CyberSec Research

The Hopfield model describes a neural network that stores memories using all-to-all-coupled spins. Memory patterns are recalled under equilibrium dynamics. Storing too many patterns breaks the associative recall process because frustration causes an exponential number of spurious patterns to arise as the network becomes a spin glass. Despite this, memory recall in a spin glass can be restored, and even enhanced, under quantum-optical nonequilibrium dynamics because spurious patterns can now serve as reliable memories. We experimentally observe associative memory with high storage capacity in a driven-dissipative spin glass made of atoms and photons. The capacity surpasses the Hopfield limit by up to seven-fold in a sixteen-spin network. Atomic motion boosts capacity by dynamically modifying connectivity akin to short-term synaptic plasticity in neural networks, realizing a precursor to learning in a quantum-optical system.

The interplay between topology and strong interactions gives rise to a variety of exotic quantum phases, including fractional quantum Hall (FQH) states and their lattice analogs - fractional Chern insulators (FCIs). Such topologically ordered states host fractionalized excitations, which for spinful systems are often accompanied by ferromagnetism and skyrmions. Here, we study a Hofstadter-Hubbard model of spinful fermions on a square lattice, extended by nearest-neighbor interactions. Using large-scale density matrix renormalization group (DMRG) simulations, we demonstrate the emergence of a spin-polarized $\frac{1}{3}$-Laughlin-like FCI phase, characterized by a quantized many-body Chern number, a finite charge gap, and hidden off-diagonal long-range order. We further investigate the quantum Hall ferromagnet at $\nu=1$ and its skyrmionic excitations upon doping. In particular, we find that nearest-neighbor repulsion is sufficient to stabilize both particle- and hole-skyrmions in the ground state around $\nu=1$, whereas we do not find such textures around $\nu=\frac{1}{3}$. The diagnostic toolbox presented in this work, based on local densities, correlation functions, and spin-resolved observables, is directly applicable in quantum gas microscopy experiments. Our results open new pathways for experimental exploration of FCIs with spin textures in both ultracold atom and electronic systems.

We use a cavity optomechanical accelerometer to perform a resonant search for ultralight dark matter at acoustic frequencies near 39 kHz (a particle mass of $0.16$ neV/$c^2$). The accelerometer is based on a Si$_3$N$_4$ membrane, cryogenically cooled to 4 K, with photothermal heating employed to scan the resonance frequency by $10^2$ detector linewidths. Leveraging shot-noise-limited displacement readout and radiation pressure feedback cooling, we realize an acceleration resolution of $\sim 10\;\text{n}g_0/\sqrt{\text{Hz}}$ over a bandwidth of $30$ Hz near the fundamental test mass resonance. We find no evidence of a dark matter signal and infer an upper bound on the coupling to normal matter that is several orders of magnitude above the stringent bounds set by equivalence principle experiments. We outline a path toward novel dark matter constraints in future experiments by exploiting arrays of mass-loaded optomechanical sensors at lower temperature probed with distributed squeezed light.

This is the manual of the first version of QEDtool, an object-oriented Python package that performs numerical quantum electrodynamics calculations, with focus on full state reconstruction in the internal degrees of freedom, correlations and entanglement quantification. Our package rests on the evaluation of polarized Feynman amplitudes in the momentum-helicity basis within a relativistic framework. Users can specify both pure and mixed initial scattering states in polarization space. From the specified initial state and polarized Feynman amplitudes, QEDtool reconstructs correlations that fully characterize the quantum polarization and entanglement within the final state. These quantities can be expressed in any inertial frame by arbitrary, built-in Lorentz transformations.

