Search

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Modern processors implement a decoupled front-end in the form of Fetch Directed Instruction Prefetching (FDIP) to avoid front-end stalls. FDIP is driven by the Branch Prediction Unit (BPU), relying on the BPU's accuracy and branch target tracking structures to speculatively fetch instructions into the Instruction Cache (L1I). As data center applications become more complex, their code footprints also grow, resulting in an increase in Branch Target Buffer (BTB) misses. FDIP can alleviate L1I cache misses, but when it encounters a BTB miss, the BPU may not identify the current instruction as a branch to FDIP. This can prevent FDIP from prefetching or cause it to speculate down the wrong path, further polluting the L1I cache. We observe that the vast majority, 75%, of BTB-missing, unidentified branches are actually present in instruction cache lines that FDIP has previously fetched but, these missing branches have not yet been decoded and inserted into the BTB. This is because the instruction line is decoded from an entry point (which is the target of the previous taken branch) till an exit point (the taken branch). Branch instructions present in the ignored portion of the cache line we call them "Shadow Branches". Here we present Skeia, a novel shadow branch decoding technique that identifies and decodes unused bytes in cache lines fetched by FDIP, inserting them into a Shadow Branch Buffer (SBB). The SBB is accessed in parallel with the BTB, allowing FDIP to speculate despite a BTB miss. With a minimal storage state of 12.25KB, Skeia delivers a geomean speedup of ~5.7% over an 8K-entry BTB (78KB) and ~2% versus adding an equal amount of state to the BTB across 16 front-end bound applications. Since many branches stored in the SBB are unique compared to those in a similarly sized BTB, we consistently observe greater performance gains with Skeia across all examined sizes until saturation., Comment: 13 pages, 16 figures, Submitted to ASPLOS 2025

Modern OOO CPUs have very deep pipelines with large branch misprediction recovery penalties. Speculatively executed instructions on the wrong path can significantly change cache state, depending on speculation levels. Architects often employ trace-driven simulation models in the design exploration stage, which sacrifice precision for speed. Trace-driven simulators are orders of magnitude faster than execution-driven models, reducing the often hundreds of thousands of simulation hours needed to explore new micro-architectural ideas. Despite this strong benefit of trace-driven simulation, these often fail to adequately model the consequences of wrong path because obtaining them is nontrivial. Prior works consider either a positive or negative impact of wrong path but not both. Here, we examine wrong path execution in simulation results and design a set of infrastructure for enabling wrong-path execution in a trace driven simulator. Our analysis shows the wrong path affects structures on both the instruction and data sides extensively, resulting in performance variations ranging from $-3.05$\% to $20.9$\% when ignoring wrong path. To benefit the research community and enhance the accuracy of simulators, we opened our traces and tracing utility in the hopes that industry can provide wrong-path traces generated by their internal simulators, enabling academic simulation without exposing industry IP., Comment: 5 pages, 7 Figures, Submited to Computer Architecture Letters

Generative AI holds the promise of enabling a range of sought-after capabilities and revolutionizing workflows in various consumer and enterprise verticals. However, putting a model in production involves much more than just generating an output. It involves ensuring the model is reliable, safe, performant and also adheres to the policy of operation in a particular domain. Guardrails as a necessity for models has evolved around the need to enforce appropriate behavior of models, especially when they are in production. In this paper, we use education as a use case, given its stringent requirements of the appropriateness of content in the domain, to demonstrate how a guardrail model can be trained and deployed in production. Specifically, we describe our experience in building a production-grade guardrail model for a K-12 educational platform. We begin by formulating the requirements for deployment to this sensitive domain. We then describe the training and benchmarking of our domain-specific guardrail model, which outperforms competing open- and closed- instruction-tuned models of similar and larger size, on proprietary education-related benchmarks and public benchmarks related to general aspects of safety. Finally, we detail the choices we made on architecture and the optimizations for deploying this service in production; these range across the stack from the hardware infrastructure to the serving layer to language model inference optimizations. We hope this paper will be instructive to other practitioners looking to create production-grade domain-specific services based on generative AI and large language models.

