QOC — System identification for control

Out-of-control Hamiltonian Learning

2026-10-05T00:00:00Z

Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable guarantees assume some level of quantum control--fast, arbitrary single-qubit gates interleaved with time evolution, and measurements in arbitrary bases--that is beyond the capabilities of near-term analog quantum simulators. Motivated by analog atom- and ion-based platforms, we study Hamiltonian learning under minimal access models. Uniform state preparation and measurements: We first consider the setting where in every experiment, one can rotate each qubit to the same state, perform short-time evolution, and measure every qubit in the same basis. Surprisingly, we show that for generic 2-local Hamiltonians on any interaction graph, all of the parameters can be reconstructed from such experiments. Computational basis state preparation and measurements: We then consider a similarly constrained setting, but where state preparation and measurement are restricted to the computational basis. For nearest-neighbor Hamiltonians with only Pauli $X/Z$ interactions, a class which captures contemporary Rydberg atom platforms, we show that over 1D and 2D rectangular lattices, all of the parameters can be reconstructed from such experiments up to unavoidable gauges. Our protocols introduce new techniques for solving structured polynomial systems over an extensive number of parameters. Taken together, our results suggest that one can learn a great deal from the dynamics of quantum many-body systems even under the most stringent experimental constraints.

Emergent Prethermal Symmetries for Scalable Hamiltonian Learning

2026-09-23T00:00:00Z

Learning an interacting many-body Hamiltonian at the Heisenberg limit generally requires control that preserves local information for evolution times of order $1/\varepsilon$ to attain $\varepsilon$ precision. To achieve this, existing protocols often rely on trusted many-qubit operations or increasingly rapid control pulses, creating a precision barrier when gates involve unknown multi-qubit interaction or have finite duration. We show that neither resource is necessary for geometrically local Hamiltonians. Our protocol applies only static single-qubit fields, whose strength is independent of system size and grows polylogarithmically with $1/\varepsilon$. These fields generate emergent prethermal symmetries that suppress thermalization for the entire learning experiment while retaining informative symmetry-preserving dynamics. On a $d$-dimensional lattice of $n$ qubits, this enables learning every coefficient to accuracy $\varepsilon$, with failure probability at most $δ$, using total evolution time $\mathcal{O}(\log^d(1/\varepsilon)\log^2(n/δ)/\varepsilon).$ Thus, our learning protocol attains the information-theoretically optimal $1/\varepsilon$ dependence up to polylogarithmic factors using product-state preparation, single-qubit measurements, and non-adaptive experiments. More broadly, our results establish emergent prethermal symmetries as a resource for quantum learning, opening a route to Heisenberg-limited characterization of many-body systems where fast or trusted many-qubit control is unavailable.

Materials for Quantum Information Science: Roles in the Quantum Evolution 2.0

2026-09-08T00:00:00Z

Quantum information science is entering a second phase, the Quantum Evolution 2.0, in which the challenge has shifted from demonstrating coherent control of individual quantum states to building scalable multi-qubit processors and networks. This transition places materials science at the center of the field. Across superconducting circuits, quantum defects, quantum photonic devices, and emerging materials platforms, including two-dimensional materials and heterostructures, performance is now limited less by device design than by poorly controlled surfaces, buried interfaces, and defects whose atomic identities remain incompletely known. This review surveys the materials challenges of these quantum platforms together with the characterization methods needed to resolve them. For each platform we identify the dominant decoherence mechanisms, the current state of materials understanding, and the most pressing open materials problems. A cross-platform comparison then reveals a shared structure-coherence problem. The implicated material chemistry recurs across platforms, involving light elements in disordered or buried environments, yet no platform can quantitatively connect a specific atomic-scale structure to a measured change in coherence. We close by identifying three needs, mechanistic understanding of decoherence at the atomistic level, high-throughput proxy metrics predictive of device performance, and characterization tools built for quantum materials, whose resolution would advance coherence, scalability, and integration across all platforms.

