Quantum-enabled millimetre wave to optical transduction using neutral atoms.

Early experiments with transiting circular Rydberg atoms in a superconducting resonator laid the foundations of modern cavity and circuit quantum electrodynamics1, and helped explore the defining features of quantum mechanics such as entanglement. Whereas ultracold atoms and superconducting circuits have since taken rather independent paths in the exploration of new physics, taking advantage of their complementary strengths in an integrated system enables access to fundamentally new parameter regimes and device capabilities2,3. Here we report on such a system, coupling an ensemble of cold 85Rb atoms simultaneously to an, as far as we are aware, first-of-its-kind optically accessible, three-dimensional superconducting resonator4 and a vibration-suppressed optical cavity in a cryogenic (5 K) environment. To demonstrate the capabilities of this platform, and with an eye towards quantum networking5, we leverage the strong coupling between Rydberg atoms and the superconducting resonator to implement a quantum-enabled millimetre wave (mmwave) photon to optical photon transducer6. We measured an internal conversion efficiency of 58(11)%, a conversion bandwidth of 360(20) kHz and added thermal noise of 0.6 photons, in agreement with a parameter-free theory. Extensions of this technique will allow near-unity efficiency transduction in both the mmwave and microwave regimes. More broadly, our results open a new field of hybrid mmwave/optical quantum science, with prospects for operation deep in the strong coupling regime for efficient generation of metrologically or computationally useful entangled states7 and quantum simulation/computation with strong non-local interactions8.

Disorder-assisted assembly of strongly correlated fluids of light.

Guiding many-body systems to desired states is a central challenge of modern quantum science, with applications from quantum computation1,2 to many-body physics3 and quantum-enhanced metrology4. Approaches to solving this problem include step-by-step assembly5,6, reservoir engineering to irreversibly pump towards a target state7,8 and adiabatic evolution from a known initial state9,10. Here we construct low-entropy quantum fluids of light in a Bose-Hubbard circuit by combining particle-by-particle assembly and adiabatic preparation. We inject individual photons into a disordered lattice for which the eigenstates are known and localized, then adiabatically remove this disorder, enabling quantum fluctuations to melt the photons into a fluid. Using our platform11, we first benchmark this lattice melting technique by building and characterizing arbitrary single-particle-in-a-box states, then assemble multiparticle strongly correlated fluids. Intersite entanglement measurements performed through single-site tomography indicate that the particles in the fluid delocalize, whereas two-body density correlation measurements demonstrate that they also avoid one another, revealing Friedel oscillations characteristic of a Tonks-Girardeau gas12,13. This work opens new possibilities for the preparation of topological and otherwise exotic phases of synthetic matter3,14,15.

Observation of Laughlin states made of light.

Much of the richness in nature emerges because simple constituents form an endless variety of ordered states1. Whereas many such states are fully characterized by symmetries2, interacting quantum systems can exhibit topological order and are instead characterized by intricate patterns of entanglement3,4. A paradigmatic example of topological order is the Laughlin state5, which minimizes the interaction energy of charged particles in a magnetic field and underlies the fractional quantum Hall effect6. Efforts have been made to enhance our understanding of topological order by forming Laughlin states in synthetic systems of ultracold atoms7,8 or photons9-11. Nonetheless, electron gases remain the only systems in which such topological states have been definitively observed6,12-14. Here we create Laughlin-ordered photon pairs using a gas of strongly interacting, lowest-Landau-level polaritons as a photon collider. Initially uncorrelated photons enter a cavity and hybridize with atomic Rydberg excitations to form polaritons15-17, quasiparticles that here behave like electrons in the lowest Landau level owing to a synthetic magnetic field created by Floquet engineering18 a twisted cavity11,19 and by Rydberg-mediated interactions between them16,17,20,21. Polariton pairs collide and self-organize to avoid each other while conserving angular momentum. Our finite-lifetime polaritons only weakly prefer such organization. Therefore, we harness the unique tunability of Floquet polaritons to distil high-fidelity Laughlin states of photons outside the cavity. Particle-resolved measurements show that these photons avoid each other and exhibit angular momentum correlations, the hallmarks of Laughlin physics. This work provides broad prospects for the study of topological quantum light22.

