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Marin Soljačić

Publications and source records attributed to Marin Soljačić.

At least 19 recordsLinked to original sources
arXiv2026

Read-Rezayi fractional Chern insulators in modulated Bernal graphene

Fibonacci anyons provide a universal platform for topological quantum computation, and emerge as low-energy excitations in the $\mathbb{Z}_3$ Read-Rezayi phase in the fractional quantum Hall effect. However, realistic microscopic realizations of this phase in the absence of a magnetic field have remained elusive. We study a model of periodically modulated Bernal bilayer graphene with gate-screened Coulomb interactions. Using the recently developed target-phase optimization method in conjunction with band-projected exact diagonalization, we identify at filling $ν=3/5$ a region of parameter space whose ground state is consistent with a Read-Rezayi fractional Chern insulator. The partially filled band from which it arises is a part of a two-band complex which mimics geometric aspects of the lowest and first Landau levels, with the ground state at $ν=1/2$ consistent with the Moore-Read state. Our results suggest that modulated Bernal graphene can realize delicate non-Abelian fractional quantum Hall states at zero magnetic field, while demonstrating target-phase optimization as a practical route to discovering such phases in realistic, high-dimensional microscopic models.

cond-mat.str-el↗
arXiv2026

SMat-Attention: Structured Long-Context Sequence Modeling

Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time training and constant-time decoding by compressing history into a fixed-size state. In this work, we ask whether we can connect these regimes through a tunable notion of structure. To this end, we introduce Structured Matrix Attention (SMat-Attention) via a family of causal masks with structured long-range routing whose row supports have VC-dimension $d$. In our construction, $d=1$ recovers the standard causal mask, and increasing $d$ permits richer subset-routing patterns. We give chunkwise forward and backward algorithms to enable hardware-efficiency. For sequences of length $T$, the hard-routing construction takes $O(T^{2-3/d}+T)$ work, despite the mask being dense, for our prescribed family. In fixed-horizon streaming, decoding after the distant prefix takes constant time per token using $O(T^{1-1/d})$ cached states. SMat-Attention therefore makes VC-dimension an explicit knob governing access-pattern complexity, prefill cost, and decoding memory. Empirically, subset-routing and rule-assisted multi-key retrieval experiments illustrate the masks' routing expressiveness. Extensions to Mamba-2 and Gated DeltaNet using learned routing with top-$k$ query reads retain subquadratic prefill, improve recall accuracy over the backbones in several settings, and achieve comparable small-scale language-modeling performance.

cs.AI↗
arXiv2026

Vision-Language Agents for Active Perception in Optics Laboratories

Vision-language models (VLMs) are increasingly being used in scientific workflows, but their ability as agents to directly control laboratory experiments from visual feedback remains underexplored. This capability is important because many laboratory tasks do not naturally provide dense, pre-defined numerical objectives: informative signals can be sparse, intermittent, or visually ambiguous. A more general laboratory agent should instead be able to interpret visual observations, take actions to acquire useful feedback, and adapt its behavior based on the consequences of those actions. We study whether general-purpose VLMs can perform this kind of closed-loop scientific control using experimental optics as a testbed. We evaluate agents on three experimental systems that isolate distinct capabilities: a Michelson interferometer, a two-mirror cavity, and a four-mirror optical relay. The agents observe camera images, directly issue actuator and measurement commands, and retain their interaction history without receiving an engineered scalar objective during control. Across these experiments and matched simulations, we find that, given task-specific natural-language guidance, VLMs can estimate actuator-response relationships, resolve ambiguous observations through intervention, and actively create informative visual feedback when signals are sparse. These results suggest that pretrained multimodal models can serve as important decision-making agents within the experimental loop. Our work also establishes optics as a physically grounded testbed for visual reasoning and active perception in scientific agents.