Multimode squeezed light generation and characterization

2026-09-08T00:00:00Z

Nowadays, the realization of quantum computations and communications based on continuous variables has attracted a significant attention due to a substantial expansion of the system dimensionality. The main progress in this area is attributed to the implementation of multimode systems based on squeezed states of light. One of the simplest ways to generate such states relies upon their producing in a single-pass optical parametric amplifier (OPA) using ultrafast pumping. However, for homodyne detection of such multimode states, the profile of the local oscillator (LO) must perfectly match the profile of the measured mode. Usually, this is not the case; therefore a proper treatment of multimode squeezing is required. In this work, we study both theoretically and experimentally the multimode squeezed light generated in type-0 and type-II OPA. We characterize such sources and investigate the degree of squeezing in dependence on the LO spectral profile, employing a pulse shaping technique. The theoretical analysis is performed using the Schmidt-mode theory. This work might have a significant impact on the realization of multimode quantum protocols

Feedback stabilization of multi-qubit Hamiltonian parameters enabled by single-shot measurement-based sequential Monte Carlo

2026-07-29T00:00:00Z

Fast measurement, signal processing, and accurate estimation of Hamiltonian parameters are essential for feedback control in quantum-classical interface circuitry. However, existing frequentist and Bayesian inference methods typically require a large number of measurements to achieve the accuracy needed to mitigate qubit decoherence. Consequently, feedback control of semiconductor qubits has largely been limited to single-qubit frequency stabilization, whereas two-qubit parameter stabilization remains experimentally unexplored. Here, we demonstrate a real-time feedback framework based on sequential Monte Carlo estimation using one bit of data from a single-shot measurement. Using a four-qubit semiconductor quantum dot device, we rapidly estimate individual qubit frequencies, yielding an approximately twofold increase in coherence time compared with a conventional Bayesian strategy. Moreover, sequential two-qubit parameter estimation using two bits of data enables stabilization of qubit-qubit coupling, allowing both quasi-static frequency drift and exchange-interaction noise to be estimated and suppressed. By shortening the time required for precise parameter estimation, these results demonstrate the importance of the synergistic development of classical and quantum electronics for building robust and scalable quantum technologies in fluctuating environments.

Topological Characterization of Discrete-Time Classical Stochastic Processes: Dual Role of Point-Gap Topology

2026-05-08T00:00:00Z

We present topological characterization of classical stochastic processes described by discrete-time Markov chains on lattices. We point out that point-gap topology of stochastic matrices entails two distinct physical consequences that hinge on the choice of the reference point. The point-gap topology around a generic reference point is related to the direction of transport, and nontrivial topology around the origin of the complex spectrum of a stochastic matrix implies non-Markovianity caused by, e.g., feedback control. On the basis of this characterization, we identify the topological origin of directed transport in a classic experiment of Maxwell's demon [S. Toyabe et al., Nat. Phys. 6, 988 (2010)] and find the topological nature of feedback control beyond thermodynamic interpretation. We demonstrate that a topologically enforced non-Markovian classical stochastic process can be simulated by a Markovian quantum master equation, indicating a topological form of quantum advantage.

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data

2026-05-02T00:00:00Z

High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum machine learning control (QMLC) framework for generative quantum circuit synthesis from gate-set tomography (GST) data that bypasses the traditional two-step pipeline of characterizing native quantum gates via GST followed by unitary decomposition algorithms. Instead, a generative concept space is directly learnt from GST data, enabling conditional synthesis of quantum circuits on a desired output distribution. Our approach tokenizes GST germ circuits and embeds them into a structured latent space using a curriculum-learning-motivated strategy, starting with short circuits and progressively incorporating longer ones with diverse output statistics. The embedded sequences are processed by a set-vision transformer with permutation-invariant pooling, producing k-seed vectors that represent the learned concept space of the quantum device. Aggregating data across multiple circuits makes this latent representation inherently context-aware, capturing the shared physical noise environment (e.g., crosstalk, drift) that isolated gate metrics miss. We propose an unconditional diffusion model to sample from the concept space. During inference, a user provides a target measurement distribution, and the model generates a corresponding circuit. To ensure fidelity and robustness, the output is denoised using a diffusion model that operates on the target conditional covariance matrix. This end-to-end framework is a step towards context-aware, hardware-native circuit synthesis directly from raw GST data, which offers a new paradigm for integrating quantum control and compilation. The QMLC framework is particularly suited for near-term quantum devices with complex calibration procedures.

