arXiv subjects

Gavin S. Hartnett

Publications and source records attributed to Gavin S. Hartnett.

At least 19 recordsLinked to original sources

arXiv2026

Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor

We report experimental digital quantum simulation of the one-dimensional Fermi-Hubbard model on a superconducting quantum processor at a scale beyond the reach of exact statevector simulation and challenging for state-of-the-art tensor-network methods. We encode this problem using up to 120 qubits through an efficient mapping that reduces circuit complexity, and we improve accuracy through error suppression to simulate dynamical evolution using up to 90 Trotter steps. From a vacancy defect introduced in the middle of an $L=31$-site (62-qubit) Néel initial state, we directly observe spin-charge separation to $t=9$ in natural units and quantitatively extract velocities $v_c$, $v_s$, which match classical simulations across model parameters, and analytics in appropriate regimes. We then extend experiments to $L=60$ fermions (120 qubits) and evolution times to $t=6$ using 30 Trotter steps. Quantum-processor outputs agree quantitatively with approximate classical simulations performed using a time-dependent variational principle (TDVP) solver and we observe that increasing the TDVP bond dimension through $χ= 4096$ expands the range of evolution times within which agreement has RMSE $\sim 1\%$ before the approaches diverge. Owing to the large scale of the simulation and the use of overhead-free error-suppression techniques, for simulated evolution times at the limit of quantum/classical agreement ($t\gtrsim 5$ in natural hopping units), the wall-clock runtime of the quantum processor is nearly three orders of magnitude faster than TDVP simulations with $χ= 4096$ and implemented using the ITensor and TeNPy packages. These results establish contemporary digital quantum processors as a versatile, quantitatively accurate, and competitive platform for the study of fermionic many-body dynamics in regimes where leading classical methods can become prohibitively expensive.

quant-ph↗

arXiv2026

Simulating the dynamics of an SU(2) matrix model on a trapped-ion quantum computer

Matrix models are an important class of systems in string theory and theoretical physics, with applications to random matrix theory, quantum chaos, and black holes. Hamiltonian Monte Carlo simulations and gauge/gravity duality have been used to study these systems at thermal equilibrium, and the bootstrap program has been used to efficiently determine operator expectation values by imposing positivity constraints. However, simulating real-time, non-equilibrium dynamics remains a fundamental challenge. In this work, we present the first digital quantum simulation of a bosonic matrix model, executed on the Quantinuum System Model H2 trapped-ion quantum computer. We focus on an $\mathrm{SU}(2)$ gauge theory with a quartic potential as it is simple enough to validate against exact classical solutions and yet complex enough to reflect the non-local structure of larger theories. Using the Loschmidt echo as our primary dynamical observable, we systematically decompose simulation errors into three distinct sources: Hilbert space truncation, Trotterization, and hardware noise. We demonstrate a new post-selection scheme that detects and discards gauge-symmetry violations in the Fock basis and show that at small scales it, along with zero-noise extrapolation, can give modest improvements in fidelity. These approaches struggle to scale to larger system sizes in their current implementations, emphasizing the need to move beyond them and to focus on depth reduction through improved compilation and unitary synthesis, and run-time error handling such as additional error suppression, error detection, as well as error correction approaches. This work establishes a foundation for extending digital quantum simulation to more complex matrix models -- revealing that fundamental challenges in qubit resources and circuit depth remain formidable obstacles for scaling to holographically interesting regimes.

quant-ph↗

arXiv2026

Automated discovery of heralded ballistic graph state generators for fusion-based photonic quantum computation

Designing photonic circuits that prepare graph states with high fidelity and success probability is a central challenge in linear optical quantum computing. Existing approaches rely on hand-crafted designs or fusion-based assemblies. In the absence of multiplexing/boosting, both post-selected ballistic circuits and sequential fusion exhibit exponentially decreasing single-shot yields - a fundamental limitation that makes optimizing individual resource state generators particularly important, as these serve as building blocks in larger FBQC architectures. We present a general-purpose optimization framework for automated photonic circuit discovery using a novel polynomial-based simulation approach, enabling efficient strong simulation and gradient-based optimization. Our framework employs a two-pass optimization procedure: the first pass identifies a unitary transformation that prepares the desired state with perfect fidelity and maximal success probability, and the second pass implements a novel sparsification algorithm that reduces this solution to a compact photonic circuit with minimal beamsplitter count while preserving performance. This sparsification procedure often reveals underlying mathematical structure, producing highly simplified circuits with rational reflection coefficients. We demonstrate our approach by discovering optimized circuits for $3$-, $4$-, and $5$-qubit graph states across multiple equivalence classes. For 4-qubit states, our circuits achieve success probabilities of $2.053 \times 10^{-3}$ to $7.813 \times 10^{-3}$, outperforming the fusion baseline by up to $4.7 \times$. For 5-qubit states, we achieve $5.926 \times 10^{-5}$ to $1.157 \times 10^{-3}$, demonstrating up to $7.5 \times$ improvement. These results include the first known state preparation circuits for certain 5-qubit graph states.

