arXiv subjects

Herschel Rabitz

Publications and source records attributed to Herschel Rabitz.

At least 19 recordsLinked to original sources

arXiv2026

Chirality routing non-polaritonic vacuum correlations in Landau polaritons

Ultrastrong coupling between matter and cavity vacuum fields can turn the electromagnetic vacuum into a structured quantum environment, thereby opening passive routes for modifying and manipulating material properties. Recent work has identified light--matter entanglement as an important ingredient in these property changes, which raises the question of where the relevant vacuum correlations actually reside. Landau polaritons provide chiral ultrastrong coupling systems in which one circular cavity polarization forms the bright polariton branches. Here, using a quantum information approach, we show that an exact chiral charge in a multimode Hopfield model routes the dominant anomalous correlations, squeezing, and cavity--matter entanglement into the opposite polarization. We find that, using parameters extracted from a multimode Landau polariton system, this hidden sector correlates the cyclotron resonance with finite momentum magnetoplasmons through Gaussian discord, while pairwise matter--matter entanglement remains absent. We further predict a polarization anisotropy of dressed vacuum electric field fluctuations as a signature of this chiral routing. These results identify chirality as a symmetry principle for organizing ultrastrong coupling vacua and show that quantum information tools provide a powerful framework for revealing the salient properties of Landau polaritons.

cond-mat.mes-hall↗

arXiv2025

Random Measures, ANOVA Models and Quantifying Uncertainty in Randomized Controlled Trials

The paper introduces a novel approach to global sensitivity analysis, grounded in the variance-covariance structure of random variables derived from random measures. The proposed methodology facilitates the application of information-theoretic rules for uncertainty quantification, offering several advantages. Specifically, the approach provides valuable insights into the decomposition of variance within discrete subspaces, similar to the standard ANOVA analysis. To illustrate this point, the method is applied to datasets obtained from the analysis of randomized controlled trials on evaluating the efficacy of the COVID-19 vaccine and assessing clinical endpoints in a lung cancer study.

stat.ME↗

arXiv2025

Directional Motional Control via Engineered Conical Intersections in Trapped Rydberg Ions

We demonstrate coherent control of motional dynamics in trapped Rydberg ions engineered to exhibit a conical intersection between adiabatic potential-energy surfaces. Using quantum optimal control, an optimally shaped electric field drives the motional wave packet between prescribed spatial configurations on microsecond timescales. Localized nonadiabatic coupling breaks the symmetry of the dynamics and produces a directed trajectory: after only a few early passages through the conical intersection region, the packet proceeds toward the target with high fidelity. In contrast, in the Born-Oppenheimer limit, where such coupling is absent, the optimized control yields symmetric, multi-cycle oscillations rather than a comparably directed displacement. While both approaches reach the target at the chosen final time, the underlying trajectories are qualitatively different. This work demonstrates that engineered conical intersections can serve as a new control resource for directional motional dynamics, complementing pulse-shaping methods in trapped-ion systems. This directional motion of the ions, enabled by the conical intersection, is expected to have important applications for quantum information processing, where controlled motional states underpin high-fidelity gate operations and scalable architectures.

quant-ph↗

arXiv2025

Orthogonal Dice

In this paper, we introduce a family of discrete rectangular uniform distributions on the natural numbers-referred to as orthogonal dice-characterized by the property that their means equal their variances. These distributions arise naturally in statistics and applied mathematics. We show that the orthogonal dice correspond to solutions of a quadratic Diophantine equation on the naturals, exhibiting divisibility properties tied to their dimensions, generating coprime arithmetic progressions, yielding disjoint partitions of the naturals, and displaying self-similarity. Their associated random counting measures (mixed binomial processes) exhibit interesting structural properties, including orthogonal splitting and convergence to Poisson limits. As a result, the orthogonal dice define canonical stochastic processes that that may be used to construct Brownian and geometric Brownian motions. More broadly, they serve as Poisson-like building blocks-natural substrates for modeling systems with bounded counts. Furthermore, they induce a trichotomy within the broader class of such distributions, partitioning them into three infinite subfamilies-negative, orthogonal, and positive-according to their mean-variance relationships.