Neural tracking measures of speech intelligibility: Manipulating intelligibility while keeping acoustics unchanged.

Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle the effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise-vocoded, ~20 s duration) are presented twice, but the second presentation is preceded by the original (nondegraded) version of the speech. This intermediate priming, which generates a "pop-out" percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affect acoustic and linguistic neural representations using multivariate temporal response functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. mTRFs analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming but only by the acoustics of the stimuli (bottom-up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later (~400 ms latency) word processing stage, in prefrontal cortex, in line with engagement of top-down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension.

Interacting Floquet polaritons.

Ordinarily, photons do not interact with one another. However, atoms can be used to mediate photonic interactions1,2, raising the prospect of forming synthetic materials3 and quantum information systems4-7 from photons. One promising approach combines highly excited Rydberg atoms8-12 with the enhanced light-matter coupling of an optical cavity to convert photons into strongly interacting polaritons13-15. However, quantum materials made of optical photons have not yet been realized, because the experimental challenge of coupling a suitable atomic sample with a degenerate cavity has constrained cavity polaritons to a single spatial mode that is resonant with an atomic transition. Here we use Floquet engineering16,17-the periodic modulation of a quantum system-to enable strongly interacting polaritons to access multiple spatial modes of an optical cavity. First, we show that periodically modulating an excited state of rubidium splits its spectral weight to generate new lines-beyond those that are ordinarily characteristic of the atom-separated by multiples of the modulation frequency. Second, we use this capability to simultaneously generate spectral lines that are resonant with two chosen spatial modes of a non-degenerate optical cavity, enabling what we name 'Floquet polaritons' to exist in both modes. Because both spectral lines correspond to the same Floquet-engineered atomic state, adding a single-frequency field is sufficient to couple both modes to a Rydberg excitation. We demonstrate that the resulting polaritons interact strongly in both cavity modes simultaneously. The production of Floquet polaritons provides a promising new route to the realization of ordered states of strongly correlated photons, including crystals and topological fluids, as well as quantum information technologies such as multimode photon-by-photon switching.

Electromagnetic and gravitational responses of photonic Landau levels.

Topology has recently become a focus in condensed matter physics, arising in the context of the quantum Hall effect and topological insulators. In both of these cases, the topology of the system is defined through bulk properties ('topological invariants') but detected through surface properties. Here we measure three topological invariants of a quantum Hall material-photonic Landau levels in curved space-through local electromagnetic and gravitational responses of the bulk material. Viewing the material as a many-port circulator, the Chern number (a topological invariant) manifests as spatial winding of the phase of the circulator. The accumulation of particles near points of high spatial curvature and the moment of inertia of the resultant particle density distribution quantify two additional topological invariants-the mean orbital spin and the chiral central charge. We find that these invariants converge to their global values when probed over increasing length scales (several magnetic lengths), consistent with the intuition that the bulk and edges of a system are distinguishable only for sufficiently large samples (larger than roughly one magnetic length). Our experiments are enabled by applying quantum optics tools to synthetic topological matter (here twisted optical resonators). Combined with advances in Rydberg-mediated photon collisions, our work will enable precision characterization of topological matter in photon fluids.

Author Correction: A dissipatively stabilized Mott insulator of photons.

Change history: In this Article, two additional references (now added as refs 12 and 14) should have been cited at the end of the sentence "Recently, photonic systems have emerged as a platform of interest for the exploration of synthetic quantum matter.". This has been corrected online.

A dissipatively stabilized Mott insulator of photons.