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Geant4 is an object-oriented toolkit for the simulation of the passage of particles through matter. Its development was initially motivated by the requirements of physics experiments at high energy hadron colliders under construction in the last decade of the 20th century. Since its release in 1998, it has been exploited in many different applicative fields, including space science, nuclear physics, medical physics and archaeology. Its valuable support to scientific discovery is demonstrated by more than 16000 citations received in the past 25 years, including notable citations for main discoveries in different fields. This accomplishment shows that well designed software plays a key role in enabling scientific advancement. In this paper we discuss the key principles and the innovative decisions at the basis of Geant4, which made it a game changer in high energy physics and related fields, and outline some considerations regarding future directions., Comment: Accepted for publication in Future Generation Computer Systems

This paper summarizes Geant4 contribution to scientific research over the past 25 years through a scientometric analysis of the results with which it has been associated. The scientometric data collected from scholarly literature and databases are evaluated with methods pertaining to econometrics and ecology to quantify relevant traits, diversity and disparity in their scientific and geographic distributions, and to identify statistically significant trends. The analysis reviews the contribution of Geant4 to the field - experimental particle physics - that originally motivated its development and highlights its role in other research domains including nuclear physics and engineering, astrophysics and space science, biomedical physics, archaeology and the cultural heritage., Comment: Presented at the 16th Topical Seminar on Innovative Particle and Radiation Detectors (IPRD23), Siena, Italy

Stacking monolayer semiconductors results in moir\'e patterns that host many correlated and topological electronic phenomena, but measurements of the basic electronic structure underpinning these phenomena are scarce. Here, we investigate the properties of the conduction band in moir\'e heterobilayers using submicron angle-resolved photoemission spectroscopy with electrostatic gating, focusing on the example of WS2/WSe2. We find that at all twist angles the conduction band edge is the K-point valley of the WS2, with a band gap of 1.58 +- 0.03 eV. By resolving the conduction band dispersion, we observe an unexpectedly small effective mass of 0.15 +- 0.02 m_e. In addition, we observe replicas of the conduction band displaced by reciprocal lattice vectors of the moir\'e superlattice. We present arguments and evidence that the replicas are due to modification of the conduction band states by the moir\'e potential rather than to final-state diffraction. Interestingly, the replicas display an intensity pattern with reduced, 3-fold symmetry, which we show implicates the pseudo vector potential associated with in-plane strain in moir\'e band formation., Comment: Main text: 12 pages, 4 figures. Appended Supporting Information: 10 pages, 11 figures

In active materials, uncoordinated internal stresses lead to emergent long-range flows. An understanding of how the behavior of active materials depends on mesoscopic (hydrodynamic) parameters is developing, but there remains a gap in knowledge concerning how hydrodynamic parameters depend on the properties of microscopic elements. In this work, we combine experiments and multiscale modeling to relate the structure and dynamics of active nematics composed of biopolymer filaments and molecular motors to their microscopic properties, in particular motor processivity, speed, and valency. We show that crosslinking of filaments by both motors and passive crosslinkers not only augments the contributions to nematic elasticity from excluded volume effects but dominates them. By altering motor kinetics we show that a competition between motor speed and crosslinking results in a nonmonotonic dependence of nematic flow on motor speed. By modulating passive filament crosslinking we show that energy transfer into nematic flow is in large part dictated by crosslinking. Thus motor proteins both generate activity and contribute to nematic elasticity. Our results provide new insights for rationally engineering active materials.

Layer-by-layer assembly of van der Waals (vdW) heterostructures underpins new discoveries in solid state physics, material science and chemistry. Despite the successes, all current 2D material (2DM) transfer techniques rely on the use of polymers which limit the cleanliness, ultimate electronic performance, and potential for optoelectronic applications of the heterostructures. In this article, we present a novel polymer-free platform for rapid and facile heterostructure assembly which utilises re-usable flexible silicon nitride membranes. We demonstrate that this allows fast and reproducible production of 2D heterostructures using both exfoliated and CVD-grown materials with perfect interfaces free from interlayer contamination and correspondingly excellent electronic behaviour, limited only by the size and intrinsic quality of the crystals used. Furthermore, removing the need for polymeric carriers allows new possibilities for vdW heterostructure fabrication: assembly at high temperatures up to 600{\deg}C, and in different environments including ultra-high vacuum (UHV) and when the materials are fully submerged in liquids. We demonstrate UHV heterostructure assembly for the first time, and show the reliable creation of graphene moir\'e superlattices with more than an order of magnitude improvement in their structural homogeneity. We believe that broad adaptation of our novel inorganic 2D materials assembly strategy will allow realisation of the full potential of vdW heterostructures as a platform for new physics and advanced optoelectronic technologies., Comment: 23 pages, 4 figures

A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dependent control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by $\sim3.7\times$ compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.

