Quantum complex network

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Quantum_complex_network 

 

Network science
Theory
Graph Complex network Contagion Small-world Scale-free Community structure Percolation Evolution Controllability Graph drawing Social capital Link analysis Optimization Reciprocity Closure Homophily Transitivity Preferential attachment Balance theory Network effect Social influence
Network types
Informational (computing) Telecommunication Transport Social Scientific collaboration Biological Artificial neural Interdependent Semantic Spatial Dependency Flow on-Chip
Graphs
Features Clique Component Cut Cycle Data structure Edge Loop Neighborhood Path Vertex Adjacency list / matrix Incidence list / matrix Types Bipartite Complete Directed Hyper Multi Random Weighted
Metrics Algorithms
Centrality Degree Motif Clustering Degree distribution Assortativity Distance Modularity Efficiency
Models
Topology Random graph Erdős–Rényi Barabási–Albert Bianconi–Barabási Fitness model Watts–Strogatz Exponential random (ERGM) Random geometric (RGG) Hyperbolic (HGN) Hierarchical Stochastic block Blockmodeling Maximum entropy Soft configuration LFR Benchmark Dynamics Boolean network agent based Epidemic/SIR
Lists Categories
Topics Software Network scientists Category:Network theory Category:Graph theory

As part of network science, the study of quantum complex networks aims to explore the impact of complexity science and network architectures in quantum systems. According to quantum information theory, it is possible to improve communication security and data transfer rates by taking advantage of quantum mechanics. In this context, the study of quantum complex networks is motivated by the possibility of quantum communications being used on an enterprise scale in the future. In this case, it is likely that quantum communication networks will acquire nontrivial features, as is common in existing communication networks today.

Motivation

In theory, it is possible to take advantage of quantum mechanics to create secure and faster communications. For example, quantum key distribution is an application of quantum cryptography that enables secure communications, and quantum teleportation can be used to transfer data at a higher rate than one can using only classical channels.

Successful quantum teleportation experiments in 1998, followed by the development of the first quantum communication networks in 2004, open the possibility of quantum communication being used on a large scale in the future. According to findings in network science, network topology is extremely important in many cases, and existing large scale communication networks tend to have non-trivial topologies and characteristics, such as small world effect, community structure, or being a scale-free network. The study of networks with quantum properties and complex network topologies can help us to not only better understand such networks but also use the network topology to improve the efficiency of communication networks in the future.

Important concepts

Qubits

In quantum information theory, qubits are analogous to bits in classical systems. A qubit is a quantum object that, when measured, can be found to be in one of only two states, and that is used to transmit information. Photon polarization or nuclear spin are examples of two-state systems that can be used as qubits.

Entanglement

Quantum entanglement is a physical phenomenon characterized by correlation between the quantum states of two or more particles. While entangled particles do not interact in the classical sense, the quantum state of such particles cannot be described independently due to their entanglement. Particles can be entangled in many ways. The maximally entangled states are the ones that maximize the entropy of entanglement. In the context of quantum communication, entangled qubits are used as a quantum channel, which is capable of transmitting information when combined with a classical channel.

Bell measurement

Bell measurement is a kind of joint quantum-mechanical measurement of two qubits such that, after the measurement, the two qubits will be maximally entangled.

Entanglement swapping

Entanglement swapping is a frequently used strategy used in the study of quantum networks that allows connections in the network to change. For example, suppose that we have 4 qubits, A, B, C and D, such that qubits C and D belong to the same station, while A and C belong to two different stations, and where qubit A is entangled with qubit C and qubit B is entangled with qubit D. By performing a Bell measurement for qubits A and B, not only will qubits A and B be entangled, but it is also possible to entangle qubits C and D, despite the fact that these two qubits never interacted directly with each other. Following this process, the entanglement between qubits A and C, and qubits B and D will be lost. This strategy can be used to shape connections on the network.

Network structure

While not all models for quantum complex networks follow exactly the same structure, usually, in such models, a node represents a set of qubits in the same station (where operations like Bell measurements and entanglement swapping can be applied) and an edge between node ${\displaystyle i}$ and ${\displaystyle j}$ means that a qubit in node ${\displaystyle i}$ is entangled to a qubit in node ${\displaystyle j}$, although those two qubits are in different places and so cannot physically interact. Quantum networks where the links are interaction terms instead of entanglement may also be considered but for very different purposes.