physics.optics↗
arXiv2026

Zoology of chiral superconductors in Chern bands

Evidence for chiral superconductivity has recently been observed in several van der Waals systems including rhombohedral multilayer graphene and twisted bilayer MoTe$_2$. In the latter, superconductivity emerges at carrier densities near a fractional Chern insulator. This raises the question of what kinds of superconductors may emerge in a system of electrons in a topological band with strong repulsive interactions. Here, we adapt the target-phase optimization method to search for chiral superconductors in a minimal model of interacting electrons in a Chern band. We construct a differentiable loss function for superconductors from the sign oscillation of the pair-binding energy and combine gradient-based optimization with a rigorous screening procedure to identify and characterize superconductors. Applying this framework within exact diagonalization to spinless electrons in periodically modulated Landau levels with screened Coulomb interactions, we uncover a broad family of chiral superconducting phases. At filling $ν=2/3$, we recover the previously identified $f-\mathrm{i} f$ hole superconductors and find additional $p\pm \mathrm{i} p$ and $f+\mathrm{i} f$ superconductors of both electrons and holes, occurring near and far from the limit of ideal quantum geometry. At $ν=1/2$, we identify $p- \mathrm{i} p$ electron and $f-\mathrm{i} f$ hole superconductors and find, for the first time, a direct transition between chiral superconductors and composite Fermi liquids. Our results reveal that chiral superconductivity in Chern bands comprises a diverse landscape of competing pairing instabilities and establish target-phase optimization as a general strategy for searching for quantum phases in complex interacting systems.

cond-mat.str-el↗
arXiv2026

A Programmable Optics Cloud Laboratory

Laboratory automation can improve experimental throughput, accessibility, and reproducibility, but many robotic laboratory systems remain difficult to reconfigure. This challenge is especially pronounced in free-space optics, where experiments are built from heterogeneous components, require precise alignment, and are frequently rearranged as experimental goals change. In this work, we present the Programmable Infrastructure for Cloud Optics (PICO), a robotic cloud-laboratory architecture designed to make reconfigurable optical experiments easier to program, operate, and reproduce. PICO provides a common domain-specific abstraction and software layer through which experimental configurations and actions can be controlled across different user interfaces. This enables the same physical laboratory to support remote interactive use, scripted experiments, autonomous routines, and features such as version control. We implement PICO on a robotic free-space optics platform and demonstrate it through an experimental case study.

physics.optics↗
arXiv2026

Fast, Accurate, and Scalable Fermionic Neural Networks via Translation Equivariance

We demonstrate that designing a neural quantum state to be an exact eigenstate of the Hamiltonian's symmetries significantly improves both training speed and final variational energy. For the 2D electron gas, we design TorFormer, a neural network wavefunction which is an exact eigenstate of the total momentum. TorFormer describes both the Fermi liquid and Wigner crystal with no supervision and significantly outperforms Psiformer-based references up to large system sizes. For $r_s = 30.0$ and $40.0$ at $N=91$, we compare TorFormer trained for $8\mathrm{K}$ steps against the previous best NQS, which required $100\mathrm{K}$ training steps. Our improvement to the total energy at $r_s = 40.0$, excluding the trivial Madelung part, is $0.12\%$---enormous compared to the tiny differences separating phases. Relative to Slater-Jastrow-backflow diffusion Monte Carlo, TorFormer's energy decrease is roughly $9.8$ times that of the previous best NQS. Our work demonstrates that neural quantum states can both accurately and efficiently solve large-scale problems.

cond-mat.str-el↗
arXiv2026

Nanophotonic control of spatial information in scintillation detectors

X-rays enable non-invasive imaging across medicine, security, materials science, and beyond, yet modern systems remain constrained by the need to resolve finer structures at lower radiation dose. Scintillators are the dominant materials for detecting X-rays, but face a longstanding compromise: thick scintillators absorb X-rays effectively, whereas optical photons generated throughout their volume spread before detection, degrading spatial information. Existing scintillator architectures largely try to preserve resolution by physically confining light using pixels, columnar crystals, or microstructured channels. Here, we show that high-resolution detection does not require the volumetric confinement of scintillation light. A metalens integrated directly with a bulk scintillator uses nanophotonic wavefront control to preferentially transfer high-spatial-frequency information from volumetrically generated scintillation light to the detector, while retaining the X-ray absorption of a thick scintillator. We experimentally recover fine spatial detail in X-ray images of inorganic and biological specimens. In a detector geometry relevant to computed tomography (CT), the experimentally validated model predicts a fivefold reduction in required X-ray dose and a 25-fold increase in resolution bandwidth relative to a state-of-the-art pixelated scintillator. These results establish wavefront engineering as a route to separating efficient X-ray absorption from optical image formation, with the potential for substantially higher-resolution, lower-dose CT.

physics.optics↗
arXiv2026

Can AI Follow In Einstein's Footsteps?

