Abstract Tuning gate-defined quantum dots for qubit operation is an often frustrating and time-consuming activity in spin qubit research due to inherent device variability and the complex interactions between dots. Furthermore, tuning these devices grows more complicated as quantum computers grow to utility-scale. Computer-automated and machine learning techniques are popular approaches to accelerate the tuning process. However, these techniques need to become much faster and more accurate to address the tuning of millions of qubits in utility-scale quantum computing. We examine the literature of computer-automated and machine learning spin qubit tuning algorithms, grouping the algorithms by similar tasks and analyzing them from a computer science perspective to keep necessary background knowledge of quantum computing/physics at a minimum. We look at which approaches seem propitious for scaling up to utility-scale systems and where computer scientists could potentially have the most impact.
Abstract We translate the one-mode unitary-dilation framework for nonunitary linear dynamics into a gate-level hybrid oscillator--qubit architecture. An ancillary oscillator encodes the integral kernel through state preparation and postselection. A qubit register represents and simulates the discretized system operator. The construction applies to time-independent dynamics \(\dot u=-(L+iH)u\), including discretized partial differential equations, and removes the \(\mathcal{O}(\log M_a)\) ancilla-qubit overhead of a discrete-variable (DV) \(M_a\)-term quadrature register. We bound the squeezed-Fock coefficient-projection error of the ideal kernel state. It decays superalgebraically with cutoff \(N\) for Schwartz-class kernels and at a stretched-exponential rate under stronger joint decay and smoothness assumptions. The finite squeezed-Fock kernel state generically has stellar rank \(N-1\), making \(N\) a discrete measure of the oracle's non-Gaussian resource. For hybrid oscillator--qubit evolution, a \(p\)th-order product formula requires \(\mathcal{O}(t^{1+1/p}N_{\mathrm{Fock}}^{(p+1)/(2p)}\epsilon_t^{-1/p})\) Trotter steps in the worst case, up to generator-dependent commutator factors, to reach error \(\epsilon_t\), where \(N_{\mathrm{Fock}}\) is the oscillator dimension. A perturbation bound separates the total scaled-map error from the physical postselection probability. We benchmark Law--Eberly synthesis and assess a variational SNAP+\(\mathcal D\) route at the state-preparation level on discretized heat-equation instances. For full circuit-level maps of one-dimensional heat and non-normal advection--diffusion instances up to \(D=32\), the fixed-scale map error is at most \(1.48\%\) and the conditional infidelity at most \(4.68\times10^{-4}\) over all computational-basis inputs. At kernel parameters selected on the one-dimensional family, a \(4\times4\) two-dimensional stress case reaches \(7.40\%\) fixed-scale error under a reference norm shrunk by the stronger two-dimensional damping, with worst-input conditional infidelity \(6.20\times10^{-3}\). At the prescribed DV sizing, the hybrid CV--DV route has smaller fixed-scale error in all ten instances. The DV route accepts with fewer repetitions in every instance. These results provide a block-by-block finite-size resource account of when a single continuous qumode can replace a discretized ancilla register.
Abstract Quantum errors in noisy environments remain a major obstacle to advancing quantum information technology. Standard quantum error correction requires massive ancillary qubit overhead, motivating the need for hardware-efficient mitigation strategies. In this work, we propose a framework for dynamical quantum error correction at the circuit level, requiring no logical encoding or ancillary qubits. By extending a geometric framework—originally developed for creating dynamical error-correcting gates at the control pulse level —to the discrete dynamics of digital circuits, we map the accumulation of coherent errors to trajectories in a high-dimensional error space. We demonstrate that inserting deterministically optimized twirling gate sequences actively shapes these trajectories, utilizing destructive interference to keep the accumulated error bounded with error scaling $\mathcal{O}(1)$. This deterministic path-shaping suppresses circuit errors fundamentally differently than the stochastic random-walk behavior of standard randomized compiling with error scaling $\mathcal{O}(\sqrt{N})$. Furthermore, we show that this circuit-level dynamical correction synergizes with pulse-level robust control, providing an analytical bridge between continuous noise dynamics and discrete quantum compilation. This research illuminates pathways to achieving highly noise-resistant quantum circuits prior to the era of fault tolerance.