Arrays of neutral atoms present a promising system for quantum computing, quantum sensors, and other applications, several of which would profit from the ability to load, cool, and image the atoms in a finite magnetic field. In this work, we develop a technique to image and prepare $^{87}$Rb atom arrays in a finite magnetic field by combining EIT cooling with fluorescence imaging. We achieve 99.6(3)% readout fidelity at 98.2(3)% survival probability and up to 68(2)% single-atom stochastic loading probability. We further develop a model to predict the survival probability, which also agrees well with several other atom array experiments. Our technique cools both the axial and radial directions, and will enable future continuously-operated neutral atom quantum processors and quantum sensors.

We study multiparty entanglement near measurement induced phase transitions (MIPTs), which arise in ensembles of local quantum circuits built with unitaries and measurements. In contrast to equilibrium quantum critical transitions, where entanglement is short-ranged, MIPTs possess long-range k-party genuine multiparty entanglement (GME) characterized by an infinite hierarchy of entanglement exponents for k >= 2. First, we represent the average spread of entanglement with "entanglement clusters", and use them to conjecture general exponent relations: 1) classical dominance, 2) monotonicity, 3) subadditivity. We then introduce measure-weighted graphs to construct such clusters in general circuits. Second, we obtain the exact entanglement exponents for a 1d MIPT in a measurement-only circuit that maps to percolation by exploiting non-unitary conformal field theory. The exponents, which we numerically verify, obey the inequalities. We also extend the construction to a 2d MIPT that maps to classical 3d percolation, and numerically find the first entanglement exponents. Our results provide a firm ground to understand the multiparty entanglement of MIPTs, and more general ensembles of quantum circuits.

Cold atoms are promising platforms for metrology and quantum computation, yet their many-body dynamics remains largely unexplored. We here investigate Rabi oscillations from optically-thick cold clouds, driven by high-intensity coherent light. A dynamical displacement from the atomic resonance is predicted, which can be detected through the collective Rabi oscillations of the atomic ensemble. Different from linear-optics shifts, this dynamical displacement grows quadratically with the optical depth, yet it reduces with increasing pump power as dipole-dipole interactions are less effective. This modification may be particularly important for Ramsey spectroscopy, when strong pulses and optically dense samples are used.

Neutral atom quantum computing's great scaling potential has resulted in it emerging as a popular modality in recent years. For state preparation, atoms are loaded stochastically and have to be detected and rearranged at runtime to create a predetermined initial configuration for circuit execution. Such rearrangement schemes either suffer from low parallelizability for acousto-optic deflector (AOD)-based approaches or are comparatively slow in case of spatial light modulators (SLMs). In our work, we introduce an algorithm that can improve the parallelizability of the former. Since the transfer of atoms from static SLM traps to AOD-generated movable traps is detrimental both in terms of atom loss rates and execution time, our approach is based on highly-parallel composite moves where many atoms are picked up simultaneously and maneuvered into target positions that may be comparatively distant. We see that our algorithm outperforms its alternatives for near-term devices with up to around 1000 qubits and has the potential to scale up to several thousand with further optimizations.

We explore the evolution of a strongly interacting dissipative quantum Ising spin chain that is driven by a slowly varying time-dependent transverse field. This system possesses an extensive number of instantaneous (adiabatic) stationary states which are coupled through non-adiabatic transitions. We analytically calculate the generator of the ensuing slow dynamics and analyze the creation of coherences through non-adiabatic processes. For a certain choice of the transverse field shape, we show that the system solely undergoes transitions among classical basis states after each pulse. The concatenation of many of such pulses leads to an evolution of the spin chain under a many-body dynamics that features kinetic constraints. Our setting not only allows for a quantitative investigation of adiabatic theorems and non-adiabatic corrections in a many-body scenario. It also directly connects to many-body systems in the focus of current research, such as ensembles of interacting Rydberg atoms which are resonantly excited by a slowly varying laser pulse and subject to dephasing noise.

