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On policy self distillation (OPSD) is promising as it removes the external teacher required by on policy distillation (OPD), yet it still needs asymmetric information between teacher and student to ensure that the self teacher provides a stronger learning signal than the student. Existing methods create this asymmetry either through privileged answers or visual evidence. We ask whether both can be removed, yieldin...
Transition metal dichalcogenides offer a promising platform for integrated coherent light sources, yet lasing has largely relied on hybrid photonic architectures without direct quantum optical verification. Here, we demonstrate an all van der Waals (all vdW) high $β$ nanobeam laser based on a WS$ 2$/MoSe$ 2$/WS$ {2}$ heterostructure, with the MoSe$ 2$ monolayer directly integrated in the WS$ {2}$ based optical res...
To maximize the value of fault tolerant quantum computers, it is essential to develop concrete applications beyond well established domains such as chemistry and condensed matter physics. Here we construct and compile quantum algorithms to simulate the structure of atomic nuclei a topic that has received relatively little attention from the quantum computing community despite its similarities to the electronic str...
Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution. We introduce EnsembleEGNN, a molecular ensemble foundation model that encodes an ensemble by first encoding each conformer with shared Equivariant Graph Neural Network (EGNN) layers, then pooling the resulting conformer representations with a Set At...
A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014 2023) to identify atypical transmission patterns. Anomalies were highly structured in space and time. Ashanti and Northern Regions accounted for most recurrent anomalies, with persistent hotspots at Tamale, Kumasi, and Accra. A key finding was the spatial distinction between anomaly burden (cumulative cases du...
Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy as a one dimensional failure mode. Models must distinguish when to incorporate others' perspectives from when to maintain a well grounded moral judgment. We study the broader resistance compliance process governing this distinction. Across three studies, we show th...
Entanglement purification protocols (EPPs) are essential for improving entanglement fidelity to support fault tolerant distributed quantum information processing. Practical entanglement sources are often heterogeneous and source labels may be unavailable at the EPP layer. We show that classical shared randomness, together with buffer memories, can enhance entanglement purification when source labels are unavailabl...
Unlike large language models (LLMs) that exhibit strong reasoning capabilities, vision language models (VLMs) struggle with visual reasoning, even on geometry problems that admit equivalent text, diagram, and combined diagram+text views. We show that these views often elicit different behaviors: a model may solve a problem from text but fail on the corresponding diagram, or succeed visually while failing textually...
While large audio language models have achieved remarkable progress in auditory perception, they still lag behind text based large language models in deep logical reasoning, primarily due to the scarcity of high quality audio reasoning data. To bridge this gap, we propose X$^3$ OPD, a cross modal on policy distillation framework that transfers reasoning capabilities from a powerful text teacher to an audio languag...
The rapid progress of AI has intensified the long standing pursuit of automation: replacing human participation with algorithms wherever possible. Implicit in this pursuit is the assumption that humans remain in the loop only because current AI systems are not yet sufficiently capable. This paper challenges that assumption. Rather than asking how far automation can extend, we ask where its conceptual limits lie an...
Variational quantum algorithms are a leading approach to near term quantum computing, but their scalability can be limited by barren plateaus and the sampling cost of resolving small changes in the loss landscape. Here, we study the trainability of passive linear optical quantum circuits and introduce a framework based on the ratio of sample variance to circuit variance. This ratio determines the number of circuit...
Entanglement entropy at one dimensional criticality typically follows the Calabrese Cardy scaling. In non Hermitian critical chains near exceptional points, however, we show that the biorthogonal entropy of a finite system retains an additional sensitivity to a small energy gap \(Δ\) even when \(Δ< 1/L\). On top of the usual Calabrese Cardy term, we find an interval independent contribution \(S {\rm res}=\log(ΔL)\...
Formal verification is becoming increasingly practical for quantum computing, yet the ability of AI agents to construct machine checkable proofs in this domain remains unmeasured. We introduce Lean QuantumAlg Bench and Lean QIT Bench, two Lean 4 benchmarks containing 36 and 40 theorem completion tasks for quantum algorithms and quantum information theory, respectively. Every task compiles in a fixed environment an...
In classical and quantum statistics, high dimensional unknown parameters are abundant and it is often prudent to make minimal assumptions about them using so called semiparametric models. To attack a wide range of semiparametric problems in one broad stroke, we present a unified treatment of statistical efficiency for classical and quantum semiparametric models, generalizing the Cramér Rao and Helstrom bounds beyo...
Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graphics methods rely on manual creation, requiring extensive human effort to fine tune materials, motions, and visual fidelity. Recent advances in generative foundation models have sparked interest in learning to generate such 4D worlds from large scale data; however, ex...
A central challenge for cavity QED based quantum technologies is to make high cooperativity optical interfaces compatible with site resolved arrays of single atoms. Here, we demonstrate optical tweezer arrays of individual $^{87}$Rb atoms inside a fiber Fabry Perot microcavity with single atom cooperativity $\mathcal{C} \sim 90$. We combine background free site resolved fluorescence imaging of extended arrays with...
Even a current high capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay its direction. Using OpenAI's gpt 5.6 sol model alias, we test 25 pre specified mirrored trade off profiles. Direct exposure to an objective authorizing concealment, fabrication, and pressure produced advice net opposed to its target. After an Id and Censor transformed the same o...
We present a strategic plan for neutral atom quantum computation, bringing together hardware development and theory advancements to achieve the goal of practical quantum advantage. The concept of practical quantum advantage is defined, along with how to verify claims of advantage, and approaches to designing quantum algorithms that deliver practical advantage. Future directions for neutral atom quantum processor h...
Speculative decoding accelerates autoregressive generation by having a cheap draft propose tokens that a target verifies in parallel. Frontier models increasingly ship a built in Multi Token Prediction (MTP/NEXTN) draft head under the assumption that the draft is negligibly cheap. At million token context this breaks: an MTP draft head typically runs full attention over the entire KV cache at every draft step, so ...
We give an unconditional construction of information theoretically secure one time private key unclonable encryption scheme for one bit messages, with efficient encryption and decryption and exponentially small unclonable indistinguishability advantage.