Superconducting circuits are a competitive platform for quantum computation because they offer controllability, long coherence times and strong interactions-properties that are essential for the study of quantum materials comprising microwave photons. However, intrinsic photon losses in these circuits hinder the realization of quantum many-body phases. Here we use superconducting circuits to explore strongly correlated quantum matter by building a Bose-Hubbard lattice for photons in the strongly interacting regime. We develop a versatile method for dissipative preparation of incompressible many-body phases through reservoir engineering and apply it to our system to stabilize a Mott insulator of photons against losses. Site- and time-resolved readout of the lattice allows us to investigate the microscopic details of the thermalization process through the dynamics of defect propagation and removal in the Mott phase. Our experiments demonstrate the power of superconducting circuits for studying strongly correlated matter in both coherent and engineered dissipative settings. In conjunction with recently demonstrated superconducting microwave Chern insulators, we expect that our approach will enable the exploration of topologically ordered phases of matter.

The influence of specific weather types on stroke occurrence: an analysis of 23,000 patients from Augsburg, Germany.

For the first time, the relationships between large-scale weather types and local stroke events in the urban area of Augsburg, Germany are analyzed. Over 23,000 stroke cases (2006 - 2020) were standardized to account for long-term trends and seasonality. Using ERA5 reanalysis data, a composite analysis identified stroke-related atmospheric variables, while seasonal weather types were classified via the neural network algorithm of self-organizing maps. Cyclonic westerlies during the cold season, which transport warm air masses from the Atlantic Ocean to Germany, were a major risk factor for ischemic stroke, while colder easterly conditions reduced stroke incidence. In the warm season, both anticyclonic conditions and westerly/northerly air advection, leading to slightly warmer or distinctly colder temperatures, were linked to increased ischemic stroke risk. Additionally, hemorrhagic strokes in the cold season were triggered by weather conditions contrary to those associated with ischemic strokes and transitory ischemic attacks.

Effects of aging on cortical representations of continuous speech.

Understanding speech in a noisy environment is crucial in day-to-day interactions and yet becomes more challenging with age, even for healthy aging. Age-related changes in the neural mechanisms that enable speech-in-noise listening have been investigated previously; however, the extent to which age affects the timing and fidelity of encoding of target and interfering speech streams is not well understood. Using magnetoencephalography (MEG), we investigated how continuous speech is represented in auditory cortex in the presence of interfering speech in younger and older adults. Cortical representations were obtained from neural responses that time-locked to the speech envelopes with speech envelope reconstruction and temporal response functions (TRFs). TRFs showed three prominent peaks corresponding to auditory cortical processing stages: early (∼50 ms), middle (∼100 ms), and late (∼200 ms). Older adults showed exaggerated speech envelope representations compared with younger adults. Temporal analysis revealed both that the age-related exaggeration starts as early as ∼50 ms and that older adults needed a substantially longer integration time window to achieve their better reconstruction of the speech envelope. As expected, with increased speech masking envelope reconstruction for the attended talker decreased and all three TRF peaks were delayed, with aging contributing additionally to the reduction. Interestingly, for older adults the late peak was delayed, suggesting that this late peak may receive contributions from multiple sources. Together these results suggest that there are several mechanisms at play compensating for age-related temporal processing deficits at several stages but which are not able to fully reestablish unimpaired speech perception.NEW & NOTEWORTHY We observed age-related changes in cortical temporal processing of continuous speech that may be related to older adults' difficulty in understanding speech in noise. These changes occur in both timing and strength of the speech representations at different cortical processing stages and depend on both noise condition and selective attention. Critically, their dependence on noise condition changes dramatically among the early, middle, and late cortical processing stages, underscoring how aging differentially affects these stages.

Optical mode conversion via spatiotemporally modulated atomic susceptibility.