math.PR↗

arXiv2025

A Fundamental Bound for Robust Quantum Gate Control

We derive a universal performance limit for coherent quantum control in the presence of modeled and unmodeled uncertainties. For any target unitary $W$ that is implementable in the absence of error, we prove that the worst-case (and hence the average) gate fidelity obeys the lower bound $F \ge \Flb\bigl(\tf \Omeff\bigr)$, where $\tf$ is the gate duration and $\Omeff$ is a single frequency-like measure that aggregates \emph{all} bounded uncertainty sources, e.g., coherent control imperfections, unknown couplings, and residual environment interactions, without assuming an initially factorizable system-bath state or a completely positive map. The bound is obtained by combining an interaction-picture averaging method with a Bellman-Gronwall inequality and holds for any finite-norm Hamiltonian decomposition. Hence it applies equally to qubits, multi-level qudits, and ancilla-assisted operations. Because $\Flb$ depends only on the dimensionless product $\tf\Omeff$, it yields a device-independent metric that certifies whether a given hardware platform can, in principle, reach a specified fault-tolerance threshold, and also sets a quantitative target for robust-control synthesis and system identification.

quant-ph↗

arXiv2025

Mechanism behind creating qubit gates expressed as interfering quantum pathway amplitudes

Hamiltonian encoding was introduced as a technique for revealing the mechanism of controlled quantum systems. It does so by decomposing the evolution into pathways between the computational basis states, where each pathway has an associated complex amplitude. The magnitude of a pathway amplitude determines its significance and many pathways constructively and/or destructively interfere to produce the final evolution of the system. In this paper, we apply Hamiltonian encoding to reveal the mechanism behind creating qubit gates implemented via optimal control pulses. An X gate, two CNOT gates, and a SWAP gate are examined to determine the degree of interference involved and to demonstrate that different optimal controls produce distinct mechanisms. Although the detailed mechanism for creating any gate depends on the nature of the control field, the mechanism analysis tools are generic. The presented gates and their mechanisms in this paper are thus illustrative and a researcher may apply these same tools to any gate with a suitable optimal control field.

quant-ph↗

arXiv2025

Tomography of Quantum States from Structured Measurements via quantum-aware transformer

Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this paper, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise.

quant-ph↗

arXiv2025

Machine Learning for Estimation and Control of Quantum Systems

The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control, calibration, and validation of quantum states and their dynamics. Machine learning methods have emerged as powerful tools owing to their remarkable capability to learn from data, and thus have been extensively utilized for different quantum tasks. This paper reviews several significant topics related to machine learning-aided quantum estimation and control. In particular, we discuss neural networks-based learning for quantum state estimation, gradient-based learning for optimal control of quantum systems, evolutionary computation for learning control of quantum systems, machine learning for quantum robust control, and reinforcement learning for quantum control. This review provides a brief background of key concepts recurring across many of these approaches with special emphasis on neural networks, evolutionary computation, and reinforcement learning.

quant-ph↗

arXiv2025

Efficient Hamiltonian encoding algorithms for extracting quantum control mechanism as interfering pathway amplitudes in the Dyson series

Hamiltonian encoding is a methodology for revealing the mechanism behind the dynamics governing controlled quantum systems. In this paper, following Mitra and Rabitz [Phys. Rev. A 67, 033407 (2003)], we define mechanism via pathways of eigenstates that describe the evolution of the system, where each pathway is associated with a complex-valued amplitude corresponding to a term in the Dyson series. The evolution of the system is determined by the constructive and destructive interference of these pathway amplitudes. Pathways with similar attributes can be grouped together into pathway classes. The amplitudes of pathway classes are computed by modulating the Hamiltonian matrix elements and decoding the subsequent evolution of the system rather than by direct computation of the individual terms in the Dyson series. The original implementation of Hamiltonian encoding was computationally intensive and became prohibitively expensive in large quantum systems. This paper presents two new encoding algorithms that calculate the amplitudes of pathway classes by using techniques from graph theory and algebraic topology to exploit patterns in the set of allowed transitions, greatly reducing the number of matrix elements that need to be modulated. These new algorithms provide an exponential decrease in both computation time and memory utilization with respect to the Hilbert space dimension of the system. To demonstrate the use of these techniques, they are applied to two illustrative state-to-state transition problems.