AI is accelerating physics discovery, but perhaps away from Einstein-level theory building. To understand this gap, we must recognize a striking trend: while being very successful, the most visible AI contributions to physics discovery appear to mirror the historical development of physics, but in reverse. Human discovery in physics progressed, in broad strokes, from ancient pattern prediction, through phenomenological laws such as Kepler's, to principle-based universal theories such as relativity and the Standard Model. On the AI side, prominent contributions to physics discovery point in the opposite direction: early milestones emphasized explicit equation-discovery methods, such as symbolic regression, whereas more recent frontier contributions are powerful predictors such as AlphaFold and GraphCast, which can be remarkably accurate yet do not provide clear theoretical understanding. If this trend continues, AI would become extraordinarily good at prediction but may struggle to ever propose its first serious contender to quantum gravity or other paradigm-level theories. We review the current landscape of AI for physics discovery and highlight a critical missing skill: the ability to pose the right questions or invent the right principles to guide the development of new theories and the tests to falsify them. This mode of discovery has driven many of the deepest advances since the 17th century, where symmetry, simplicity, and new mathematical frameworks guided theory construction before experimental tests. Equipping AI systems with such skills could move them from predicting within known frameworks to proposing the next paradigm-level discovery in physics.

physics.hist-ph↗
arXiv2026

Three-dimensional confinement of light in photonic crystals without bandgaps

We demonstrate that confinement of light in three dimensions is possible in photonic crystals without a complete photonic bandgap. Our approach exploits symmetry-protected quadratic degeneracies in the bulk band structure, where the photonic density of states vanishes at an isolated frequency. By introducing a point defect, we create a localized mode whose symmetry representation is incompatible with that of the surrounding bulk modes, suppressing coupling to propagating channels. The combination of vanishing density of states and a symmetry mismatch yields bound defect modes despite the absence of a spectral gap, as confirmed by time- and frequency-domain numerical simulations. This approach highlights the role of engineering the photonic environment around the defect to enable confinement, potentially providing a new route for designing optical cavities in three-dimensional photonic crystals.

physics.optics↗
arXiv2026

Vacuum Fluctuation-Induced State Switching in Degenerate Optical Parametric Oscillators

Bistable driven-dissipative systems near bifurcations can exhibit noise-activated switching between steady states. Here, we investigate how quantum vacuum fluctuations induce such switching in a biased optical parametric oscillator (OPO), a nonlinear system with intrinsic bistability. We show how microscopic quantum fluctuations driving macroscopic transitions can be controlled with an external bias field that reshapes the OPO steady-state metapotential. We derive analytical expressions for the average switching time and validate them through simulations of the OPO field distribution and inter-state probability flow under bias injection. We further examine how switching depends on bias strength, pump gain, and optical nonlinearity. Our findings clarify how quantum noise can shape macroscopic dynamics and provide a foundation for noise-assisted photonic machine learning and probabilistic quantum gates.

quant-ph↗
arXiv2026

CLUSTER: Derivative-free optimization of smooth functions with parameter-change costs

We introduce the CLUSTER algorithm (\textbf{c}oordinate-\textbf{l}evel \textbf{u}pdate \textbf{s}trategy for \textbf{t}rust-region step \textbf{e}valuation \textbf{r}efinement) for local derivative-free optimization problems where there is a cost to changing each parameter (or clusters of parameters). For example, this type of cost model is appropriate for optimizing robot-controlled laboratory experiments, in which a robot may incur a separate motion for each parameter cluster to be adjusted. We build off of a class of quadratic-interpolation optimization algorithms by Powell and Conn that are known to perform well for twice-differentiable objectives (e.g. low-noise experiments), and show that the CLUSTER variants improve performance on a variety of test problems (including an optics laboratory experiment) by around 50$\%$, and greatly outperform common competing algorithms for laboratory optimization (Bayesian optimization and Nelder--Mead). We also adapt the convergence proof of the Conn algorithm to obtain a similar convergence guarantee for CLUSTER-Conn.