Heisenberg-limited Hamiltonian learning without short-time control

2026-04-30T00:00:00Z

Characterizing quantum systems by learning their underlying Hamiltonians is a central task in quantum information science. While recent algorithmic advances have achieved near-optimal efficiency in this task, they critically rely on accessing arbitrarily short-time dynamics. This reliance poses severe experimental challenges due to finite control bandwidth and transient pulse errors. In this work, we demonstrate that Heisenberg-limited Hamiltonian learning can be achieved without short-time control. We introduce a framework in which every query to the unknown dynamics has duration at least a prescribed minimum time $T$, and show that this restriction does not preclude Heisenberg-limited scaling. The key ingredient is a method for emulating the continuous quantum control required by iterative learning algorithms using only such lower-bounded evolution times. This reduces the learning task to sparse pure-state tomography. Notably, for logarithmically sparse Hamiltonians, our algorithm achieves the information-theoretically optimal $1/\varepsilon$ scaling in total evolution time for any arbitrary constant minimum evolution time $T$. For many-body (polynomially sparse) systems, we uncover a rigorous quantitative tradeoff, showing that the minimum required evolution time can be significantly relaxed from the standard limit at a polynomial cost in total evolution time. Our results affirmatively resolve a prominent open problem in the field and reveal that high-bandwidth, ultra-short pulses are not fundamentally necessary for optimal quantum learning.

Direct characterization of classical dephasing noise for a qubit

2026-04-28T00:00:00Z

We propose a general method to characterize classical stochastic noise causing qubit dephasing through repetitive Ramsey interferometry measurements (RIMs) on the qubit. Compared to filter-function-based spectroscopy, our method with simpler pulse control is less constrained by probe coherence time and can directly detect arbitrary-order correlation functions of quasistatic noise processes. We show that each RIM with a short evolution time and suitably chosen control pulses directly samples the noise field and the $n$-point correlations of the RIM outcomes are proportional to the $n$-point correlation functions of the noise processes. We demonstrate the method numerically for two representative cases: an Ornstein-Uhlenbeck Gaussian process and a non-Gaussian ensemble of two-level fluctuators. While practical constraints such as readout contrast and sampling cost persist, our method offers a direct route to quasistatic classical noise spectroscopy across diverse platforms.

Hardware-Aware Performance Characterization of Small Dense Lindblad Propagation for Near-Term Quantum Control

2026-03-17T00:00:00Z

Dense Lindblad propagation at the Hilbert-space sizes of near-term transmon control ($d = 3$, $9$, $27$) sits in a regime where cache boundaries, launch overhead, and fixed latency decide which hardware is useful, not peak arithmetic rate. This paper characterizes that regime on two x86 CPUs, two NVIDIA GPUs, and a low-cost FPGA. Each implementation is stated as an algorithm in $d$ with an idealized cost built from separately measured ceilings, and the measured gaps are attributed to specific mechanisms: thread fork/join, the strided read of the propagator, PCI Express (PCIe) transfers, and propagator re-upload. An end-to-end gradient-ascent (GRAPE-style) benchmark against released QuTiP 5.2.3 shows that fast propagation does not imply fast control optimization: the C path wins by up to two orders of magnitude at $d = 3$ but loses at $d = 27$, where the dense LU solve inside the Padé propagator build takes over $90\%$ of the cost. The FPGA contributes a deterministic-latency operating point, $95$ cycles per step at a stress-characterized $108$ MHz with zero cycle spread over $1000$ trials, and its Q1.15 fixed-point drift is measured on the device. The results are a map of operating regimes for the measured grid, with the propagator build, not propagation, as the dominant cost at the largest size.

Experimental characterization of coherent and non-Markovian errors using tangent space decomposition

2026-02-23T00:00:00Z

Accurate characterization of coherent and non-Markovian errors remains a central challenge in quantum information processing, as conventional benchmarking techniques typically rely on Markovian and time-independent noise assumptions. In practice, however, quantum devices exhibit both systematic coherent miscalibrations and temporally correlated fluctuations, which complicate error diagnosis and mitigation. Here, we apply a technique based on tangent-space decomposition to characterize such error in single-qubit quantum gates implemented on a trapped ion platform. Small imperfections in a quantum operation are treated as perturbations of the target quantum map, represented as tangent vectors in the space of quantum channels. This formulations enables a natural decomposition of the deviation into three components corresponding to coherent, Markovian and non-Markovian processes.The relative weights of these components provide a quantitative measure of the contribution from each type of error mechanism, directly from a single tomographic snapshot. We experimentally validate this method on a single-qubit gates implemented on a trapped $^{40}$Ca$^+$ ion, where control is achieved through laser-driven optical transitions. By analyzing experimentally reconstructed process matrices, expressed in the Pauli Transfer Matrix and Choi representations, we identify and quantify non-Markovian effects arising from controlled injection of slow fluctuations in the experimental environment. We also characterize deterministic coherent miscalibrations using the same technique. This approach provides a physically transparent and experimentally accessible tool for diagnosing complex error sources in quantum control systems.