Notation

Each node in the network contains a set of qubits that can be in different states. To represent the quantum state of these qubits, it is convenient to use Dirac notation and represent the two possible states of each qubit as ${\displaystyle |0\rangle }$ and ${\displaystyle |1\rangle }$. In this notation, two particles are entangled if the joint wave function, ${\displaystyle |\psi _{ij}\rangle }$, cannot be decomposed as

${\displaystyle |\psi _{ij}\rangle =|\phi \rangle _{i}\otimes |\phi \rangle _{j},}$

where ${\displaystyle |\phi \rangle _{i}}$ represents the quantum state of the qubit at node i and ${\displaystyle |\phi \rangle _{j}}$ represents the quantum state of the qubit at node j.

Another important concept is maximally entangled states. The four states (the Bell states) that maximize the entropy of entanglement between two qubits can be written as follows:

${\displaystyle |\Phi _{ij}^{+}\rangle ={\frac {1}{\sqrt {2}}}(|0\rangle _{i}\otimes |0\rangle _{j}+|1\rangle _{i}\otimes |1\rangle _{j}),}$

${\displaystyle |\Phi _{ij}^{-}\rangle ={\frac {1}{\sqrt {2}}}(|0\rangle _{i}\otimes |0\rangle _{j}-|1\rangle _{i}\otimes |1\rangle _{j}),}$

${\displaystyle |\Psi _{ij}^{+}\rangle ={\frac {1}{\sqrt {2}}}(|0\rangle _{i}\otimes |1\rangle _{j}+|1\rangle _{i}\otimes |0\rangle _{j}),}$

${\displaystyle |\Psi _{ij}^{-}\rangle ={\frac {1}{\sqrt {2}}}(|0\rangle _{i}\otimes |1\rangle _{j}-|1\rangle _{i}\otimes |0\rangle _{j}).}$

Models

Quantum random networks

The quantum random network model proposed by Perseguers et al. (2009) can be thought of as a quantum version of the Erdős–Rényi model. In this model, each node contains ${\displaystyle N-1}$ qubits, one for each other node. The degree of entanglement between a pair of nodes, represented by ${\displaystyle p}$, plays a similar role to the parameter ${\displaystyle p}$ in the Erdős–Rényi model: in the Erdős–Rényi model, two nodes form a connection with probability ${\displaystyle p}$, whereas in the context of quantum random networks, ${\displaystyle p}$ refers to the probability of an entangled pair of qubits being successfully converted to a maximally entangled state using only local operations and classical communication.

Using the Dirac notation introduced previously, we can represent a pair of entangled qubits connecting the nodes ${\displaystyle i}$ and ${\displaystyle j}$ as

${\displaystyle |\psi _{ij}\rangle ={\sqrt {1-p/2}}|0\rangle _{i}\otimes |0\rangle _{j}+{\sqrt {p/2}}|1\rangle _{i}\otimes |1\rangle _{j},}$

For ${\displaystyle p=0}$, the two qubits are not entangled:

${\displaystyle |\psi _{ij}\rangle =|0\rangle _{i}\otimes |0\rangle _{j},}$

and for ${\displaystyle p=1}$, we obtain the maximally entangled state:

${\displaystyle |\psi _{ij}\rangle ={\sqrt {1/2}}(|0\rangle _{i}\otimes |0\rangle _{j}+|1\rangle _{i}\otimes |1\rangle _{j})}$.

For intermediate values of ${\displaystyle p}$, ${\displaystyle 0<p<1}$, any entangled state is, with probability ${\displaystyle p}$, successfully converted to the maximally entangled state using LOCC operations.

One feature that distinguishes this model from its classical analogue is the fact that, in quantum random networks, links are only truly established after measurements in the networks being made, and it is possible to take advantage of this fact to shape the final state of the network. For an initial quantum complex network with an infinite number of nodes, Perseguers et al. showed that, by doing the right measurements and entanglement swapping, it is possible to collapse the initial network to a network containing any finite subgraph, provided that ${\displaystyle p}$ scales with ${\displaystyle N}$ as ${\textstyle p\sim N^{Z}}$, where ${\displaystyle Z\geq -2}$. This result is contrary to what we find in classical graph theory, where the type of subgraphs contained in a network is bounded by the value of ${\displaystyle z}$.

Entanglement percolation

The goal of entanglement percolation models is to determine if a quantum network is capable of establishing a connection between two arbitrary nodes through entanglement, and to find the best strategies to create such connections.

In a model proposed by Cirac et al. (2007) and applied to complex networks by Cuquet et al. (2009), nodes are distributed in a lattice or in a complex network, and each pair of neighbors share two pairs of entangled qubits that can be converted to a maximally entangled qubit pair with probability ${\displaystyle p}$. We can think of maximally entangled qubits as the true links between nodes. In classical percolation theory, with a probability ${\displaystyle p}$ of two nodes being connected, there is a critical value of ${\displaystyle p}$ (denoted by ${\displaystyle p_{c}}$), so that if ${\displaystyle p>p_{c}}$ there is a finite probability of a path between two randomly selected nodes existing, and for ${\displaystyle p<p_{c}}$ the probability of such a path existing is asymptotically zero. ${\displaystyle p_{c}}$ depends only on the topology of the network.