Measurement is an essential component of quantum algorithms, and for superconducting qubits it is often the most error prone. Here, we demonstrate model-based readout optimization achieving low measurement errors while avoiding detrimental side-effects. For simultaneous and mid-circuit measurements across 17 qubits, we observe 1.5% error per qubit with a 500ns end-to-end duration and minimal excess reset error from residual resonator photons. We also suppress measurement-induced state transitions achieving a leakage rate limited by natural heating. This technique can scale to hundreds of qubits and be used to enhance the performance of error-correcting codes and near-term applications.

Switching, routing, and security functions are the backbone of packet processing networks. Fast and efficient processing of packets requires maintaining the state of a large number of transient network connections. In particular, modern stateful firewalls, security monitoring devices, and software-defined networking (SDN) programmable dataplanes require maintaining stateful flow tables. These flow tables often grow much larger than can be expected to fit within on-chip memory, requiring a managed caching layer to maintain performance. This paper focuses on improving the efficiency of caching, an important architectural component of the packet processing data planes. We present a novel predictive approach to network flow table cache management. Our approach leverages a Hashed Perceptron binary classifier as well as an iterative approach to feature selection and ranking to improve the reliability and performance of the data plane caches. We validate the efficiency of the proposed techniques through extensive experimentation using real-world data sets. Our numerical results demonstrate that our techniques improve the reliability and performance of flow-centric packet processing architectures., Comment: 26 pages, 22 figures

AI Advancements have augmented casual writing and story generation, but their usage poses challenges in collaborative storytelling. In role-playing games such as Dungeons & Dragons (D&D), composing prompts using generative AI requires a technical understanding to generate ideal results, which is difficult for novices. Thus, emergent narratives organically developed based on player actions and decisions have yet to be fully utilized. This paper envisions the use of generative AI in transforming storytelling into an interactive drama using dynamic and immersive narratives. First, we describe scenarios where narratives are created and character conversations are designed within an overarching fantasy disposition. Then, we recommend design guidelines to help create tools using generative AI in interactive storytelling. Lastly, we raise questions on its potential impact on player immersion and cognitive load. Our contributions may be expanded within the broader interactive storytelling domain, such as speech-conversational AI and persona-driven chatbots., Comment: 5 pages, 2 figures

Inorganic solid-state battery electrolytes show high ionic conductivities and enable the fabrication of all solid-state batteries. In this work, we present the temperature dependence of spin-lattice relaxation time (T1), spin-spin relaxation time (T2), and resonance linewidth of the 7Li nuclear magnetic resonance (NMR) for four solid-state battery electrolytes (Li3InCl6 (LIC), Li3YCl6 (LYC), Li1.48Al0.48Ge1.52(PO4)3 (LAGP) and LiPS5Cl (LPSC)) from 173 K to 403 K at a 7Li resonance frequency of 233 MHz, and from 253 K to 353 K at a 7Li resonance frequency of 291 MHz. Additionally, we measured the spin-lattice relaxation rates at an effective 7Li resonance frequency of 133 kHz using a spin-locking pulse sequence in the temperature range of 253 K to 353 K. In LPSC, the 7Li NMR relaxation is consistent with the Bloembergen-Pound-Purcell (BPP) theory of NMR relaxation of dipolar nuclei. In LIC, LYC and LAGP, the BPP theory does not describe the NMR relaxation rates for the temperature range and frequencies of our measurements. The presented NMR relaxation data assists in providing a complete picture of Li diffusion in the four solid-state battery electrolytes.