Characterization and cancellation of power-line-induced motional-mode frequency noise in a trapped-ion system

2026-02-23T00:00:00Z

The stability of motional-mode frequency is essential for realizing high-fidelity quantum gates in trapped-ion quantum computing. While broadband Gaussian noise has been extensively studied and mitigated using pulse shaping techniques, the impact of coherent periodic noise has remained largely unexplored. Here we report a systematic investigation of 60-Hz power-line noise and its effect on the secular frequencies of a single ${}^{171}\mathrm{Yb}^{+}$ ion. Using spin-echo Ramsey spectroscopy, we characterize the amplitude and phase of the resulting secular-frequency modulation and validate this characterization via passive phase correction of the Ramsey sequence. Building on this, we implement a cancellation scheme by injecting a compensation tone into the set-point of a PI controller that stabilizes the trap RF drive amplitude. A phasor-fitting procedure optimizes the amplitude and phase of the compensation signal, enabling near-complete suppression of the 60-Hz component. With the cancellation applied, the coherence time of a radial motional mode is extended from approximately 10 ms to 35 ms, consistent with the limit set by motional heating. Our results provide both a clear characterization of periodic motional-mode noise and a practical framework for its suppression in trapped-ion quantum computing platforms.

High-Field NMR Characterization and Indirect $J$-Spectroscopy of a Nuclear Spin Chain [U-$^{13}$C,$^{15}$N]-butyronitrile

2026-02-14T00:00:00Z

One-dimensional chains of coupled spins are minimal models of strongly correlated quantum matter, and have been proposed as wires for transporting quantum information. In liquids, rapid molecular tumbling averages anisotropic dipolar couplings and leaves effective isotropic scalar $J$-coupling Hamiltonians. At zero- to ultralow-field (ZULF) conditions, differences in frequency between nuclear spins of different types are quenched and the internal Hamiltonians can be closely approximated by an isotropic Heisenberg model. In this work, we present [U-$^{13}$C,$^{15}$N]-butyronitrile as a chemically engineered nuclear spin chain whose full spin-spin coupling network can be determined and validated by combining high-field NMR detection with evolution at ultralow fields. Starting from high-field (16.4 T) NMR spectra of $^1$H, $^{13}$C, and $^{15}$N nuclei, we extract all relevant $J$-couplings within a 12-spin network (four $^{13}$C, one $^{15}$N, and seven $^1$H). We then employ a mechanical field-cycling apparatus to prepolarize the spins at high field, shuttle them into a magnetically shielded region for evolution at <50 nT, and detect signals after returning to high field. Fourier analysis of the ultralow-field evolution yields indirect $J$-spectra that are conceptually analogous to ZULF NMR spectra but measured by a high-field NMR spectrometer. We observe clear spectral features at $J$, 1.5$J$, and 2$J$, in good agreement with simulations using the extracted coupling matrix. Finally, we demonstrate 2D experiments that correlate high-field chemical shifts and, thus, fully map interactions within the molecular spin chain. Our results establish [U-$^{13}$C,$^{15}$N]-butyronitrile as an extremely well-characterized spin chain model system and provide a quantitative Hamiltonian benchmark for future hyperpolarization and quantum-control studies.