A similar phenomenon was found in the model proposed by Cirac et al. (2007), where the probability of forming a maximally entangled state between two randomly selected nodes is zero if ${\displaystyle p<p_{c}}$ and finite if ${\displaystyle p>p_{c}}$. The main difference between classical and entangled percolation is that, in quantum networks, it is possible to change the links in the network, in a way changing the effective topology of the network. As a result, ${\displaystyle p_{c}}$ depends on the strategy one uses to convert partially entangled qubits to maximally connected qubits. With a naïve approach, ${\displaystyle p_{c}}$ for a quantum network is equal to ${\displaystyle p_{c}}$ for a classic network with the same topology. Nevertheless, it was shown that is possible to take advantage of quantum swapping to lower ${\displaystyle p_{c}}$ both in regular lattices and complex networks.

Mathematical modelling of infectious disease

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Mathematical_modelling_of_infectious_disease 

Mathematical models can project how infectious diseases progress to show the likely outcome of an epidemic (including in plants) and help inform public health and plant health interventions. Models use basic assumptions or collected statistics along with mathematics to find parameters for various infectious diseases and use those parameters to calculate the effects of different interventions, like mass vaccination programs. The modelling can help decide which intervention(s) to avoid and which to trial, or can predict future growth patterns, etc.

History

The modeling of infectious diseases is a tool that has been used to study the mechanisms by which diseases spread, to predict the future course of an outbreak and to evaluate strategies to control an epidemic.

The first scientist who systematically tried to quantify causes of death was John Graunt in his book Natural and Political Observations made upon the Bills of Mortality, in 1662. The bills he studied were listings of numbers and causes of deaths published weekly. Graunt's analysis of causes of death is considered the beginning of the "theory of competing risks" which according to Daley and Gani  is "a theory that is now well established among modern epidemiologists".

The earliest account of mathematical modelling of spread of disease was carried out in 1760 by Daniel Bernoulli. Trained as a physician, Bernoulli created a mathematical model to defend the practice of inoculating against smallpox. The calculations from this model showed that universal inoculation against smallpox would increase the life expectancy from 26 years 7 months to 29 years 9 months. Daniel Bernoulli's work preceded the modern understanding of germ theory.

In the early 20th century, William Hamer and Ronald Ross applied the law of mass action to explain epidemic behaviour.

The 1920s saw the emergence of compartmental models. The Kermack–McKendrick epidemic model (1927) and the Reed–Frost epidemic model (1928) both describe the relationship between susceptible, infected and immune individuals in a population. The Kermack–McKendrick epidemic model was successful in predicting the behavior of outbreaks very similar to that observed in many recorded epidemics.

Recently, agent-based models (ABMs) have been used in exchange for simpler compartmental models. For example, epidemiological ABMs have been used to inform public health (nonpharmaceutical) interventions against the spread of SARS-CoV-2. Epidemiological ABMs, in spite of their complexity and requiring high computational power, have been criticized for simplifying and unrealistic assumptions. Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated.

Assumptions

Models are only as good as the assumptions on which they are based. If a model makes predictions that are out of line with observed results and the mathematics is correct, the initial assumptions must change to make the model useful.

• Rectangular and stationary age distribution, i.e., everybody in the population lives to age L and then dies, and for each age (up to L) there is the same number of people in the population. This is often well-justified for developed countries where there is a low infant mortality and much of the population lives to the life expectancy.
• Homogeneous mixing of the population, i.e., individuals of the population under scrutiny assort and make contact at random and do not mix mostly in a smaller subgroup. This assumption is rarely justified because social structure is widespread. For example, most people in London only make contact with other Londoners. Further, within London then there are smaller subgroups, such as the Turkish community or teenagers (just to give two examples), who mix with each other more than people outside their group. However, homogeneous mixing is a standard assumption to make the mathematics tractable.

Types of epidemic models

Stochastic

"Stochastic" means being or having a random variable. A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. Stochastic models depend on the chance variations in risk of exposure, disease and other illness dynamics. Statistical agent-level disease dissemination in small or large populations can be determined by stochastic methods.

Deterministic

When dealing with large populations, as in the case of tuberculosis, deterministic or compartmental mathematical models are often used. In a deterministic model, individuals in the population are assigned to different subgroups or compartments, each representing a specific stage of the epidemic.