Light is an excellent medium for both classical and quantum information transmission due to its speed, manipulability, and abundant degrees of freedom into which to encode information. Recently, space-division multiplexing has gained attention as a means to substantially increase the rate of information transfer by utilizing sets of infinite-dimensional propagation eigenmodes such as the Laguerre-Gaussian "donut" modes. Encoding in these high-dimensional spaces necessitates devices capable of manipulating photonic degrees of freedom with high efficiency. In this work, we demonstrate controlling the optical susceptibility of an atomic sample can be used as powerful tool for manipulating the degrees of freedom of light that pass through the sample. Utilizing this tool, we demonstrate photonic mode conversion between two Laguerre-Gaussian modes of a twisted optical cavity with high efficiency. We spatiotemporally modulate the optical susceptibility of an atomic sample that sits at the cavity waist using an auxiliary Stark-shifting beam, in effect creating a mode-coupling optic that converts modes of orbital angular momentum l = 3 → l = 0. The internal conversion efficiency saturates near unity as a function of the atom number and modulation beam intensity, finding application in topological few-body state preparation, quantum communication, and potential development as a flexible tabletop device.

Neural speech restoration at the cocktail party: Auditory cortex recovers masked speech of both attended and ignored speakers.

Humans are remarkably skilled at listening to one speaker out of an acoustic mixture of several speech sources. Two speakers are easily segregated, even without binaural cues, but the neural mechanisms underlying this ability are not well understood. One possibility is that early cortical processing performs a spectrotemporal decomposition of the acoustic mixture, allowing the attended speech to be reconstructed via optimally weighted recombinations that discount spectrotemporal regions where sources heavily overlap. Using human magnetoencephalography (MEG) responses to a 2-talker mixture, we show evidence for an alternative possibility, in which early, active segregation occurs even for strongly spectrotemporally overlapping regions. Early (approximately 70-millisecond) responses to nonoverlapping spectrotemporal features are seen for both talkers. When competing talkers' spectrotemporal features mask each other, the individual representations persist, but they occur with an approximately 20-millisecond delay. This suggests that the auditory cortex recovers acoustic features that are masked in the mixture, even if they occurred in the ignored speech. The existence of such noise-robust cortical representations, of features present in attended as well as ignored speech, suggests an active cortical stream segregation process, which could explain a range of behavioral effects of ignored background speech.

Poststroke acute dysexecutive syndrome, a disorder resulting from minor stroke due to disruption of network dynamics.

Stroke patients with small central nervous system infarcts often demonstrate an acute dysexecutive syndrome characterized by difficulty with attention, concentration, and processing speed, independent of lesion size or location. We use magnetoencephalography (MEG) to show that disruption of network dynamics may be responsible. Nine patients with recent minor strokes and eight age-similar controls underwent cognitive screening using the Montreal cognitive assessment (MoCA) and MEG to evaluate differences in cerebral activation patterns. During MEG, subjects participated in a visual picture-word matching task. Task complexity was increased as testing progressed. Cluster-based permutation tests determined differences in activation patterns within the visual cortex, fusiform gyrus, and lateral temporal lobe. At visit 1, MoCA scores were significantly lower for patients than controls (median [interquartile range] = 26.0 [4] versus 29.5 [3], P = 0.005), and patient reaction times were increased. The amplitude of activation was significantly lower after infarct and demonstrated a pattern of temporal dispersion independent of stroke location. Differences were prominent in the fusiform gyrus and lateral temporal lobe. The pattern suggests that distributed network dysfunction may be responsible. Additionally, controls were able to modulate their cerebral activity based on task difficulty. In contrast, stroke patients exhibited the same low-amplitude response to all stimuli. Group differences remained, to a lesser degree, 6 mo later; while MoCA scores and reaction times improved for patients. This study suggests that function is a globally distributed property beyond area-specific functionality and illustrates the need for longer-term follow-up studies to determine whether abnormal activation patterns ultimately resolve or another mechanism underlies continued recovery.

Cortical Processing of Arithmetic and Simple Sentences in an Auditory Attention Task.