Quantum-controlled synthetic materials

2026-02-05T00:00:00Z

Analog quantum simulators and digital quantum computers are two distinct paradigms driving near-term applications in modern quantum science, from probing many-body phenomena to identifying computational advantage over classical systems. A transformative opportunity on the horizon is merging the high-fidelity many-body evolution in analog simulators with the robust control and measurement of digital machines. Such a hybrid platform would unlock new capabilities in state preparation, characterization and dynamical control. Here, we embed digital quantum control in the analog evolution of a synthetic quantum material by entangling the lattice potential landscape of a Bose-Hubbard circuit with an ancilla qubit. This Hamiltonian-level control induces dynamics under a superposition of different lattice configurations and guides the many-body system to novel strongly-correlated states where different phases of matter coexist -- ordering photons into superpositions of solid and fluid eigenstates. Leveraging hybrid control modalities, we adiabatically introduce disorder to localize the photons into an entangled cat state and enhance its coherence using a many-body echo technique. This work illustrates the potential for entangling quantum computers with quantum matter -- synthetic and solid-state -- for advantage in sensing and materials characterization.

Data-Driven Qubit Characterization and Optimal Control using Deep Learning

2026-01-26T00:00:00Z

Quantum computing requires the optimization of control pulses to achieve high-fidelity quantum gates. We propose a machine learning-based protocol to address the challenges of evaluating gradients and modeling complex system dynamics. By training a recurrent neural network (RNN) to predict qubit behavior, our approach enables efficient gradient-based pulse optimization without the need for a detailed system model. First, we sample qubit dynamics using random control pulses with weak prior assumptions. We then train the RNN on the system's observed responses, and use the trained model to optimize high-fidelity control pulses. We demonstrate the effectiveness of this approach through simulations on a single $ST_0$ qubit.

Deep learning parameter estimation and quantum control of single molecule

2026-01-05T00:00:00Z

Coherent control, a central concept in physics and chemistry, has sparked significant interest due to its ability to fine-tune interference effects in atoms and individual molecules for applications ranging from light-harvesting complexes to molecular qubits. However, precise characterization of the system's dissipative dynamics is required for its implementation, especially at high temperature. In a quantum control experiment, this means learning system-bath parameters and driving coupling strengths. Here, we demonstrate how to infer key physical parameters of a single molecule driven by spectrally modulated pulses at room temperature. We develop and compare two computational approaches based on two-photon absorption photoluminescence signals: an optimization-based minimization scheme and a feed-forward neural network. The robustness of our approach highlights the importance of reliable parameter estimation in designing effective coherent control protocols. Our results have direct applications in ultrafast spectroscopy, quantum materials and technology.

How to measure laser chirp rate at single-emitter excitation energies

2025-12-05T00:00:00Z

We present a simple and direct method for measuring laser chirp rate, i.e., group delay dispersion (GDD) of ultrashort laser pulses at power levels compatible with single-quantum-emitter excitation. Traditional pulse characterization techniques rely on nonlinear optical processes that require high peak powers, making them unsuitable for the attojoule-to-femtojoule regime relevant to quantum photonics. Our approach utilizes a wavelength-to-time mapping method in which the arrival times of spectrally filtered components of a broadband pulse are recorded using a superconducting nanowire single-photon detector and correlated via a high-resolution time-tagging system. The resulting linear relationship between wavelength and arrival time directly yields the dispersion parameter and, subsequently, the GDD. Beyond single-emitter excitation, this technique can be applied in areas such as single-photon spectroscopy, ultralow-power optical communications, and time-domain quantum control, where linear and non-destructive dispersion characterization is essential.

Quantum Probe Tomography

2025-10-09T00:00:00Z

Characterizing quantum many-body systems is a fundamental problem across physics, chemistry, and materials science. While significant progress has been made, many existing Hamiltonian learning protocols demand digital quantum control over the entire system, creating a disconnect from many real-world settings that provide access only through small, local probes. Motivated by this, we introduce and formalize the problem of quantum probe tomography, where one seeks to learn the parameters of a many-body Hamiltonian using a single local probe access to a small subsystem of a many-body thermal state undergoing time evolution. We address the identifiability problem of determining which Hamiltonians can be distinguished from probe data through a new combination of tools from algebraic geometry and smoothed analysis. Using this approach, we prove that generic Hamiltonians in various physically natural families are identifiable up to simple, unavoidable structural symmetries. Building on these insights, we design the first efficient end-to-end algorithm for probe tomography that learns Hamiltonian parameters to accuracy $\varepsilon$, with query complexity scaling polynomially in $1/\varepsilon$ and classical post-processing time scaling polylogarithmically in $1/\varepsilon$. In particular, we demonstrate that translation- and rotation-invariant nearest-neighbor Hamiltonians on square lattices in one, two, and three dimensions can be efficiently reconstructed from single-site probes of the Gibbs state, up to inversion symmetry about the probed site. Our results demonstrate that robust Hamiltonian learning remains achievable even under severely constrained experimental access.