The transition rates from one class to another are mathematically expressed as derivatives, hence the model is formulated using differential equations. While building such models, it must be assumed that the population size in a compartment is differentiable with respect to time and that the epidemic process is deterministic. In other words, the changes in population of a compartment can be calculated using only the history that was used to develop the model.

Sub-exponential growth

A common explanation for the growth of epidemics holds that 1 person infects 2, those 2 infect 4 and so on and so on with the number of infected doubling every generation. It is analogous to a game of tag where 1 person tags 2, those 2 tag 4 others who've never been tagged and so on. As this game progresses it becomes increasing frenetic as the tagged run past the previously tagged to hunt down those who have never been tagged. Thus this model of an epidemic leads to a curve that grows exponentially until it crashes to zero as all the population have been infected. i.e. no herd immunity and no peak and gradual decline as seen in reality.

A better analogy is a game of roulette. Consider a wheel made of only red and black pockets, spin the wheel and roll a ball. The rules of the game are that if a ball lands in a red or black pocket then the pocket turns green and, on the next spin, an extra ball is rolled. If, instead, a ball lands in a green pocket then it is removed from play. The game is played until there are no balls left to roll at which point there might have been exponential growth observed but it is highly improbable. Taking the average of a number of games and it is expected that the initial growth is sub-exponential and that, at the end of the game, not all of the pockets will be green which corresponds to herd immunity.

Reproduction number

Main article: Basic reproduction number

The basic reproduction number (denoted by R₀) is a measure of how transferable a disease is. It is the average number of people that a single infectious person will infect over the course of their infection. This quantity determines whether the infection will increase sub-exponentially, die out, or remain constant: if R₀ > 1, then each person on average infects more than one other person so the disease will spread; if R₀ < 1, then each person infects fewer than one person on average so the disease will die out; and if R₀ = 1, then each person will infect on average exactly one other person, so the disease will become endemic: it will move throughout the population but not increase or decrease.

Endemic steady state

An infectious disease is said to be endemic when it can be sustained in a population without the need for external inputs. This means that, on average, each infected person is infecting exactly one other person (any more and the number of people infected will grow sub-exponentially and there will be an epidemic, any less and the disease will die out). In mathematical terms, that is:

${\displaystyle \ R_{0}S\ =1.}$

The basic reproduction number (R₀) of the disease, assuming everyone is susceptible, multiplied by the proportion of the population that is actually susceptible (S) must be one (since those who are not susceptible do not feature in our calculations as they cannot contract the disease). Notice that this relation means that for a disease to be in the endemic steady state, the higher the basic reproduction number, the lower the proportion of the population susceptible must be, and vice versa. This expression has limitations concerning the susceptibility proportion, e.g. the R₀ equals 0.5 implicates S has to be 2, however this proportion exceeds the population size.

Assume the rectangular stationary age distribution and let also the ages of infection have the same distribution for each birth year. Let the average age of infection be A, for instance when individuals younger than A are susceptible and those older than A are immune (or infectious). Then it can be shown by an easy argument that the proportion of the population that is susceptible is given by:

${\displaystyle S={\frac {A}{L}}.}$

We reiterate that L is the age at which in this model every individual is assumed to die. But the mathematical definition of the endemic steady state can be rearranged to give:

${\displaystyle S={\frac {1}{R_{0}}}.}$

Therefore, due to the transitive property:

${\displaystyle {\frac {1}{R_{0}}}={\frac {A}{L}}\Rightarrow R_{0}={\frac {L}{A}}.}$

This provides a simple way to estimate the parameter R₀ using easily available data.

For a population with an exponential age distribution,

${\displaystyle R_{0}=1+{\frac {L}{A}}.}$

This allows for the basic reproduction number of a disease given A and L in either type of population distribution.

Compartmental models in epidemiology

Main article: Compartmental models in epidemiology

Compartmental models are formulated as Markov chains. A classic compartmental model in epidemiology is the SIR model, which may be used as a simple model for modeling epidemics. Multiple other types of compartmental models are also employed.

The SIR model

Diagram of the SIR model with initial values ${\textstyle S(0)=997,I(0)=3,R(0)=0}$, and rates for infection ${\textstyle \beta =0.4}$ and for recovery ${\textstyle \gamma =0.04}$

Animation of the SIR model with initial values ${\textstyle S(0)=997,I(0)=3,R(0)=0}$, and rate of recovery ${\textstyle \gamma =0.04}$. The animation shows the effect of reducing the rate of infection from ${\textstyle \beta =0.5}$ to ${\textstyle \beta =0.12}$. If there is no medicine or vaccination available, it is only possible to reduce the infection rate (often referred to as "flattening the curve") by appropriate measures such as social distancing.