quant-ph↗

arXiv2026

Integrated error-suppressed pipeline for quantum optimization of nontrivial binary combinatorial optimization problems on gate-model hardware at the 156-qubit scale

We introduce a novel hybrid quantum-classical variational optimization method for unconstrained binary combinatorial optimization problems on gate-model quantum computers, integrating a custom variational ansatz, staged feedback-based dual variational parameter update strategies, efficient parametric compilation, automated error suppression during hardware execution, and scalable O($n$) classical post-processing to correct for bitflip errors. Without this integrated approach, we show that standard circuit execution at scale produces output indistinguishable from random sampling, establishing the necessity of each pipeline component. We benchmark the method on IBM superconducting quantum computers for classically nontrivial optimization problems, where the optimization is conducted on hardware with no use of classical simulation or prior knowledge of the solution. For Max-Cut on random regular graphs with topologies not matched to device connectivity, the method achieves approximation ratios of 100% for unweighted 3-regular graphs up to 156 nodes, weighted regular graphs up to 80 nodes, and weighted 7-regular graphs up 50 nodes. Applied to higher-order binary optimization, the method finds the ground state energy of 127- and 156-qubit spin-glass models matched to device topology with linear, quadratic, and cubic interaction terms, achieving approximation ratios of at least 99.5% across all instances tested. The method consistently outperforms a classical local solver across all problems. Where published results on identical problem instances are available, our method demonstrates competitive or superior performance. These results demonstrate that an appropriately engineered approach enables gate-model quantum computers to produce high-quality solutions for nontrivial binary optimization problems at the 156 qubit scale, where naive implementations are insufficient for good performance.

quant-ph↗

arXiv2025

Achieving computational gains with quantum error-correction primitives: Generation of long-range entanglement enhanced by error detection

The resource overhead required to achieve net computational benefits from quantum error correction (QEC) limits its utility while current systems remain constrained in size, despite exceptional progress in experimental demonstrations. In this paper, we demonstrate that the strategic application of QEC primitives without logical encoding can yield significant advantages on superconducting processors--relative to any alternative error-reduction strategy--while only requiring a modest overhead. We first present a novel protocol for implementing long-range CNOT gates that relies on a unitarily prepared Greenberger-Horne-Zeilinger (GHZ) state as well as a unitary disentangling step; the protocol natively introduces an error-detection process using the disentangled qubits as flags. We demonstrate that it achieves state-of-the-art gate fidelities of over 85% across up to 40 lattice sites, significantly and consistently outperforming the best alternative measurement-based protocol without introducing any additional ancilla qubits. We then apply sparse stabilizer measurements to generate large GHZ states by detecting bit-flip and amplitude-damping errors. Employing this technique in combination with deterministic error suppression, we generate a 75-qubit GHZ state exhibiting genuine multipartite entanglement, the largest reported to date. The generation requires no more than 9 ancilla qubits and the fraction of samples discarded due to errors grows no higher than 78%, far lower than previous discard fractions required for tests using comparable numbers of fully encoded qubits. This work in total represents compelling evidence that adopting QEC primitives on current-generation devices can deliver substantial net benefits.

quant-ph↗

arXiv2024

Learning to rank quantum circuits for hardware-optimized performance enhancement

We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware. We apply our method to the problem of layout selection, in which abstracted qubits are assigned to physical qubits on a given device. Circuit measurements performed on IBM hardware indicate that the maximum and median fidelities of logically equivalent layouts can differ by an order of magnitude. We introduce a circuit score used for ranking that is parameterized in terms of a physics-based, phenomenological error model whose parameters are fit by training a ranking-loss function over a measured dataset. The dataset consists of quantum circuits exhibiting a diversity of structures and executed on IBM hardware, allowing the model to incorporate the contextual nature of real device noise and errors without the need to perform an exponentially costly tomographic protocol. We perform model training and execution on the 16-qubit ibmq_guadalupe device and compare our method to two common approaches: random layout selection and a publicly available baseline called Mapomatic. Our model consistently outperforms both approaches, predicting layouts that exhibit lower noise and higher performance. In particular, we find that our best model leads to a $1.8\times$ reduction in selection error when compared to the baseline approach and a $3.2\times$ reduction when compared to random selection. Beyond delivering a new form of predictive quantum characterization, verification, and validation, our results reveal the specific way in which context-dependent and coherent gate errors appear to dominate the divergence from performance estimates extrapolated from simple proxy measures.