Identification and optimal control strategies for the transversal splitting of ultra--cold Bose gases

2025-10-08T00:00:00Z

Splitting a Bose--Einstein condensate (BEC) is a key operation in fundamental physics experiments and emerging quantum technologies, where precise preparation of well--defined initial states requires fast yet coherent control of the condensate's nonlinear dynamics. This work formulates the BEC splitting process as an optimal feedforward control problem based on a physically interpretable, reduced--order model identified from limited experimental data. We introduce a systematic calibration strategy that combines optimal experiment selection and constrained nonlinear parameter estimation, enabling accurate system identification with minimal experimental overhead. Using this calibrated model, we compute energy--optimal trajectories via indirect optimal control to realize shortcuts to adiabaticity (STAs), achieving rapid transitions to the ground state of a double--well potential while suppressing excitations. Experiments confirm that the proposed control framework yields high--fidelity state transfers across multiple configurations, demonstrating its robustness and scalability for quantum control applications.

Design and characterization of a cryogenic vacuum chamber for ion trapping experiments

2025-10-02T00:00:00Z

We present the design and characterization of a cryogenic vacuum chamber incorporating mechanical isolation from vibrations, a high numerical-aperture in-vacuum imaging objective, in-vacuum magnetic shielding, and an antenna for global radio-frequency manipulation of trapped ions. The cold shield near 4 K is mechanically referenced to an underlying optical table via thermally insulating supports and exhibits root-mean-square vibrations less than 7.61(4) nm. Using the in-vacuum objective, we can detect 397 nm photons from a trapped $^{40}\mathrm{Ca}^{+}$ ion with 1.77% efficiency and achieve 99.9963(4)% single-shot state-detection fidelity in 50 $μ$s. To characterize the efficacy of the magnetic shields, we perform Ramsey experiments on the ground state qubit and obtain a coherence time of 24(2) ms, which extends to 0.25(1) s with a single spin-echo pulse. XY4 and XY32 dynamical decoupling sequences driven via the radio-frequency antenna extend the coherence to 0.72(2) s and 0.81(3) s, respectively.

Classical feature map surrogates and metrics for quantum control landscapes

2025-09-30T00:00:00Z

We derive and analyze three feature map representations of parametrized quantum dynamics, which generalize variational quantum circuits. These are (i) a Lie-Fourier partial sum, (ii) a Taylor expansion, and (iii) a finite-dimensional sinc kernel regression representation. The Lie-Fourier representation is shown to have a dense spectrum with discrete peaks, that reflects control Hamiltonian properties, but that is also compressible in typically found symmetric systems. We prove boundedness in the spectrum and the cost function derivatives, and discrete symmetries of the coefficients, with implications for learning and simulation. We further show the landscape is Lipschitz continuous, linking global variation bounds to local Taylor approximation error - key for step size selection, convergence estimates, and stopping criteria in optimization. This also provides a new form of polynomial barren plateaux originating from the Lie-Fourier structure of the quantum dynamics. These results may find application in local and general surrogate model learning, which we benchmark numerically, in characterizations of hardness in the problem instances, and for meta-parameter heuristics in quantum optimizers.

Pulse-to-Circuit Characterization of Stealthy Crosstalk Attack on Multi-Tenant Superconducting Quantum Hardware

2025-09-14T00:00:00Z

Hardware crosstalk in multi-tenant superconducting quantum computers constitutes a significant security threat, enabling adversaries to inject targeted errors across tenant boundaries. We present the first end-to-end framework for mapping physical pulse-level attacks to interpretable logical error channels, integrating density-matrix simulation, quantum process tomography (QPT), and a novel isometry-based circuit extraction method. Our pipeline reconstructs the complete induced error channel and fits an effective logical circuit model, revealing a fundamentally asymmetric attack mechanism: one adversarial qubit acts as a driver to set the induced logical rotation, while a second, the catalyst, refines the attack's coherence. Demonstrated on a linear three-qubit system, our approach shows that such attacks can significantly disrupt diverse quantum protocols, sometimes reducing accuracy to random guessing, while remaining effective and stealthy even under realistic hardware parameter variations. We further propose a protocol-level detection strategy based on observable attack signatures, showing that stealthy attacks can be exposed through targeted monitoring and providing a foundation for future defense-in-depth in quantum cloud platforms.