Abstract Quantum algorithms require encoding classical vectors as quantum states, a step known as amplitude encoding. General-purpose routines produce circuits with O ( 2 m ) gates for vectors of length N = 2 m , for an m -qubit register. However, vectors arising in scientific and engineering applications often exhibit mathematical structure that admits far more efficient encoding. Theoretical work over the last decade has established efficient circuits for several structured vector classes, but without open-source implementations. We present PyEncode , an open-source Python library that implements this body of theory in a unified framework. It covers ten exact pattern families: sparse, step, square, Walsh, Fourier, geometric, Hamming, staircase, Dicke , and polynomial . A function encode maps each pattern to a verified Qiskit circuit, with no vector materialization and no approximation; for example, encode(SPARSE([(19, 1.0)]), N = 64) encodes the vector e 19 of length N = 64 . Sparse, step, Walsh, Hamming, and staircase patterns require O ( m ) gates; square and Fourier patterns require O ( m 2 ) ; Dicke states | D k m ⟩ require O ( k ( m − k ) ) , that denotes uniform superpositions over indices of Hamming weight k ; degree- d polynomials require O ( m d + 1 ) . A companion predict_gates function estimates transpiled gate counts without synthesis, and a reverse-lookup utility match_vector identifies which family best fits a given numerical vector. Three composition primitives are supported: SUM for weighted superpositions, PARTITION for ancilla-free composition of disjoint-support patterns, and TENSOR for separable states over disjoint subregisters. For amplitude vectors outside these exact families, PyEncode also provides a matrix product state (MPS) loader, encode_mps for approximate vector encoding. The library is available at https://github.com/UW-ERSL/PyEncode .
Abstract Quantum reservoir computing (QRC) offers a powerful approach to exploit the rich dynamics of quantum systems for information processing. However, the computational performance of conventional Hermitian reservoirs is inherently constrained by their nonlinearity and information-spreading ability. In this work, we propose a non-Hermitian QRC in which non-Hermitian dynamics are employed as a tunable resource to significantly enhance the QRC performance. By incorporating an imaginary interaction term into the one-dimensional XY spin model, the reservoir's information propagation extends beyond the Lieb-Robinson bound, resulting in accelerated information scrambling. Through spectral analysis and memory evaluation, we demonstrate that the non-Hermitian reservoir can be tuned toward the edge of chaos by varying a single parameter that controls the non-Hermitian strength. This tuning optimizes both memory and computational capacities, which are crucial for processing temporal sequences. For applications, we evaluate the predictive performance of both classical and quantum chaotic time series. Our results demonstrate superior performance compared with the Hermitian counterpart, with particularly notable advantages in predicting signals generated by the Sachdev-Ye-Kitaev model.
Abstract The search for quantum-like wave formulations of the Navier–Stokes equations (NSEs), here referred to as Schrödinger–Navier–Stokes (SNS) equation, has attracted increasing attention in recent years because of its potential application in the simulation of classical dissipative fluids on quantum computers. An SNS formulation of classical fluids was first presented in a largely unnoticed paper by Dietrich and Vautherin in 1985 [Sur l’équivalence entre des types particuliers des équations de Navier–Stokes et de Schrödinger non linéaire J. Phys. 46 313–6]. In this paper, we revisit this SNS formulation and assess its suitability for quantum implementation based on Carleman linearization. Specifically, we (i) clarify why the non-polynomial dissipative and quantum-pressure terms of the SNS equation obstruct a direct Carleman treatment and reformulate the dynamics as a Navier–Stokes–Hamilton–Jacobi (NSHJ) system; (ii) develop a corresponding quantum algorithm based on Carleman linearization of the NSHJ equations, referred to as Carleman–Hamilton–Jacobi (CHJ), together with a tensor-network representation that substantially reduces the memory requirements of its classical emulation; and (iii) emulate the CHJ dynamics on a classical computer and analyze its convergence and accuracy for Kolmogorov-like flows at moderate Reynolds numbers. To the best of our knowledge, this is the first quantum algorithm based on a quantum-like wave formulation of the full NSE, including pressure, dissipation and vorticity.
Quantum sensors promise measurement sensitivities that can scale at the Heisenberg limit, but in practice their performance is often degraded by noise, finite sampling, and implementation imperfections. In this work we present a general framework for improving parameter estimation in such settings by exploiting intrinsic structural constraints of time-domain correlation functions. Our approach builds on the observation of Kemper et al. [PRL 132, 160403 (2024)] that two-time correlation functions of Hermitian observables generate Gram matrices that are positive semidefinite, a property that can be violated in experimentally acquired data. We formulate signal reconstruction as a convex optimization problem that enforces positive semidefiniteness, Toeplitz structure, and low-rank priors motivated by the underlying dynamics. We show analytically that, under suitable conditions, the ground-truth signal can be uniquely identified in the noiseless case and recovered stably in the presence of noise. We further demonstrate numerically, in a GHZ-based magnetometry protocol, that enforcing these physical constraints can significantly improve frequency estimation from sparse and noisy data. In particular, we observe a clear advantage in the data-starved regime, where only a small number of time samples are available and standard spectral estimation methods, including matrix pencil techniques, provide limited or unstable improvement over direct fitting. While the reconstructed signals do not in general reach the shot-noise-limited performance, the proposed approach consistently reduces estimation error and recovers much of the underlying structure of the signal. These results indicate that incorporating universal physical constraints into data analysis can enhance the practical performance of quantum sensing protocols without requiring additional hardware resources or calibration.