Measurement has a special role in quantum theory: by collapsing the wavefunction it can enable phenomena such as teleportation and thereby alter the "arrow of time" that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time that go beyond established paradigms for characterizing phases, either in or out of equilibrium. On present-day NISQ processors, the experimental realization of this physics is challenging due to noise, hardware limitations, and the stochastic nature of quantum measurement. Here we address each of these experimental challenges and investigate measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping, to avoid mid-circuit measurement and access different manifestations of the underlying phases -- from entanglement scaling to measurement-induced teleportation -- in a unified way. We obtain finite-size signatures of a phase transition with a decoding protocol that correlates the experimental measurement record with classical simulation data. The phases display sharply different sensitivity to noise, which we exploit to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realize measurement-induced physics at scales that are at the limits of current NISQ processors.

The coupled transport of charge and heat provide fundamental insights into the microscopic thermodynamics and kinetics of materials. We describe a sensitive ac differential resistance bridge that enables measurements of the temperature difference on two sides of a coin cell with a resolution of better than 10 uK. We use this temperature difference metrology to determine the ionic Peltier coefficients of symmetric Li-ion electrochemical cells as a function of Li salt concentration, solvent composition, electrode material, and temperature. The Peltier coefficients {\Pi} are negative, i.e., heat flows in the direction opposite to the drift of Li ions in the applied electric field, large, 30 kJ mol-1, and increase with increasing temperature at T > 300 K. The Peltier coefficient is approximately constant on time scales that span the characteristic time for mass diffusion across the thickness of the electrolyte, suggesting that heat of transport plays a minor role in comparison to the changes in partial molar entropy of Li at the interface between the electrode and electrolyte. Our work demonstrates a new platform for studying the non-equilibrium thermodynamics of electrochemical cells and provides a window into the transport properties of electrochemical materials through measurements of temperature differences and heat currents that complement traditional measurements of voltages and charge currents.

Leakage of quantum information out of computational states into higher energy states represents a major challenge in the pursuit of quantum error correction (QEC). In a QEC circuit, leakage builds over time and spreads through multi-qubit interactions. This leads to correlated errors that degrade the exponential suppression of logical error with scale, challenging the feasibility of QEC as a path towards fault-tolerant quantum computation. Here, we demonstrate the execution of a distance-3 surface code and distance-21 bit-flip code on a Sycamore quantum processor where leakage is removed from all qubits in each cycle. This shortens the lifetime of leakage and curtails its ability to spread and induce correlated errors. We report a ten-fold reduction in steady-state leakage population on the data qubits encoding the logical state and an average leakage population of less than $1 \times 10^{-3}$ throughout the entire device. The leakage removal process itself efficiently returns leakage population back to the computational basis, and adding it to a code circuit prevents leakage from inducing correlated error across cycles, restoring a fundamental assumption of QEC. With this demonstration that leakage can be contained, we resolve a key challenge for practical QEC at scale., Comment: Main text: 7 pages, 5 figures

Recent years have seen a dramatic increase in the microarchitectural complexity of processors. This increase in complexity presents a twofold challenge for the field of computer architecture. First, no individual architect can fully comprehend the complexity of the entire microarchitecture of the core. This leads to increasingly specialized architects, who treat parts of the core outside their particular expertise as black boxes. Second, with increasing complexity, the field becomes decreasingly accessible to new students of the field. When learning core microarchitecture, new students must first learn the big picture of how the system works in order to understand how the pieces all fit together. The tools used to study microarchitecture experience a similar struggle. As with the microarchitectures they simulate, an increase in complexity reduces accessibility to new users. In this work, we present ChampSim. ChampSim uses a modular design and configurable structure to achieve a low barrier to entry into the field of microarchitecural simulation. ChampSim has shown itself to be useful in multiple areas of research, competition, and education. In this way, we seek to promote access and inclusion despite the increasing complexity of the field of computer architecture.

Indistinguishability of particles is a fundamental principle of quantum mechanics. For all elementary and quasiparticles observed to date - including fermions, bosons, and Abelian anyons - this principle guarantees that the braiding of identical particles leaves the system unchanged. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well developed mathematical description of non-Abelian anyons and numerous theoretical proposals, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. While efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasi-particles, superconducting quantum processors allow for directly manipulating the many-body wavefunction via unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons, we implement a generalized stabilizer code and unitary protocol to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of employing the anyons for quantum computation and utilize braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and - through the future inclusion of error correction to achieve topological protection - could open a path toward fault-tolerant quantum computing.