In 1927, W. O. Kermack and A. G. McKendrick created a model in which they considered a fixed population with only three compartments: susceptible, ${\displaystyle S(t)}$; infected, ${\displaystyle I(t)}$; and recovered, ${\displaystyle R(t)}$. The compartments used for this model consist of three classes:

• ${\displaystyle S(t)}$ is used to represent the individuals not yet infected with the disease at time t, or those susceptible to the disease of the population.
• ${\displaystyle I(t)}$ denotes the individuals of the population who have been infected with the disease and are capable of spreading the disease to those in the susceptible category.
• ${\displaystyle R(t)}$ is the compartment used for the individuals of the population who have been infected and then removed from the disease, either due to immunization or due to death. Those in this category are not able to be infected again or to transmit the infection to others.

Other compartmental models

There are many modifications of the SIR model, including those that include births and deaths, where upon recovery there is no immunity (SIS model), where immunity lasts only for a short period of time (SIRS), where there is a latent period of the disease where the person is not infectious (SEIS and SEIR), and where infants can be born with immunity (MSIR).

Infectious disease dynamics

Mathematical models need to integrate the increasing volume of data being generated on host-pathogen interactions. Many theoretical studies of the population dynamics, structure and evolution of infectious diseases of plants and animals, including humans, are concerned with this problem. Research topics include:

• antigenic shift
• epidemiological networks
• evolution and spread of resistance
• immuno-epidemiology
• intra-host dynamics
• Pandemic
• pathogen population genetics
• persistence of pathogens within hosts
• phylodynamics
• role and identification of infection reservoirs
• role of host genetic factors
• spatial epidemiology
• statistical and mathematical tools and innovations
• Strain (biology) structure and interactions
• transmission, spread and control of infection
• virulence

Mathematics of mass vaccination

If the proportion of the population that is immune exceeds the herd immunity level for the disease, then the disease can no longer persist in the population and its transmission dies out. Thus, a disease can be eliminated from a population if enough individuals are immune due to either vaccination or recovery from prior exposure to disease. For example, smallpox eradication, with the last wild case in 1977, and certification of the eradication of indigenous transmission of 2 of the 3 types of wild poliovirus (type 2 in 2015, after the last reported case in 1999, and type 3 in 2019, after the last reported case in 2012).

The herd immunity level will be denoted q. Recall that, for a stable state:

${\displaystyle R_{0}\cdot S=1.}$

In turn,

${\displaystyle R_{0}={\frac {N}{S}}={\frac {\mu N\operatorname {E} (T_{L})}{\mu N\operatorname {E} [\min(T_{L},T_{S})]}}={\frac {\operatorname {E} (T_{L})}{\operatorname {E} [\min(T_{L},T_{S})]}},}$

which is approximately:

${\displaystyle {\frac {\operatorname {\operatorname {E} } (T_{L})}{\operatorname {\operatorname {E} } (T_{S})}}=1+{\frac {\lambda }{\mu }}={\frac {\beta N}{v}}.}$

Graph of herd immunity threshold vs basic reproduction number with selected diseases

S will be (1 − q), since q is the proportion of the population that is immune and q + S must equal one (since in this simplified model, everyone is either susceptible or immune). Then:

${\displaystyle {\begin{aligned}&R_{0}\cdot (1-q)=1,\\[6pt]&1-q={\frac {1}{R_{0}}},\\[6pt]&q=1-{\frac {1}{R_{0}}}.\end{aligned}}}$

Remember that this is the threshold level. Die out of transmission will only occur if the proportion of immune individuals exceeds this level due to a mass vaccination programme.

We have just calculated the critical immunization threshold (denoted q_c). It is the minimum proportion of the population that must be immunized at birth (or close to birth) in order for the infection to die out in the population.

${\displaystyle q_{c}=1-{\frac {1}{R_{0}}}.}$

Because the fraction of the final size of the population p that is never infected can be defined as:

${\displaystyle \lim _{t\to \infty }S(t)=e^{-\int _{0}^{\infty }\lambda (t)\,dt}=1-p.}$

Hence,

${\displaystyle p=1-e^{-\int _{0}^{\infty }\beta I(t)\,dt}=1-e^{-R_{0}p}.}$

Solving for ${\displaystyle R_{0}}$, we obtain:

${\displaystyle R_{0}={\frac {-\ln(1-p)}{p}}.}$

When mass vaccination cannot exceed the herd immunity

If the vaccine used is insufficiently effective or the required coverage cannot be reached, the program may fail to exceed q_c. Such a program will protect vaccinated individuals from disease, but may change the dynamics of transmission.