Learning to Restore Heisenberg Limit in Noisy Quantum Sensing via Quantum Digital Twin

2025-08-15T00:00:00Z

Quantum sensors leverage nonclassical resources to achieve sensing precision at the Heisenberg limit, surpassing the standard quantum limit attainable through classical strategies. However, a critical issue is that the environmental noise induces rapid decoherence, fundamentally limiting the realizability of the Heisenberg limit. In this Letter, we propose a quantum digital twin protocol to overcome this issue. The protocol first establishes observable-constrained state reconstruction to infer random errors in the decoherence process, and then utilizes reinforcement learning to derive adaptive compensatory control strategies. Demonstrated across discrete, continuous variable and multi-qubit circuit systems, our approach bypasses quantum state tomography's exponential overhead and discovers optimal control schemes to restore the Heisenberg limit. Unlike quantum error correction or mitigation schemes requiring precise noise characterization and ancillary qubits, our autonomous protocol achieves noise-resilient sensing through environment-adaptive control sequencing. This work establishes quantum digital twin as a generic methodology for quantum control, proposing a noise-immune paradigm for next-generation quantum sensors compatible with NISQ-era experimental constraints.

A multi-dimensional quantum estimation and model learning framework based on variational Bayesian inference

2025-07-30T00:00:00Z

The advancement and scaling of quantum technology has made the learning and identification of quantum systems and devices in highly-multidimensional parameter spaces a pressing task for a variety of applications. In many cases, the integration of real-time feedback control and adaptive choice of measurement settings places strict demands on the speed of this task. Here we present a joint model selection and parameter estimation algorithm that is fast and operable on a large number of model parameters. The algorithm is based on variational Bayesian inference (VBI), which approximates the target posterior distribution by optimizing a tractable family of distributions, making it more scalable than exact inference methods relying on sampling and that generally suffer from high variance and computational cost in high-dimensional spaces. We show how a regularizing prior can be used to select between competing models, each comprising a different number of parameters, identifying the simplest model that explains the experimental data. The regularization can further separate the degrees of freedom, e.g. quantum systems in the environment or processes, which contribute to major features in the observed dynamics, with respect to others featuring small coupling, which only contribute to a background. As an application of the introduced framework, we consider the problem of the identification of multiple individual nuclear spins with a single electron spin quantum sensor, relevant for nanoscale nuclear magnetic resonance. With the number of environmental spins unknown a priori, our Bayesian approach is able to correctly identify the model, i.e. the number of spins and their couplings. We benchmark the algorithm on both simulated and experimental data, using standard figures of merit, and demonstrating that we can estimate dozens of parameters within minutes.

Digital Twin Simulations Toolbox of the Nitrogen-Vacancy Center in Diamond

2025-07-24T00:00:00Z

The nitrogen-vacancy (NV) center in diamond is a crucial platform for quantum technologies, where its precise numerical modeling is indispensable for the continued advancement of the field. We present here a Python library for simulating the NV spin dynamics under general experimental conditions, i.e. a digital twin. Our library accounts for electromagnetic pulses and other environmental inputs, which are used to solve the system's time evolution, resulting in a physical output in the form of a quantum observable given by fluorescence. The simulation framework is based on a non-perturbative time-dependent Hamiltonian model, where the states initialization and readout are postulated from the interaction with optical fields. By eliminating oversimplifications such as the adoption of rotating frames for the microwave and radio frequency fields, our simulations reveal subtle dynamics emerging from realistic pulse constraints. The software is illustrated with three examples and validated by comparing the simulations with experimental reports, relevant to the fields of quantum computing (conditional logic gates), sensing (dynamical decoupling sequences with coupled spins) and networks (state teleportation). Overall, this digital twin delivers a robust numerical modeling of the NV spin dynamics, with simple and accessible usability, which can be used for a wide range of applications.