Cortical processing of arithmetic and of language rely on both shared and task-specific neural mechanisms, which should also be dissociable from the particular sensory modality used to probe them. Here, spoken arithmetical and non-mathematical statements were employed to investigate neural processing of arithmetic, compared with general language processing, in an attention-modulated cocktail party paradigm. Magnetoencephalography (MEG) data were recorded from 22 human subjects listening to audio mixtures of spoken sentences and arithmetic equations while selectively attending to one of the two speech streams. Short sentences and simple equations were presented diotically at fixed and distinct word/symbol and sentence/equation rates. Critically, this allowed neural responses to acoustics, words, and symbols to be dissociated from responses to sentences and equations. Indeed, the simultaneous neural processing of the acoustics of words and symbols was observed in auditory cortex for both streams. Neural responses to sentences and equations, however, were predominantly to the attended stream, originating primarily from left temporal, and parietal areas, respectively. Additionally, these neural responses were correlated with behavioral performance in a deviant detection task. Source-localized temporal response functions (TRFs) revealed distinct cortical dynamics of responses to sentences in left temporal areas and equations in bilateral temporal, parietal, and motor areas. Finally, the target of attention could be decoded from MEG responses, especially in left superior parietal areas. In short, the neural responses to arithmetic and language are especially well segregated during the cocktail party paradigm, and the correlation with behavior suggests that they may be linked to successful comprehension or calculation.SIGNIFICANCE STATEMENT Neural processing of arithmetic relies on dedicated, modality independent cortical networks that are distinct from those underlying language processing. Using a simultaneous cocktail party listening paradigm, we found that these separate networks segregate naturally when listeners selectively attend to one type over the other. Neural responses in the left temporal lobe were observed for both spoken sentences and equations, but the latter additionally showed bilateral parietal activity consistent with arithmetic processing. Critically, these responses were modulated by selective attention and correlated with task behavior, consistent with reflecting high-level processing for speech comprehension or correct calculations. The response dynamics show task-related differences that were used to reliably decode the attentional target of sentences or equations.

Neural Markers of Speech Comprehension: Measuring EEG Tracking of Linguistic Speech Representations, Controlling the Speech Acoustics.

When listening to speech, our brain responses time lock to acoustic events in the stimulus. Recent studies have also reported that cortical responses track linguistic representations of speech. However, tracking of these representations is often described without controlling for acoustic properties. Therefore, the response to these linguistic representations might reflect unaccounted acoustic processing rather than language processing. Here, we evaluated the potential of several recently proposed linguistic representations as neural markers of speech comprehension. To do so, we investigated EEG responses to audiobook speech of 29 participants (22 females). We examined whether these representations contribute unique information over and beyond acoustic neural tracking and each other. Indeed, not all of these linguistic representations were significantly tracked after controlling for acoustic properties. However, phoneme surprisal, cohort entropy, word surprisal, and word frequency were all significantly tracked over and beyond acoustic properties. We also tested the generality of the associated responses by training on one story and testing on another. In general, the linguistic representations are tracked similarly across different stories spoken by different readers. These results suggests that these representations characterize the processing of the linguistic content of speech.SIGNIFICANCE STATEMENT For clinical applications, it would be desirable to develop a neural marker of speech comprehension derived from neural responses to continuous speech. Such a measure would allow for behavior-free evaluation of speech understanding; this would open doors toward better quantification of speech understanding in populations from whom obtaining behavioral measures may be difficult, such as young children or people with cognitive impairments, to allow better targeted interventions and better fitting of hearing devices.

NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis.

Identifying the directed connectivity that underlie networked activity between different cortical areas is critical for understanding the neural mechanisms behind sensory processing. Granger causality (GC) is widely used for this purpose in functional magnetic resonance imaging analysis, but there the temporal resolution is low, making it difficult to capture the millisecond-scale interactions underlying sensory processing. Magnetoencephalography (MEG) has millisecond resolution, but only provides low-dimensional sensor-level linear mixtures of neural sources, which makes GC inference challenging. Conventional methods proceed in two stages: First, cortical sources are estimated from MEG using a source localization technique, followed by GC inference among the estimated sources. However, the spatiotemporal biases in estimating sources propagate into the subsequent GC analysis stage, may result in both false alarms and missing true GC links. Here, we introduce the Network Localized Granger Causality (NLGC) inference paradigm, which models the source dynamics as latent sparse multivariate autoregressive processes and estimates their parameters directly from the MEG measurements, integrated with source localization, and employs the resulting parameter estimates to produce a precise statistical characterization of the detected GC links. We offer several theoretical and algorithmic innovations within NLGC and further examine its utility via comprehensive simulations and application to MEG data from an auditory task involving tone processing from both younger and older participants. Our simulation studies reveal that NLGC is markedly robust with respect to model mismatch, network size, and low signal-to-noise ratio, whereas the conventional two-stage methods result in high false alarms and mis-detections. We also demonstrate the advantages of NLGC in revealing the cortical network-level characterization of neural activity during tone processing and resting state by delineating task- and age-related connectivity changes.