math.OC↗
arXiv2026

Continuum Neural Momentum Eigenstate for Variationally Solving Quasiparticles

We design the first neural quantum state for continuum particles that, for any chosen allowed momentum $\mathbf{k}$, is by construction an exact eigenstate of total momentum with eigenvalue $\mathbf{k}$. Our architecture, EVE, enables off-the-shelf VMC to solve for momentum-sector ground states. We test EVE on 2D bosons with mutual $1/r$ interactions, finding that a single unified ansatz is capable of describing four qualitatively different states: superfluid, roton, crystal, and phonon. At different densities, we extract the underlying phase of matter from the dispersion's shape. At $r_s = 20.0$, we see the roton minimum at finite $k$ expected of a superfluid. At $r_s = 100.0$, we see striking zone folding indicative of crystalline order, with periodically spaced minima representing floating crystals connected by phonon arcs in between. Using density-density correlation functions, we confirm the phase diagnoses and probe the excitations' correlation structures. Finally, we analyze the roton's phase texture and find unexpected multi-particle phase strings, formed when several vortex dipoles merge, leaving two vortices connected by a phase slip.

cond-mat.quant-gas↗
arXiv2026

Dosimetric characterization of a nanophotonic scintillator and applications to real-time in-vivo total body irradiation dosimetry

Purpose: Recent advances in metasurface photonics and manufacturing have enabled a nanophotonic surface coating to be applied to conventional scintillators, which has been shown to significantly improve light yield. However, the dosimetric properties of such coatings has not been established. We performed the first dosimetric characterization of a nanophotonic scintillator and explored clinical application to real-time in-vivo total body irradiation (TBI) dosimetry. Methods: A 4.5x1.5 cm cerium-doped yttrium aluminum garnet (YAG:Ce) scintillator, half patterned with the nanophotonic structure, half left unpatterned, enabled direct comparison between conventional and nanophotonic surfaces. The scintillator was placed in a 3D-printed light-tight box with an off-axis CMOS camera and irradiated with a clinical linear accelerator. Absolute dosimetry was done with dose-calibrated radiochromic film. For TBI, the scintillator was positioned on an anthropomorphic phantom in a TBI booth, with signal measured by both CMOS and consumer-grade phone cameras under different room lighting conditions. Results: The nanophotonic scintillator showed a 4.1x increase in signal and a 3.7x increase in contrast-to-noise ratio versus the conventional scintillator. Both scintillators exhibited dose-rate independence and linear dose response, with modest energy dependence. For TBI, the nanophotonic scintillator produced a clearly detectable signal with both CMOS and phone cameras, whereas the conventional scintillator signal was undetectable. Conclusions: Nanophotonic structures significantly enhance light output of conventional scintillators without impacting their dosimetric properties. Nanophotonic scintillators may enable real-time in-vivo TBI dosimetry. Future work should investigate this technology for improved dosimetry equipment and X-ray imaging detectors.

physics.med-ph↗
arXiv2026

End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout

End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formalism characterizing when and why such systems outperform conventional lens-based imaging is largely lacking. This paper focuses on object classification, a central imaging task, and asks when end-to-end optimization of a phase mask for incoherent imaging improves performance over a conventional focusing lens. We find that these gains arise primarily under constrained detector readout and are limited under full detector readout. In the latter setting, we prove that no incoherent phase mask exceeds the ideal-channel mutual information between detector measurements and class labels; a conventional focusing lens approaches this ceiling, and joint optimization yields no empirical gain. When detector readout is constrained -- by coarse spatial sampling or a limited number of measurements -- optimized optics can substantially improve classification by increasing class separability in the detector measurements. These gains are largest under low detector noise and shrink as noise grows, because the optics shape the signal before it reaches the detector but cannot remove noise added afterward. The advantage also depends on the spectral structure of the task: co-design helps most when class-discriminative content is concentrated at lower spatial frequencies than within-class variation. We develop a theoretical framework formalizing these distinctions and test its predictions on synthetic data and standard benchmarks (MNIST, FashionMNIST, SVHN).