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems

2025-07-16T00:00:00Z

Accurate characterization of quantum systems is essential for the development of quantum technologies, particularly in the noisy intermediate-scale quantum (NISQ) era. While traditional methods for Hamiltonian learning and noise characterization often require extensive measurements and scale poorly with system size, machine learning approaches offer promising alternatives. In this work, we extend the inverse physics-informed neural network (referred to as PINNverse) framework to open quantum systems governed by Lindblad master equations. By incorporating both coherent and dissipative dynamics into the neural network training, our method enables simultaneous identification of Hamiltonian parameters and decay rates from noisy experimental data. We demonstrate the effectiveness and robustness of the approach through numerical simulations of two-qubit open systems. Our results show that PINNverse provides a scalable and noise-resilient framework for quantum system identification, with potential applications in quantum control and error mitigation.

High-throughput spin-bath characterization of spin-defects in semiconductors

2025-06-24T00:00:00Z

Detailed knowledge of the local environments of spin-defects in semiconductors, such as nitrogen vacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, a direct experimental characterization of individual defect environments is not scalable, as spin bath measurements are extremely time consuming. In this work, we address the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin-defects from sparse experimental coherence signals, which can be obtained in hours. To address the challenge to determine the number of isotopic nuclear spins along with their hyperfine couplings, we employ a trans-dimensional Bayesian approach that incorporates ab initio data. This approach provides posterior distributions of the numbers, hyperfine couplings, and locations of nuclear spins present in the sample. In addition to enabling high-throughput screening of spin-defects, we demonstrate how this trans-dimensional Bayesian approach can guide experimental design for dynamical decoupling experiments to detect nuclear spins within targeted hyperfine coupling regimes. While the primary focus is on accelerating spin-defect characterization, this Bayesian approach also lays the foundation for digital twin studies of spin-defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin-defects in semiconductors for quantum sensing and information applications.

Quantum Engineering of Qudits with Interpretable Machine Learning

2025-06-16T00:00:00Z

Higher-dimensional quantum systems (qudits) offer advantages in information encoding, error resilience, and compact gate implementations, and naturally arise in platforms such as superconducting and solid-state systems. However, realistic conditions such as non-Markovian noise, non-ideal pulses, and beyond rotating wave approximation (RWA) dynamics, pose significant challenges for controlling and characterizing qudits. In this work, we present a machine-learning-based graybox framework for the control and noise characterization of qudits with arbitrary dimension, extending recent methods developed for single-qubit systems. Additionally, we introduce a local analytic expansion that enables interpretable modelling of the noise dynamics, providing a structured and efficient way to simulate system behaviour and compare different noise models. This interpretability feature allows us to to understand the mechanisms underlying successful control strategies; and opens the way for developing methods for distinguishing noise sources with similar effects. We demonstrate high-fidelity implementations of both global unitary operations as well as two-level subspace gates. Our work establishes a foundation for scalable and interpretable quantum control techniques applicable to both NISQ devices and finite-dimensional quantum systems, enhancing the performance of next-generation quantum technologies.

Impact and mitigation of Hamiltonian characterization errors in digital-analog quantum computation

2025-05-06T00:00:00Z

Digital-analog is a universal quantum computing paradigm which employs the natural entangling Hamiltonian of the system and single-qubit gates as resources. Here, we study the stability of these protocols against Hamiltonian characterization errors. For this, we bound the maximum separation between the target and the implemented Hamiltonians. Additionally, we obtain an upper bound for the deviation in the expected value of an observable. We further propose a protocol for mitigating calibration errors which resembles dynamical-decoupling techniques. These results open the possibility of scaling digital-analog to intermediate and large scale systems while having an estimation on the errors committed.

Enhanced Hamiltonian Learning Precision with Multi-Stage Neural Networks

2025-03-10T00:00:00Z

Learning quantum Hamiltonians with high precision is important for quantum physics and quantum information science. We propose a multi-stage neural network framework that significantly enhances Hamiltonian learning precision through successive network optimization of residual errors. Our approach utilizes time-series data from single-qubit Pauli measurements of random initial states, enabling the estimation of unknown Hamiltonian parameters without prior structural assumptions. We demonstrate the framework on two-qubit systems, achieving orders-of-magnitude improvement in parameter accuracy, and further extend the method to larger systems by integrating dynamical decoupling techniques. Additionally, the protocol exhibits robustness against experimental noise. This work bridges the gap between scalable Hamiltonian learning and high-precision requirements, offering a practical tool for precise quantum control and metrology.