is 3D. This is part of a more general shift to 3D in both the computational domain and the end user domain. Technological interfaces are accommodating more 3D interactions with reality (which is itself 3D). Emblematic of 3D interfaces is that point cloud data is a new kind of internet traffic. Point cloud data captures 3D positioning information about entities (humans, robots, objects) in the context of their surroundings, with simultaneous localization and mapping (SLAM) technology. Another example of 3D-type interfaces is an advance in machine learning called geometric deep learning in which information can be analyzed in its native form rather than being compressed into lower-dimensional representations for processing. The point for quantum-readiness is that many smart network technologies such as blockchain and deep learning, as well as many other contemporary analytical systems, are already instantiated in computation graphs which are by definition 3D (and really n-dimensional), which could facilitate their potential transition to quantum information systems.
2.3Two Eras of Network Computing
The notion of smart networks is configured in the conceptualization of there being two fundamental eras of network computing (Figure 2.1). Most of the progress to date, from mainframes to mobile computing, has concerned the transfer of basic information on simple networks. Now, however, in a second phase of network computing, a new paradigm is being inaugurated, smart networks (Swan, 2015).
Figure 2.1. Eras of network computing: Simple networks and smart networks.
Table 2.1. Early smart networks (Smart Networks 1.0).
| Internet of trading | Internet of advertising |
| High-frequency trading | Real-time bidding |
| MarketTech | AdTech |
| Internet of energy | Internet of things |
| Smart grids (power) | Smart city |
| EnergyTech | SensorTech |
2.3.1 Smart Networks 1.0
There are different developmental phases of smart networks. An early class of smart networks (Smart Networks 1.0) can be identified (Table 2.1). Smart Networks 1.0 include HFT networks, the RTB market for advertising, smart energy grids with automated load-rebalancing, and smart city Internet of Things (IoT) sensor ecologies. The concept of smart network technologies emerged with programmatic or HFT systems for automated stock market trading. In 2016, HFT was estimated to comprise 10–40% of the total US trading volume in equities, and 10–15% of the trading volume in foreign exchange and commodities (Aldridge & Krawciw, 2017). Another early example of a smart network technology is the RTB market, an automated marketplace for online display advertising (web-based ads). Impressions are sold in a RTB model in which advertisers bid for impressions in real time, as consumers visit websites. Although RTB is highly efficient, two pricing models persist in this market, both RTB and ahead-of-time reservation contracts (Sayedi, 2018). In other early smart network technologies, smart energy grids conduct automated load-rebalancing, in which the emergence of complex behavior has been noted, in particular the synchronization of coupled oscillators (Dorfler et al., 2013). Smart city IoT sensor ecologies indicate the substantial smart network challenges that are faced in coordinating the 50 billion connected objects that are estimated to be deployed by 2020 (Hammi et al., 2017).
2.3.2 Smart Networks 2.0
The contemporary generation of smart network technologies is Smart Networks 2.0 (Table 2.2). Although blockchain distributed ledgers and deep learning systems are some of the most prominent examples of smart network technologies, there are many kinds of such intelligent self-operating networks. Other examples include automated supply chain and logistics networks (TradeTech), autonomous vehicle networks (TransportTech), industrial robotics cloudminds, the potential quantum internet, Web 3.0’s internet of data structures, and virtual reality video gaming. Smart networks operate at scales ranging from the very large, in space logistics platforms (Supply Chain 4.0) (Chen & Ho, 2018), to the very small, for control systems in brain–computer interfaces (Swan, 2016) and human brain–cloud interfaces (Martins et al., 2019).
Table 2.2. Robust self-operating smart networks (Smart Networks 2.0).
| Internet of value | Internet of analytics |
| Blockchains | Deep learning |
| EconTech, GovTech, PrivacyTech, ProofTech | IDTech |
| Internet of goods and services | Internet of vehicles |
| Automated supply chain | Autonomous driving networks |
| TradeTech | TransportTech |
| Internet of brains | Internet of qubits |
| Cloudminds | Quantum internet |
| Medical nanorobots (BCI) | |
| NeuralTech | QuantumTech |
| Internet of data structures | Internet of virtual reality |
| Web 3.0 | Gaming |
| DataTech (HashTech) | VirtualRealityTech |
2.3.2.1Blockchains
A blockchain is a distributed data structure which is cryptographically protected against modification, malicious or otherwise. Blockchains (technically, transaction blocks cryptographically linked together) are one topology among others in the more general class of distributed ledger technologies (Tasca & Tessone, 2019). Distributed ledger technology is EconTech and GovTech in the sense that institutional functions may be outsourced to computing networks (the administrative functions that orchestrate the patterns of human activity). Blockchains provide an alternative legal jurisdiction for the coordination of large groups of transnational actors using game theoretic incentives instead of policing, and economics as a design principle. Blockchains may be evolving into a new era that of PrivacyTech and ProofTech through zero-knowledge proof technology and verifiable computing.
2.3.2.2Machine learning: Deep learning neural networks
Machine learning is an artificial intelligence technology comprising algorithms that perform tasks by relying on information patterns and inference instead of explicit instructions.