quant-ph↗

arXiv2024

Resource-efficient context-aware dynamical decoupling embedding for arbitrary large-scale quantum algorithms

We introduce and implement GraphDD: an efficient method for real-time, circuit-specific, optimal embedding of dynamical decoupling (DD) into executable quantum algorithms. We demonstrate that for an arbitrary quantum circuit, GraphDD exactly refocuses both quasi-static single-qubit dephasing and crosstalk idling errors over the entire circuit, while using a minimal number of additional single-qubit gates embedded into idle periods. The method relies on a graph representation of the embedding problem, where the optimal decoupling sequence can be efficiently calculated using an algebraic computation that scales linearly with the number of idles. This allows optimal DD to be embedded during circuit compilation, without any calibration overhead, additional circuit execution, or numerical optimization. The method is generic and applicable to any arbitrary circuit; in compiler runtime the specific pulse-sequence solutions are tailored to the individual circuit, and consider a range of contextual information on circuit structure and device connectivity. We verify the ability of GraphDD to deliver enhanced circuit-level error suppression on 127-qubit IBM devices, showing that the optimal circuit-specific DD embedding resulting from GraphDD provides orders of magnitude improvements to measured circuit fidelities compared with standard embedding approaches available in Qiskit.

quant-ph↗

arXiv2024

The Karzas-Latter-Seiler Model of a High-Altitude Electromagnetic Pulse: A New Numerical Code for an Old Model

A high-altitude nuclear blast can produce an electromagnetic pulse (EMP) capable of disrupting electronics on Earth. The basic phenomenology of the initial (E1) phase of the EMP was initially worked out in the 1960s by Longmire, Karzas, and Latter, and although more accurate and sophisticated EMP models have since been devised, the Karzas-Latter model is particularly simple and amenable to implementation as a numerical code. This paper accompanies the release of a new software implementation of an approximation of the Karzas-Latter model due to Seiler. This is, as far as we are aware, the only such publicly available numerical EMP code. After reviewing the physics and assumptions of the model, the numerical results for EMP simulations under a range of conditions are presented. It is shown how the results from multiple line of sight integrations of the field equations can be assembled to form a map of the EMP intensity across a broad geographic region. The model predictions are at least qualitatively correct and in general agreement with other simulation results, including the characteristic "smile" pattern in the spatial variation of the EMP intensity.

physics.plasm-ph↗

arXiv2023

A Rubik's Cube inspired approach to Clifford synthesis

The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis. Drawing inspiration from similarities between this and the famous Rubik's Cube problem, we develop a machine learning approach for Clifford synthesis based on learning an approximation to the distance to the identity. This approach is probabilistic and computationally intensive. However, when a decomposition is successfully found, it often involves fewer gates than existing synthesis algorithms. Additionally, our approach is much more flexible than existing algorithms in that arbitrary gate sets, device topologies, and gate fidelities may incorporated, thus allowing for the approach to be tailored to a specific device.

quant-ph↗

arXiv2022

Empirical Evaluation of Physical Adversarial Patch Attacks Against Overhead Object Detection Models

Adversarial patches are images designed to fool otherwise well-performing neural network-based computer vision models. Although these attacks were initially conceived of and studied digitally, in that the raw pixel values of the image were perturbed, recent work has demonstrated that these attacks can successfully transfer to the physical world. This can be accomplished by printing out the patch and adding it into scenes of newly captured images or video footage. In this work we further test the efficacy of adversarial patch attacks in the physical world under more challenging conditions. We consider object detection models trained on overhead imagery acquired through aerial or satellite cameras, and we test physical adversarial patches inserted into scenes of a desert environment. Our main finding is that it is far more difficult to successfully implement the adversarial patch attacks under these conditions than in the previously considered conditions. This has important implications for AI safety as the real-world threat posed by adversarial examples may be overstated.

cs.CV↗

arXiv2021

Modeling the Impact of Social Distancing and Targeted Vaccination on the Spread of COVID-19 through a Real City-Scale Contact Network

