N. F. Johnson’s research while affiliated with George Washington University and other places

Nanoscale quantum dots in microwave cavities can be used as a laboratory for exploring electron-electron interactions and their spin in the presence of quantized light and a magnetic field. We show how a simple theoretical model of this interplay at resonance predicts complex but measurable effects. New polariton states emerge that combine spin, relative modes, and radiation. These states have intricate spin-space correlations and undergo polariton transitions controlled by the microwave cavity field. We uncover novel topological effects involving highly correlated spin and charge density that display singlet-triplet and inhomogeneous Bell-state distributions. Signatures of these transitions are imprinted in the photon distribution, which will allow for optical read-out protocols in future experiments and nanoscale quantum technologies.

Nanoscale quantum dots in microwave cavities can be used as a laboratory for exploring electron-electron interactions and their spin in the presence of quantized light and a magnetic field. We show how a simple theoretical model of this interplay at resonance predicts complex but measurable effects. New polariton states emerge that combine spin, relative modes, and radiation. These states have intricate spin-space correlations and undergo polariton transitions controlled by the microwave cavity field. We uncover novel topological effects involving highly correlated spin and charge density, that display singlet-triplet and inhomogeneous Bell-state distributions. Signatures of these transitions are imprinted in the photon distribution, which will allow for optical read out protocols in future experiments and nanoscale quantum technologies.

Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.

We uncover a remarkably regular array of singularitylike structures within the deep strong-coupling limit of qubit-oscillator (e.g., light-matter) systems described by the quantum Rabi model as a function of time and coupling strength. These nonanalytic anomalies in the Loschmidt amplitude (echoes) suggest the existence of new forms of dynamical phase transition within this deep strong-coupling regime. The key feature whereby the initial state collapses into orthogonal states at select values of the interaction strength and select times may be used to enhance—or attack—quantum information processing or computation schemes that rely on removing—or retaining—a given quantum state.

Ensuring widespread public exposure to best-science guidance is crucial in a crisis, e.g. Covid-19, climate change. Mapping the emitter-receiver dynamics of Covid-19 guidance among 87 million Facebook users, we uncover a multi-sided battle over exposure that gets lost well before the pandemic's official announcement. By the time Covid-19 vaccines emerge, the mainstream majority -- including many parenting communities -- have moved even closer to more extreme communities. The hidden heterogeneity explains why Facebook's own promotion of best-science guidance also missed key audience segments. A simple mathematical model reproduces these exposure dynamics at the system level. Our findings can be used to tailor guidance at scale while accounting for individual diversity, and to predict tipping point behavior and system-level responses to interventions.

We uncover a remarkably regular array of singularity-like structures within the deep strong-coupling limit of qubit-oscillator (e.g. light-matter) systems described by the quantum Rabi model, as a function of time and coupling strength. These non-analytic anomalies in the Loschmidt amplitude (echoes) suggest the existence of new forms of dynamical phase transition within this deep strong-coupling regime. The key feature whereby the initial state collapses into orthogonal states at select values of the interaction strength and select times, may be used to enhance - or attack - quantum information processing or computation schemes that rely on removing - or retaining - a given quantum state.

We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.

Disrupting the emergence and evolution of potentially violent online extremist movements is a crucial challenge. Extremism research has analyzed such movements in detail, focusing on individual- and movement-level characteristics. But are there system-level commonalities in the ways these movements emerge and grow? Here we compare the growth of the Boogaloos, a new and increasingly prominent U.S. extremist movement, to the growth of online support for ISIS, a militant, terrorist organization based in the Middle East that follows a radical version of Islam. We show that the early dynamics of these two online movements follow the same mathematical order despite their stark ideological, geographical, and cultural differences. The evolution of both movements, across scales, follows a single shockwave equation that accounts for heterogeneity in online interactions. These scientific properties suggest specific policies to address online extremism and radicalization. We show how actions by social media platforms could disrupt the onset and ‘flatten the curve’ of such online extremism by nudging its collective chemistry. Our results provide a system-level understanding of the emergence of extremist movements that yields fresh insight into their evolution and possible interventions to limit their growth.

Parents - particularly moms - increasingly consult social media for support when taking decisions about their young children, and likely also when advising other family members such as elderly relatives. Minimizing malignant online influences is therefore crucial to securing their assent for policies ranging from vaccinations, masks and social distancing against the pandemic, to household best practices against climate change, to acceptance of future 5G towers nearby. Here we show how a strengthening of bonds across online communities during the pandemic, has led to non-Covid-19 conspiracy theories (e.g. fluoride, chemtrails, 5G) attaining heightened access to mainstream parent communities. Alternative health communities act as the critical conduits between conspiracy theorists and parents, and make the narratives more palatable to the latter. We demonstrate experimentally that these inter-community bonds can perpetually generate new misinformation, irrespective of any changes in factual information. Our findings show explicitly why Facebook's current policies have failed to stop the mainstreaming of non-Covid-19 and Covid-19 conspiracy theories and misinformation, and why targeting the largest communities will not work. A simple yet exactly solvable and empirically grounded mathematical model, shows how modest tailoring of mainstream communities' couplings could prevent them from tipping against establishment guidance. Our conclusions should also apply to other social media platforms and topics.

From the moment the first COVID-19 vaccines are rolled out, there will need to be a large fraction of the global population ready in line. It is therefore crucial to start managing the growing global hesitancy to any such COVID-19 vaccine. The current approach of trying to convince the "no"s cannot work quickly enough, nor can the current policy of trying to find, remove and/or rebut all the individual pieces of COVID and vaccine misinformation. Instead, we show how this can be done in a simpler way by moving away from chasing misinformation content and focusing instead on managing the "yes--no--not-sure" hesitancy ecosystem.