cs.CV↗
arXiv2026

TraversalBench: Challenging Paths to Follow for Vision Language Models

Vision-language models (VLMs) perform strongly on multimodal benchmarks, but their ability to follow complex visual paths remains under-tested. We introduce TraversalBench, a controlled benchmark for exact visual path traversal. Each instance contains a continuous polyline with a unique start marker and labeled vertices; models must recover the ordered sequence encountered from start to finish. The benchmark balances self-intersection count, tortuosity, vertex count, and nearby confounding lines while limiting reliance on OCR, world knowledge, or open-ended planning. We find that self-intersections are the dominant source of difficulty. A first-crossing analysis localizes failures to crossing points: performance is stable before the first crossing, then drops sharply when the model must resolve the correct continuation. Nearby confounders have weaker but compounding effects, and an auxiliary reading-order benchmark reveals a consistent left-to-right bias. Together, these results characterize how VLMs perceive and fail on visual paths. Finally, we position TraversalBench as a new contribution to the growing line of sustained and precise visual grounding benchmarks for VLMs. Code, benchmark data, and rendered examples are available at https://github.com/clarapetrova/traversalbench.

cs.CV↗
arXiv2026

Diffractive cascades for polychromatic hard X-ray focusing

Diffractive focusing of hard X-rays has traditionally required structures with large aspect ratios due to the limited interaction of most materials with X-rays. This has increased the complexity of fabricating diffractive X- ray lenses, restricting their widespread deployment. Here, we utilize topology optimization to design diffractive cascades to focus X-rays. When restricting the structures to a maximum aspect ratio of 8, a diffractive cascade can achieve a focusing efficiency of 40%, far exceeding the 3% efficiency of a zone plate with the same aspect ratio. Diffractive cascades also allow the focusing of beams with energies beyond 20 keV and bandwidths exceeding 1%, loosening the restrictions on other system components. We characterize the robustness of these cascades to alignment, fabrication, and heating perturbations, demonstrating the ability of our designs to operate under real-world conditions. Finally, we exploit the flexibility of our framework to include multiple depths in the objective function. This enables a depth of focus exceeding that of a zone plate or a cascade designed using single-plane optimization. This work demonstrates the utility of topology optimization in the X-ray regime and the possibility of advancing X-ray manipulation across a range of tasks.

physics.optics↗
arXiv2026

Non-Hermiticity-induced chirality imbalance of Weyl Landau levels

Weyl semimetals obey a global chirality constraint: the net chiral topological charge and any associated chiral spectral flow must vanish, as required by the Nielsen-Ninomiya theorem. Under magnetic fields, this constraint manifests through counter-propagating zeroth Landau levels associated with Weyl nodes of opposite chirality. Here, we experimentally demonstrate how non-Hermiticity can reshape this balance in a synthetic photonic Weyl semimetal. Using engineered gauge fields in one-dimensional multilayer structures, we realize both homogeneous and axial magnetic fields and directly probe the resulting Landau-level spectra. While a homogeneous field produces the expected chirality-balanced zeroth Landau levels, an axial field spatially separates the compensating chiral channels: co-propagating bulk pseudo-Landau levels carry one chirality, whereas the opposite chirality resides in boundary-localized surface states. We show that radiative boundary loss selectively suppresses these surface states, removing them from the long-lived observable spectrum and producing an experimentally accessible chirality imbalance. By reducing boundary loss, we recover the hidden chiral channel and reveal its surface-state origin. These results show that non-Hermiticity, present naturally in photonics, can control and relax fundamental chirality constraints in topological systems, enabling access to otherwise forbidden spectral responses.

cond-mat.mes-hall↗
arXiv2026

Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution

Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as reversing information loss across scales. We introduce $\textbf{SKILD}$, a $\textbf{S}$cale-invariant $\textbf{K}$-Space $\textbf{I}$mage $\textbf{L}$earning $\textbf{D}$iffusion model that unifies generation and continuous super-resolution within a single unconditional framework. Both natural images and critical physical systems exhibit scale invariance, and we leverage it to design a forward process that attenuates image content from fine to coarse scales while injecting spectrum-matched Gaussian noise, making scale an explicit coordinate of the diffusion dynamics. The same trained reverse process performs generation and continuous super-resolution by varying only the starting timestep: $\textit{no task-specific architecture, no conditioning branch, no classifier-free guidance, no retraining per scale factor}$. Empirically, SKILD reaches FID $2.65$ and Inception Score $9.63$ on unconditional CIFAR-10, performs $2\times$--$8\times$ super-resolution on ImageNet from a single unconditional checkpoint while outperforming conditional models across perceptual metrics, and reconstructs critical Ising models whose connected four-point correlations closely track the ground truth.

cs.CV↗