We use mobile device data to construct empirical interpersonal physical contact networks in the city of Portland, Oregon, both before and after social distancing measures were enacted during the COVID-19 pandemic. These networks reveal how social distancing measures and the public's reaction to the incipient pandemic affected the connectivity patterns within the city. We find that as the pandemic developed there was a substantial decrease in the number of individuals with many contacts. We further study the impact of these different network topologies on the spread of COVID-19 by simulating an SEIR epidemic model over these networks, and find that the reduced connectivity greatly suppressed the epidemic. We then investigate how the epidemic responds when part of the population is vaccinated, and we compare two vaccination distribution strategies, both with and without social distancing. Our main result is that the heavy-tailed degree distribution of the contact networks causes a targeted vaccination strategy that prioritizes high-contact individuals to reduce the number of cases far more effectively than a strategy that vaccinates individuals at random. Combining both targeted vaccination and social distancing leads to the greatest reduction in cases, and we also find that the marginal benefit of a targeted strategy as compared to a random strategy exceeds the marginal benefit of social distancing for reducing the number of cases. These results have important implications for ongoing vaccine distribution efforts worldwide.

physics.soc-ph↗

arXiv2020

Deep Generative Modeling in Network Science with Applications to Public Policy Research

Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness both promises to be quite useful for policy research, while at the same time posing a challenge for the useful extraction of information from these datasets - a challenge which calls for new data analysis methods. In this report, we formulate a research agenda of key methodological problems whose solutions would enable new advances across many areas of policy research. We then review recent advances in applying deep learning to network data, and show how these methods may be used to address many of the methodological problems we identified. We particularly emphasize deep generative methods, which can be used to generate realistic synthetic networks useful for microsimulation and agent-based models capable of informing key public policy questions. We extend these recent advances by developing a new generative framework which applies to large social contact networks commonly used in epidemiological modeling. For context, we also compare and contrast these recent neural network-based approaches with the more traditional Exponential Random Graph Models. Lastly, we discuss some open problems where more progress is needed.

cs.LG↗

arXiv2020

Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows

Spin-glasses are universal models that can capture complex behavior of many-body systems at the interface of statistical physics and computer science including discrete optimization, inference in graphical models, and automated reasoning. Computing the underlying structure and dynamics of such complex systems is extremely difficult due to the combinatorial explosion of their state space. Here, we develop deep generative continuous spin-glass distributions with normalizing flows to model correlations in generic discrete problems. We use a self-supervised learning paradigm by automatically generating the data from the spin-glass itself. We demonstrate that key physical and computational properties of the spin-glass phase can be successfully learned, including multi-modal steady-state distributions and topological structures among metastable states. Remarkably, we observe that the learning itself corresponds to a spin-glass phase transition within the layers of the trained normalizing flows. The inverse normalizing flows learns to perform reversible multi-scale coarse-graining operations which are very different from the typical irreversible renormalization group techniques.

cs.LG↗

arXiv2020

A Probability Density Theory for Spin-Glass Systems

Spin-glass systems are universal models for representing many-body phenomena in statistical physics and computer science. High quality solutions of NP-hard combinatorial optimization problems can be encoded into low energy states of spin-glass systems. In general, evaluating the relevant physical and computational properties of such models is difficult due to critical slowing down near a phase transition. Ideally, one could use recent advances in deep learning for characterizing the low-energy properties of these complex systems. Unfortunately, many of the most promising machine learning approaches are only valid for distributions over continuous variables and thus cannot be directly applied to discrete spin-glass models. To this end, we develop a continuous probability density theory for spin-glass systems with arbitrary dimensions, interactions, and local fields. We show how our formulation geometrically encodes key physical and computational properties of the spin-glass in an instance-wise fashion without the need for quenched disorder averaging. We show that our approach is beyond the mean-field theory and identify a transition from a convex to non-convex energy landscape as the temperature is lowered past a critical temperature. We apply our formalism to a number of spin-glass models including the Sherrington-Kirkpatrick (SK) model, spins on random Erdős-Rényi graphs, and random restricted Boltzmann machines.