quant-ph↗

arXiv2024

The Top Manifold Connectedness of Quantum Control Landscapes

The control of quantum systems has been proven to possess trap-free optimization landscapes under the satisfaction of proper assumptions. However, many details of the landscape geometry and their influence on search efficiency still need to be fully understood. This paper numerically explores the path-connectedness of globally optimal control solutions forming the top manifold of the landscape. We randomly sample a plurality of optimal controls in the top manifold to assess the existence of a continuous path at the top of the landscape that connects two arbitrary optimal solutions. It is shown that for different quantum control objectives including state-to-state transition probabilities, observable expectation values and unitary transformations, such a continuous path can be readily found, implying that these top manifolds are fundamentally path-connected. The significance of the latter conjecture lies in seeking locations in the top manifold where an ancillary objective can also be optimized while maintaining the full optimality of the original objective that defined the landscape.

quant-ph↗

arXiv2022

Classical Elastic Two-Particle Collision Energy Conservation using Edward Nelson's Energy, Double Diffusion and Special Relativity

The present paper shows that Edward Nelson's stochastic mechanics approach for quantum mechanics can be derived from the two classical elastically colliding particles with masses M and m satisfying a collision momentum preserving equation. The properties of the classical elastic momentum collision expression determine the full Edward Nelson energy collision energy for both particles. This classical total energy expression does not require a statistical expectation since no process was defined for the energy and it models the main and incident particle velocities perfectly. Quantum mechanics can be obtained by modelling the incident particle as a non-random potential using stochastic processes modelling the forward, post-collision and backward pre-collision velocities of the main particle. This presents the Schroedinger equation exactly the way that Nelson proposed in 1966 except for the diffusion constant. In this case the average energy is conserved in time and the forward, post-collision and backward pre-collision velocities of the system are related using statistical methods. If the incident particle does not have a potential the additional constraints for the movement of the incident particle leads to another Schroedinger equation. Finally, under suitable conditions it will be shown that the colliding particles satisfy Minkowski metric in special relativity. This last example shows how gravity can be quantized using details of this energy expression.

quant-ph↗

arXiv2022

Quantum Optimal Control without Arbitrary Waveform Generators

Simple, precise, and robust control is demanded for operating a large quantum information processor. However, existing routes to high-fidelity quantum control rely heavily on arbitrary waveform generators that are difficult to scale up. Here, we show that arbitrary control of a quantum system can be achieved by simply turning on and off the control fields in a proper sequence. The switching instances can be designed by conventional quantum optimal control algorithms, while the required computational resources for matrix exponential can be substantially reduced. We demonstrate the flexibility and robustness of the resulting control protocol, and apply it to superconducting quantum circuits for illustration. We expect this proposal to be readily achievable with current semiconductor and superconductor technologies, which offers a significant step towards scalable quantum computing.

quant-ph↗

arXiv2022

Robust Quantum Control: Analysis & Synthesis via Averaging

An approach is presented for robustness analysis and quantum (unitary) control synthesis based on the classic method of averaging. The result is a multicriterion optimization competing the nominal (uncertainty-free) fidelity with a well known robustness measure: the size of an interaction (error) Hamiltonian, essentially the first term in the Magnus expansion of an interaction unitary. Combining this with the fact that the topology of the control landscape at high fidelity is determined by the null space of the nominal fidelity Hessian, we arrive at a new two-stage algorithm. Once the nominal fidelity is sufficiently high, we approximate both the nominal fidelity and robustness measure as quadratics in the control increments. An optimal solution is obtained by solving a convex optimization for the control increments at each iteration to keep the nominal fidelity high and reduce the robustness measure. Additionally, by separating fidelity from the robustness measure, more flexibility is available for uncertainty modeling.