Suppose that a proportion of the population q (where q < q_c) is immunised at birth against an infection with R₀ > 1. The vaccination programme changes R₀ to R_q where

${\displaystyle R_{q}=R_{0}(1-q)}$

This change occurs simply because there are now fewer susceptibles in the population who can be infected. R_q is simply R₀ minus those that would normally be infected but that cannot be now since they are immune.

As a consequence of this lower basic reproduction number, the average age of infection A will also change to some new value A_q in those who have been left unvaccinated.

Recall the relation that linked R₀, A and L. Assuming that life expectancy has not changed, now:

${\displaystyle R_{q}={\frac {L}{A_{q}}},}$

${\displaystyle A_{q}={\frac {L}{R_{q}}}={\frac {L}{R_{0}(1-q)}}.}$

But R₀ = L/A so:

${\displaystyle A_{q}={\frac {L}{(L/A)(1-q)}}={\frac {AL}{L(1-q)}}={\frac {A}{1-q}}.}$

Thus, the vaccination program may raise the average age of infection, and unvaccinated individuals will experience a reduced force of infection due to the presence of the vaccinated group. For a disease that leads to greater clinical severity in older populations, the unvaccinated proportion of the population may experience the disease relatively later in life than would occur in the absence of vaccine.

When mass vaccination exceeds the herd immunity

If a vaccination program causes the proportion of immune individuals in a population to exceed the critical threshold for a significant length of time, transmission of the infectious disease in that population will stop. If elimination occurs everywhere at the same time, then this can lead to eradication.

Elimination

Interruption of endemic transmission of an infectious disease, which occurs if each infected individual infects less than one other, is achieved by maintaining vaccination coverage to keep the proportion of immune individuals above the critical immunization threshold.

Eradication

Elimination everywhere at the same time such that the infectious agent dies out (for example, smallpox and rinderpest).

Reliability

Models have the advantage of examining multiple outcomes simultaneously, rather than making a single forecast. Models have shown broad degrees of reliability in past pandemics, such as SARS, SARS-CoV-2, Swine flu, MERS and Ebola.

Podcast

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Podcast 

 

The Serial podcast being played through the Pocket Casts app on an iPhone

A podcast is a program made available in digital format for download over the Internet. For example, an episodic series of digital audio or video files that a user can download to a personal device to listen to at a time of their choosing. Streaming applications and podcasting services provide a convenient and integrated way to manage a personal consumption queue across many podcast sources and playback devices. There also exist podcast search engines, which help users find and share podcast episodes.

A podcast series usually features one or more recurring hosts engaged in a discussion about a particular topic or current event. Discussion and content within a podcast can range from carefully scripted to completely improvised. Podcasts combine elaborate and artistic sound production with thematic concerns ranging from scientific research to slice-of-life journalism. Many podcast series provide an associated website with links and show notes, guest biographies, transcripts, additional resources, commentary, and even a community forum dedicated to discussing the show's content.

The cost to the consumer is low, with many podcasts free to download. Some are underwritten by corporations or sponsored, with the inclusion of commercial advertisements. In other cases, a podcast could be a business venture supported by some combination of a paid subscription model, advertising or product delivered after sale. Because podcast content is often free, podcasting is often classified as a disruptive medium, adverse to the maintenance of traditional revenue models.

Production and listening

Podcasting studio in What Cheer Writers Club in Providence, Rhode Island

A podcast generator maintains a central list of the files on a server as a web feed that one can access through the Internet. The listener or viewer uses special client application software on a computer or media player, known as a podcast client, which accesses this web feed, checks it for updates, and downloads any new files in the series. This process can be automated to download new files automatically, so it may seem to listeners as though podcasters broadcast or "push" new episodes to them. Podcast files can be stored locally on the user's device, or streamed directly. There are several different mobile applications that allow people to follow and listen to podcasts. Many of these applications allow users to download podcasts or stream them on demand. Most podcast players or applications allow listeners to skip around the podcast and to control the playback speed.

Podcasting has been considered a converged medium (a medium that brings together audio, the web and portable media players), as well as a disruptive technology that has caused some individuals in radio broadcasting to reconsider established practices and preconceptions about audiences, consumption, production and distribution.

Podcasts can be produced at little to no cost and are usually disseminated free-of-charge, which sets this medium apart from the traditional 20th-century model of "gate-kept" media and their production tools. Podcasters can, however, still monetize their podcasts by allowing companies to purchase ad time. They can also garner support from listeners through crowdfunding websites like Patreon, which provide special extras and content to listeners for a fee.

