum computer

15.5.2Computing particle-many systems with the quantum internet

15.6Risks and Limitations

15.7Conclusion

15.7.1From probability to correspondence

15.7.2Farther consequences: Quantum computing eras

References

Glossary

Index