quant-ph↗

arXiv2022

Kernel-based Global Sensitivity Analysis Obtained from a Single Data Set

Results from global sensitivity analysis (GSA) often guide the understanding of complicated input-output systems. Kernel-based GSA methods have recently been proposed for their capability of treating a broad scope of complex systems. In this paper we develop a new set of kernel GSA tools when only a single set of input-output data is available. Three key advances are made: (1) A new numerical estimator is proposed that demonstrates an empirical improvement over previous procedures. (2) A computational method for generating inner statistical functions from a single data set is presented. (3) A theoretical extension is made to define conditional sensitivity indices, which reveal the degree that the inputs carry shared information about the output when inherent input-input correlations are present. Utilizing these conditional sensitivity indices, a decomposition is derived for the output uncertainty based on what is called the optimal learning sequence of the input variables, which remains consistent when correlations exist between the input variables. While these advances cover a range of GSA subjects, a common single data set numerical solution is provided by a technique known as the conditional mean embedding of distributions. The new methodology is implemented on benchmark systems to demonstrate the provided insights.

stat.ME↗

arXiv2022

A field equation for induction-transduction of activation-deactivation probability on measurable space

Induction-transduction of activating-deactivating points are fundamental mechanisms of action that underlie innumerable systems and phenomena, mathematical, natural, and anthropogenic, and can exhibit complex behaviors such as self-excitation, phase transitions, hysteresis, polarization, periodicity, chaos, wave behavior, geometry, and energy transfer. We describe a class of primitives for induction-transduction based on dynamics on images of marked random counting measures under graphical random transformations. We derive a field equation for the law of the activation-deactivation (Bernoulli) process on an arbitrary measurable space and describe some mechanisms of action on the unit interval.

math.DS↗

arXiv2022

Random Graphs by Product Random Measures

A natural representation of random graphs is the random measure. The collection of product random measures, their transformations, and non-negative test functions forms a general representation of the collection of non-negative weighted random graphs, directed or undirected, labeled or unlabeled, where (i) the composition of the test function and transformation is a non-negative edge weight function, (ii) the mean measures encode edge density/weight and vertex degree density/weight, and (iii) the mean edge weight, when square-integrable, encodes generalized spectral and Sobol representations. We develop a number of properties of these random graphs, and we give simple examples of some of their possible applications.

math.PR↗

arXiv2022

The Optimization Landscape of Hybrid Quantum-Classical Algorithms: from Quantum Control to NISQ Applications

This review investigates the landscapes of prevalent hybrid quantum-classical optimization algorithms in many rapidly developing quantum technologies, where the objective function is either computed by a natural quantum system or a quantum ansatz that is engineered, but the optimizer is classical. In any particular case, the nature of the underlying control landscape is fundamentally important for systematic optimization of the objective. In early studies on the optimal control of few-body dynamics, the optimizer could take full control of the quantum systems to be manipulated whose Hilbert space dimension is relatively small. Stepping into the noisy intermediate-scale quantum (NISQ) era, the experimentally growing computational power of the ansatz expressed as quantum hardware may bring quantum advantage over classical computers, but the classical optimizer is often limited by the available control resources. Across these different scales, we will show that the landscape's geometry experiences morphological changes from favorable trap-free landscapes to easily trapping rugged landscapes, and eventually to barren-plateau landscapes on which the optimizer can hardly move. This unified view provides the basis for understanding classes of systems that may be readily controlled out to those with special consideration, including the difficulties and potential advantages of NISQ technologies, as well as seeking possible ways to escape traps or plateaus, in particular circumstances.

quant-ph↗

arXiv2021

The Jacobi Theta Distribution

We form the Jacobi theta distribution through discrete integration of exponential random variables over an infinite inverse square law surface. It is continuous, supported on the positive reals, has a single positive parameter, is unimodal, positively skewed, and leptokurtic. Its cumulative distribution and density functions are expressed in terms of the Jacobi theta function. We describe asymptotic and log-normal approximations, inference, and a few applications of such distributions to modeling.

math.PR↗