Etymology

"Podcast" is a portmanteau of "iPod" and "broadcast". The earliest use of "podcasting" was traced to The Guardian columnist and BBC journalist Ben Hammersley, who invented it in early February 2004 while writing an article for The Guardian newspaper. The term was first used in the audioblogging community in September 2004, when Danny Gregoire introduced it in a message to the iPodder-dev mailing list, from where it was adopted by podcaster Adam Curry. Despite the etymology, the content can be accessed using any computer or similar device that can play media files. The term "podcast" predates Apple's addition of podcasting features to the iPod and the iTunes software. Some sources have suggested the backronym "portable on demand" or "play on demand" for POD to avoid the loose reference to the iPod. This usage has been criticized as a retcon by tech blogger John Gruber.

History

Main article: History of podcasting

In October 2000, the concept of attaching sound and video files in RSS feeds was proposed in a draft by Tristan Louis. The idea was implemented by Dave Winer, a software developer and an author of the RSS format.

Podcasting, once an obscure method of spreading audio information, has become a recognized medium for distributing audio content, whether for corporate or personal use. Podcasts are similar to radio programs in form, but they exist as audio files that can be played at a listener's convenience, anytime and anywhere.

The first application to make this process feasible was iPodderX, developed by August Trometer and Ray Slakinski. By 2007, audio podcasts were doing what was historically accomplished via radio broadcasts, which had been the source of radio talk shows and news programs since the 1930s. This shift occurred as a result of the evolution of internet capabilities along with increased consumer access to cheaper hardware and software for audio recording and editing.

In August 2004, Adam Curry launched his show Daily Source Code. It was a show focused on chronicling his everyday life, delivering news, and discussions about the development of podcasting, as well as promoting new and emerging podcasts. Curry published it in an attempt to gain traction in the development of what would come to be known as podcasting and as a means of testing the software outside of a lab setting. The name Daily Source Code was chosen in the hope that it would attract an audience with an interest in technology. Daily Source Code started at a grassroots level of production and was initially directed at podcast developers. As its audience became interested in the format, these developers were inspired to create and produce their own projects and, as a result, they improved the code used to create podcasts. As more people learned how easy it was to produce podcasts, a community of pioneer podcasters quickly appeared.

In June 2005, Apple released iTunes 4.9 which added formal support for podcasts, thus negating the need to use a separate program in order to download and transfer them to a mobile device. Although this made access to podcasts more convenient and widespread, it also effectively ended advancement of podcatchers by independent developers. Additionally, Apple issued cease and desist orders to many podcast application developers and service providers for using the term "iPod" or "Pod" in their products' names.

The logo used by Apple to represent podcasting in Apple Podcasts.

Within a year, many podcasts from public radio networks like the BBC, CBC Radio One, NPR, and Public Radio International placed many of their radio shows on the iTunes platform. In addition, major local radio stations like WNYC in New York City, WHYY-FM radio in Philadelphia, and KCRW in Los Angeles placed their programs on their websites and later on the iTunes platform. Concurrently, CNET, This Week in Tech, and later Bloomberg Radio, the Financial Times, and other for-profit companies provided podcast content, some using podcasting as their only distribution system.

As of early 2019, the podcasting industry still generated little overall revenue, although the number of persons who listen to podcasts continues to grow steadily. Edison Research, which issues the Podcast Consumer quarterly tracking report, estimates that in 2019, 90 million persons in the U.S. have listened to a podcast in the last month. In 2020, 58% of the population of South Korea and 40% of the Spanish population had listened to a podcast in the last month. 12.5% of the UK population had listened to a podcast in the last week. The form is also acclaimed for its low overhead for a creator to start and maintain their show, merely requiring a good-quality microphone, a computer or mobile device and associated software to edit and upload the final product, and some form of acoustic quieting. Podcast creators tend to have a good listener base because of their relationships with the listeners.

IP issues in trademark and patent law

Trademark applications

Between February 10 and 25 March 2005, Shae Spencer Management, LLC of Fairport, New York filed a trademark application to register the term "podcast" for an "online prerecorded radio program over the internet". On September 9, 2005, the United States Patent and Trademark Office (USPTO) rejected the application, citing Wikipedia's podcast entry as describing the history of the term. The company amended their application in March 2006, but the USPTO rejected the amended application as not sufficiently differentiated from the original. In November 2006, the application was marked as abandoned.

Apple trademark protections

On September 26, 2004, it was reported that Apple Inc. had started to crack down on businesses using the string "POD", in product and company names. Apple sent a cease and desist letter that week to Podcast Ready, Inc., which markets an application known as "myPodder". Lawyers for Apple contended that the term "pod" has been used by the public to refer to Apple's music player so extensively that it falls under Apple's trademark cover. Such activity was speculated to be part of a bigger campaign for Apple to expand the scope of its existing iPod trademark, which included trademarking "IPOD", "IPODCAST", and "POD". On November 16, 2006, the Apple Trademark Department stated that "Apple does not object to third-party usage of the generic term 'podcast' to accurately refer to podcasting services" and that "Apple does not license the term". However, no statement was made as to whether or not Apple believed they held rights to it.