Synthetic Landau levels for photons.

Synthetic photonic materials are an emerging platform for exploring the interface between microscopic quantum dynamics and macroscopic material properties. Photons experiencing a Lorentz force develop handedness, providing opportunities to study quantum Hall physics and topological quantum science. Here we present an experimental realization of a magnetic field for continuum photons. We trap optical photons in a multimode ring resonator to make a two-dimensional gas of massive bosons, and then employ a non-planar geometry to induce an image rotation on each round-trip. This results in photonic Coriolis/Lorentz and centrifugal forces and so realizes the Fock­Darwin Hamiltonian for photons in a magnetic field and harmonic trap. Using spatial- and energy-resolved spectroscopy, we track the resulting photonic eigenstates as radial trapping is reduced, finally observing a photonic Landau level at degeneracy. To circumvent the challenge of trap instability at the centrifugal limit, we constrain the photons to move on a cone. Spectroscopic probes demonstrate flat space (zero curvature) away from the cone tip. At the cone tip, we observe that spatial curvature increases the local density of states, and we measure fractional state number excess consistent with the Wen­Zee theory, providing an experimental test of this theory of electrons in both a magnetic field and curved space. This work opens the door to exploration of the interplay of geometry and topology, and in conjunction with Rydberg electromagnetically induced transparency, enables studies of photonic fractional quantum Hall fluids and direct detection of anyons.

Optical mode conversion in coupled Fabry-Perot resonators.

Low-loss conversion among a complete and orthogonal set of optical modes is important for high-bandwidth quantum and classical communication. In this Letter, we explore tunable impedance mismatch between coupled Fabry-Perot resonators as a powerful tool for manipulation of the spatial and temporal properties of optical fields. In the single-mode regime, frequency-dependent impedance matching enables tunable finesse optical resonators. Introducing the spatial dependence of the impedance mismatch enables coherent spatial mode conversion of optical photons at near-unity efficiency. We experimentally demonstrate a NIR resonator whose finesse is tunable over a decade, and an optical mode converter with efficiency >75% for the first six Hermite-Gauss modes. We anticipate that this new perspective on coupled multimode resonators will have exciting applications in micro- and nano-photonics and computer-aided inverse design.

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise.

Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

Disease diagnosis and treatment; could theranostics change everything?

There has always been an intimate and complex relationship between the diagnosis of a disease and its treatment. The approach dubbed theranostics aims to combine diagnostic techniques with therapeutic ones by deploying the same molecule in two roles, exploiting the specificity of its function to render disease treatment more effective. Does this technical development have the potential to change our conception of disease diagnosis? With the treatment approach so intimately linked to the diagnostic tool, might it be possible to treat a disease without having first made an independent clinical or laboratory diagnosis? Here we discuss medical diagnosis, arguing for three categories of diagnosis, before presenting an example of a theranostic approach using radioactive prostate-specific membrane antigen ligands. This example allows us to envision a form of theranostic agent that would be able to diagnose a cancer, for example, and engage directly in its treatment, opening up the possibility of treating patients at risk of developing this cancer without any other clinical diagnostic steps. Would it be a problem if these approaches eventually became independent of any specialist clinical diagnostic supervision? If a theranostic technique is shown to work, following its own logic, do we still need an independent 'traditional' diagnosis prior to its use? We argue that such a diagnosis would no longer be necessary provided certain conditions are fulfilled.