cond-mat.dis-nn↗

arXiv2019

Covariant Noether charges for type IIB and 11-dimensional supergravities

The covariant Noether charge formalism (also known as the covariant phase method) of Wald and collaborators, including its cohomological extension, is a manifestly covariant Hamiltonian formalism that, in principle, allows one to define and compute the energy, angular momenta, and chemical potentials of generic solutions of gravitational theories. However, it has been observed that for some supergravity solutions the variation of the Noether charge is not (at least manifestably) integrable, and as a result it is unclear whether there are well-defined thermodynamic charges for these solutions. In this work, we derive an expression for the variation of the covariant Noether charges for any solution of Type IIB 10-dimensional supergravity or 11-dimensional supergravity. Although this Noether quantity is not integrable in general, we show that for asymptotically scale-invariant solutions, it is. In particular, the asymptotic scale-invariance allows one to define an energy density and conjugate chemical potentials which obey a first law of thermodynamics and a Smarr relation. These two thermodynamic relations can then be shown to imply that the variation of the Noether charge is integrable, and accordingly the energy and other thermodynamic charges may be defined unambiguously. We explicitly demonstrate and illustrate our claim by computing the thermodynamic charges of two non-trivial supergravity solutions that were recently constructed: 1) the Polchinski-Strassler black brane that is dual to the deconfined phase of $\mathcal{N}=1^*$ theory, and 2) the CGLP black brane that asymptotes to the mass deformed Cvetič-Gibbons-Lü-Pope (CGLP) solution.

hep-th↗

arXiv2019

Adversarial Examples for Cost-Sensitive Classifiers

Motivated by safety-critical classification problems, we investigate adversarial attacks against cost-sensitive classifiers. We use current state-of-the-art adversarially-resistant neural network classifiers [1] as the underlying models. Cost-sensitive predictions are then achieved via a final processing step in the feed-forward evaluation of the network. We evaluate the effectiveness of cost-sensitive classifiers against a variety of attacks and we introduce a new cost-sensitive attack which performs better than targeted attacks in some cases. We also explored the measures a defender can take in order to limit their vulnerability to these attacks. This attacker/defender scenario is naturally framed as a two-player zero-sum finite game which we analyze using game theory.

stat.ML↗

arXiv2018

Holographic dual of hot Polchinski-Strassler quark-gluon plasma

We construct the supergravity dual of the hot quark-gluon plasma in the mass-deformed ${\cal N}=4$ Super-Yang-Mills theory (also known as ${\cal N}=1^*$). The full ten-dimensional type IIB holographic dual is described by 20 functions of two variables, which we determine numerically, and it contains a black hole with $S^5$ horizon topology. As we lower the temperature to around half of the mass of the chiral multiplets, we find evidence for (most likely a first-order) phase transition, which could lead either to one of the Polchinski-Strassler confining, screening, or oblique vacua with polarized branes, or to an intermediate phase corresponding to blackened polarized branes with an $S^2 \times S^3$ horizon topology. This phase transition is a feature that could in principle be seen by putting the theory on the lattice, and thus our result for the ratio of the chiral multiplet mass to the phase transition temperature, $m_c/T = 2.15467491205(6)$, constitutes the first prediction of string theory and AdS/CFT that could be independently checked via four-dimensional super-QCD lattice computation. We also construct the black-hole solution in certain five-dimensional gauged supergravity truncations and, without directly using uplift/reduction formulae, we find strong evidence that the five- and ten-dimensional solutions are the same. This indicates that five-dimensional gauged supergravity is powerful enough to capture the physics of the high-temperature deconfined phase of the Polchinski-Strassler quark-gluon plasma.

hep-th↗

arXiv2018

Constraining the mass of dark photons and axion-like particles through black-hole superradiance

Ultralight bosons and axion-like particles appear naturally in different scenarios and could solve some long-standing puzzles. Their detection is challenging, and all direct methods hinge on unknown couplings to the Standard Model of particle physics. However, the universal coupling to gravity provides model-independent signatures for these fields. We explore here the superradiant instability of spinning black holes triggered in the presence of such fields. The instability taps angular momentum from and limits the maximum spin of astrophysical black holes. We compute, for the first time, the spectrum of the most unstable modes of a massive vector (Proca) field for generic black-hole spin and Proca mass. The observed stability of the inner disk of stellar-mass black holes can be used to derive \emph{direct} constraints on the mass of dark photons in the mass range $ 10^{-13}\,{\rm eV}\lesssim m_V \lesssim 3\times 10^{-12}\,{\rm eV}$. By including also higher azimuthal modes, similar constraints apply to axion-like particles in the mass range $6\times10^{-13}\,{\rm eV}\lesssim m_{\rm ALP} \lesssim 10^{-11}\, {\rm eV}$. Likewise, mass and spin distributions of supermassive BHs --~as measured through continuum fitting, K$α$ iron line, or with the future space-based gravitational-wave detector LISA~-- imply indirect bounds in the mass range approximately $10^{-19}\,{\rm eV}\lesssim m_V, m_{\rm ALP} \lesssim 10^{-13}\, {\rm eV}$, for both axion-like particles and dark photons. Overall, superradiance allows to explore a region of approximately $8$ orders of magnitude in the mass of ultralight bosons.

gr-qc↗