Support Vector Machines (SVMs) are a cornerstone of supervised learning, widely used for data classification. A central component of their success lies in kernel functions, which enable efficient computation of inner products in high-dimensional feature spaces. Recent years have seen growing interest in leveraging quantum circuits -- both qubit-based and quantum optical -- for computing kernel matrices, with ongoing research exploring potential quantum advantages. In this work, we investigate two classical techniques for enhancing SVM performance through kernel learning -- the Fisher criterion and quasi-conformal transformations -- and translate them into the framework of quantum optical circuits. Conversely, using the example of the displaced squeezed vacuum state, we demonstrate how established concepts from quantum optics can inspire novel perspectives and enhancements in SVM methodology. This cross-disciplinary approach highlights the potential of quantum optics to both inform and benefit from advances in machine learning.

Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently by bypassing the costly training of gradient-based learning methods. Here, we demonstrate the capability of this approach to predict and characterize chaotic dynamics in discrete nonlinear maps, exemplified through the logistic and H\'enon maps. While achieving excellent predictive accuracy, we also demonstrate the optimization of training hyperparameters of the quantum reservoir based on the properties of the underlying map, thus paving the way for efficient forecasting with other discrete and continuous time-series data. Furthermore, the framework exhibits robustness against decoherence when trained in situ and shows insensitivity to reservoir Hamiltonian variations. These results highlight quantum reservoir computing as a scalable and noise-resilient tool for modeling complex dynamical systems, with immediate applicability in near-term quantum hardware.

High-fidelity detection of charge transitions in quantum dots (QDs) is a key ingredient in solid state quantum computation. We demonstrate high-bandwidth radio-frequency charge detection in bilayer graphene quantum dots (QDs) using a capacitively coupled quantum point contact (QPC). The device design suppresses screening effects and enables sensitive QPC-based charge readout. The QPC is arranged to maximize the readout contrast between two neighboring, coupled electron and hole QDs. We apply the readout scheme to a single-particle electron-hole double QD and demonstrate time-resolved detection of charge states as well as magnetic field dependent tunneling rates. This promises a high-fidelity readout scheme for individual spin and valley states, which is important for the operation of spin, valley or spin-valley qubits in bilayer graphene.

A key challenge in quantum complexity is how entanglement structure emerges from dynamics, highlighted by advances in simulators and information processing. The Lieb--Robinson bound sets a locality-based speed limit on information propagation, while the Small-Incremental-Entangling (SIE) theorem gives a universal constraint on entanglement growth. Yet, SIE bounds only total entanglement, leaving open the fine entanglement structure. In this work, we introduce Spectral-Entangling Strength, measuring the structural entangling power of an operator, and prove a Spectral SIE theorem: a universal limit for R\'enyi entanglement growth at $\alpha \ge 1/2$, revealing a robust $1/s^2$ tail in the entanglement spectrum. At $\alpha=1/2$ the bound is qualitatively and quantitatively optimal, identifying the universal threshold beyond which growth is unbounded. This exposes the detailed structure of Schmidt coefficients, enabling rigorous truncation-based error control and linking entanglement to computational complexity. Our framework further establishes a generalized entanglement area law under adiabatic paths, extending a central principle of many-body physics to general interactions. Practically, we show that 1D long-range interacting systems admit polynomial bond-dimension approximations for ground, time-evolved, and thermal states. This closes the quasi-polynomial gap and proves such systems are simulable with polynomial complexity comparable to short-range models. By controlling R\'enyi entanglement, we also derive the first rigorous precision-guarantee bound for the time-dependent density-matrix-renormalization-group algorithm. Overall, our results extend SIE and provide a unified framework that reveals the detailed structure of quantum complexity.