Personal Audio lawsuits

Personal Audio, a company referred to as a "patent troll" by the Electronic Frontier Foundation, filed a patent on podcasting in 2009 for a claimed invention in 1996. In February 2013, Personal Audio started suing high-profile podcasters for royalties, including The Adam Carolla Show and the HowStuffWorks podcast.

In October 2013, the EFF filed a petition with the US Trademark Office to invalidate the Personal Audio patent.

On August 18, 2014, the Electronic Frontier Foundation announced that Adam Carolla had settled with Personal Audio.

On April 10, 2015, the U.S. Patent and Trademark Office invalidated five provisions of Personal Audio's podcasting patent.

Types of podcasts

Enhanced podcasts

An enhanced podcast, also known as a slidecast, is a type of podcast that combines audio with a slide show presentation. It is similar to a video podcast in that it combines dynamically-generated imagery with audio synchronization, but it is different in that it uses presentation software to create the imagery and the sequence of display separately from the time of the original audio podcast recording. The Free Dictionary, YourDictionary, and PC Magazine define an enhanced podcast as "an electronic slide show delivered as a podcast". Enhanced podcasts are podcasts that incorporate graphics and chapters. iTunes developed an enhanced podcast feature called "Audio Hyperlinking" that they patented in 2012. Enhanced podcasts can be used by businesses or in education. Enhanced podcasts can be created using QuickTime AAC or Windows Media files. Enhanced podcasts were first used in 2006.

Fiction podcast

A fiction podcast (also referred to as a "scripted podcast" or "narrative podcast") is similar to a radio drama, but in podcast form. They deliver a fictional story, usually told over multiple episodes and seasons, using multiple voice actors, dialogue, sound effects, and music to enrich the story. Fiction podcasts have attracted a number of well-known actors as voice talents, including Demi Moore & Matthew McConaughey as well as from content producers like Netflix, Spotify, Marvel, and DC Comics. While science-fiction and horror are quite popular, fiction podcasts cover a full range of literary genres from romance, comedy, and drama to fantasy, sci-fi, and detective fiction. Examples of fiction podcasts include The Bright Sessions, The Magnus Archives, Homecoming, Wooden Overcoats , We're Alive and Wolverine: The Long Night.

Podcast novels

A podcast novel (also known as a "serialized audiobook" or "podcast audiobook") is a literary form that combines the concepts of a podcast and an audiobook. Like a traditional novel, a podcast novel is a work of literary fiction; however, it is recorded into episodes that are delivered online over a period of time. The episodes may be delivered automatically via RSS or through a website, blog, or other syndication method. Episodes can be released on a regular schedule, e.g., once a week, or irregularly as each episode is completed. In the same manner as audiobooks, some podcast novels are elaborately narrated with sound effects and separate voice actors for each character, similar to a radio play or scripted podcast, but many have a single narrator and few or no sound effects.

Some podcast novelists give away a free podcast version of their book as a form of promotion. On occasion such novelists have secured publishing contracts to have their novels printed. Podcast novelists have commented that podcasting their novels lets them build audiences even if they cannot get a publisher to buy their books. These audiences then make it easier to secure a printing deal with a publisher at a later date. These podcast novelists also claim the exposure that releasing a free podcast gains them makes up for the fact that they are giving away their work for free.

Video podcasts

A video podcast on the Crab Nebula created by NASA

A video podcast is a podcast that contains video content. Web television series are often distributed as video podcasts. Dead End Days, a serialized dark comedy about zombies released from 31 October 2003 through 2004, is commonly believed to be the first video podcast.

Live podcasts

A number of podcasts are recorded either in total or for specific episodes in front of a live audience. Ticket sales allow the podcasters an additional way of monetising. Some podcasts create specific live shows to tour which are not necessarily included on the podcast feed. Events including the London Podcast Festival, SF Sketchfest and others regularly give a platform for podcasters to perform live to audiences.

Equipment

The most basic equipment for a podcast is a computer and a microphone. It is helpful to have a sound-proof room and headphones. The computer should have a recording or streaming application installed. Typical microphones for podcasting are connected using USB. If the podcast involves two or more people, each person requires a microphone, and a USB audio interface is needed to mix them together. If the podcast includes video (livestreaming), then a separate webcam might be needed, and additional lighting.