Scaling quantum computers, i.e., quantum processing units (QPUs) to enable the execution of large quantum circuits is a major challenge, especially for applications that should provide a quantum advantage over classical algorithms. One approach to scale QPUs is to connect multiple machines through quantum and classical channels to form clusters or even quantum networks. Using this paradigm, several smaller QPUs can collectively execute circuits that each would not be able on its own. However, communication between QPUs is costly as it requires generating and maintaining entanglement, and hence it should be used wisely. In this paper, we evaluate the architectures, and in particular the entanglement patterns, of variational quantum circuits in a distributed quantum computing (DQC) setting. That is, using Qiskit, we simulate the execution of an eight qubit circuit using four QPUs each with two computational and two communication qubits where non-local CX-gates are performed using the remote-CX protocol. We compare the performance of various circuits on a binary classification task where training is executed under ideal and testing under noisy conditions. The study provides initial results on suitable VQC architectures for the DQC paradigm, and indicates that a standard VQC baseline is not always the best choice, and alternative architectures that use entanglement between QPUs sparingly deliver better results under noise.

This work investigates the scaling characteristics of post-compilation circuit resources for Quantum Machine Learning (QML) models on connectivity-constrained NISQ processors. We analyze Quantum Kernel Methods and Quantum Neural Networks across processor topologies (linear, ring, grid, star), focusing on SWAP overhead, circuit depth, and two-qubit gate count. Our findings reveal that entangling strategy significantly impacts resource scaling, with circular and shifted circular alternating strategies showing steepest scaling. Ring topology demonstrates slowest resource scaling for most QML models, while Tree Tensor Networks lose their logarithmic depth advantage after compilation. Through fidelity analysis under realistic noise models, we establish quantitative relationships between hardware improvements and maximum reliable qubit counts, providing crucial insights for hardware-aware QML model design across the full-stack architecture.

Consider a Hermitian operator $A$ acting on a complex Hilbert space of dimension $2^n$. We show that when $A$ has small degree in the Pauli expansion, or in other words, $A$ is a local $n$-qubit Hamiltonian, its operator norm can be approximated independently of $n$ by maximizing $|\braket{\psi|A|\psi}|$ over a small collection $\mathbf{X}_n$ of product states $\ket{\psi}\in (\mathbf{C}^{2})^{\otimes n}$. More precisely, we show that whenever $A$ is $d$-local, \textit{i.e.,} $\deg(A)\le d$, we have the following discretization-type inequality: \[ \|A\|\le C(d)\max_{\psi\in \mathbf{X}_n}|\braket{\psi|A|\psi}|. \] The constant $C(d)$ depends only on $d$. This collection $\mathbf{X}_n$ of $\psi$'s, termed a \emph{quantum norm design}, is independent of $A$, and consists of product states, and can have cardinality as small as $(1+\eps)^n$, which is essentially tight. Previously, norm designs were known only for homogeneous $d$-localHamiltonians $A$ \cite{L,BGKT,ACKK}, and for non-homogeneous $2$-local traceless $A$ \cite{BGKT}. Several other results, such as boundedness of Rademacher projections for all levels and estimates of operator norms of random Hamiltonians, are also given.

We explore the possibility of forming a oriented polar molecule directly from a pair of colliding atoms. The process comprises the photoassociation and vibrational stabilization along with the molecular orientation. These processes are driven by a single time-dependent, linearly polarized control field and proceeds entirely within the electronic ground state, leveraging the presence of a permanent dipole moment. The control field is found by means of an optimal quantum control algorithm with a single target observable given by the restriction of the orientation operator on a subset of bound levels. We consider a rovibrational model system for the collision of O + H atoms and solve directly the time-dependent Schrodinger equation. We show that the optimized field is capable of enhancing the molecular orientation already induced by the photoassociation and vibrational stabilization thus yielding oriented polar molecules that can be useful for many applications.

Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we introduce a novel framework for performing Lindblad tomography using Physics-Informed Neural Networks (PINNs). By embedding the Lindblad master equation directly into the neural network's loss function, our approach simultaneously learns the quantum state's evolution and infers the underlying dissipation parameters from sparse, time-series measurement data. Our results show that PINNs can reconstruct both the system dynamics and the functional form of unknown noise parameters, presenting a sample-efficient and scalable solution for quantum device characterization. Ultimately, our method produces a fully-differentiable digital twin of a noisy quantum system by learning its governing master equation.