Programmable quantum control systems increasingly rely on predictive modules for certification, real-time feedback, and autonomous decision-making. This development raises a fundamental question: can self-analyzing quantum platforms universally predict their own experimental outcomes? Wolpert formalized a general impossibility of universal self-prediction. Here we translate that limitation into an explicit laboratory obstruction that can be realized with finite resources. We consider settings with programmable quantum control in which predictors can be embedded as subroutines within the experiments they analyze. Our diagonal construction uses Kleene's recursion theorem to transform any deterministic bounded-time predictor into a reversible protocol encoding its own specification. The resulting protocol invokes the predictor on that specification and deterministically produces a classical pointer record that contradicts the forecast. For efficient predictors, the compilation has polynomial overhead and admits concrete physical realizations as a fault-tolerant quantum circuit and as a minimal Mach-Zehnder interferometer. These realizations connect computability-theoretic self-reference to programmable quantum hardware. We also introduce and formally define Gödel-safe architectures. These architectures block the forbidden causal path from the protocol description to an actuator that can affect the pointer during the same run. We analyze their implications for real-time quantum error correction, including the resulting expressiveness trade-offs. As quantum control loops grow in computational expressiveness, the limits of self-reference cease to be mere mathematical abstractions and become explicit engineering constraints for the reliable operation of autonomous quantum technologies.
Flat-band systems offer a uniquely powerful tool for quantum control in dynamics due to their characteristic feature of having a dispersionless energy band. Simulating such highly sensitive systems on current digital quantum computers is a challenging task, due to the intrinsic limitations of the noisy intermediate-scale quantum (NISQ) devices. Here we present high-fidelity digital quantum simulations of flat-band (FB) and all-bands-flat (ABF) lattices, using an advanced tensor network based variational optimization approach to compress the circuit depth. With the compressed quantum circuits, we first explore single-particle dynamics and observe two distinct behaviours: strong localization in ABF lattices and delocalization in FB lattices. By integrating FB and ABF lattices into a one-dimensional hybrid structure, we achieve controllable quantum transport, where the ABF lattice acts as a quantum switch. Extending to two-particle dynamics, we show that transport remains controllable by tuning the hopping amplitude alone, even in the presence of interactions. These results establish flat-band engineered systems as a promising pathway for scalable control of quantum transport in emerging quantum technologies, with potential applications in qubit isolation, particle trapping, and state transfer.
This paper constructs the first efficient implementation of a quantum wavelet packet transform with a ‘parabolic scaling’ tree structure, sometimes called a quantum wave atom transform. Classically, wave atom transforms are used to construct sparse representations of differential operators, which enable fast classical algorithms for solving wave equations. Compared to previous work on quantum wavelet transforms, our quantum algorithm can implement a larger class of wavelet and wave atom transforms, by using an efficient representation for a larger class of possible tree structures. Our quantum implementation has O ( poly ( n ) ) gate complexity for applying a transform of dimension 2 n , while classical implementations use O ( n 2 n ) floating point operations. This is potentially useful for designing quantum algorithms for solving wave equations that achieve an exponential speedup over classical algorithms.
The generation of high-purity, coherent qubits is essential for quantum technologies. Free electron wavepackets are a promising platform, but their development into qubits has been hindered by the multi-level sideband structure generated in standard laser-electron interactions. Here, we overcome this fundamental limitation by demonstrating the distillation of a pristine flying electron qubit with over 99% purity from such a multi-level state. Through a sequence of coherent laser modulations, we engineer quantum interference to distill the electron state, coherently suppressing all except two energy sidebands and confining the population to a genuine two-level system. We further demonstrate coherent control of the relative population and phase, enabling balanced coherent superpositions that are valuable for quantum interference applications. Our work establishes distillation as a general route to realize pristine flying-electron qubits encoded in discrete momentum states.
Micromachined vapor cells have revolutionized chip-scale quantum sensors, including magnetometers and atomic clocks. In parallel, Rydberg-atom quantum sensing has emerged as a powerful platform for broadband, non-invasive and ultra-sensitive electrometry, enabling compact atom-based antenna elements for electromagnetic reception, often referred to as quantum antennas. Yet, to date, Rydberg sensing has largely been limited to glass-blown, cm-scale vapor cells. Here, we perform Rydberg spectroscopy and electrometry using a wafer-scale-fabricated Pyrex-Si-Pyrex cell with millimeter-scale dimensions. The Rydberg spectroscopic line is characterized with respect to critical parameters such as temperature, the frequency and amplitude of the applied radio frequency (RF) field, light intensity, and the spatial position of the interrogating beam. Our study reveals lineshapes directly influenced by a complex landscape of electrostatic fields with values up to approximately 0.6 V cm -1. By controlling key parameters, we were able to reduce the effect of these internal electric fields, and demonstrate the detection of RF fields assessed using the Autler-Townes splitting with a minimum detectable field of 20 mu V cm -1 . Our results highlight the potential of micromachined vapor cells for subwavelength electromagnetic field measurements, with applications in communications, near-field RF imaging, and chip-scale quantum technologies.
Quantum neural networks are increasingly distributed and deployed as third-party components, creating a supply-chain attack surface in which backdoors can be implanted during training yet remain difficult to detect under strict black-box constraints. In the probability-only minimal-interface regime, defenders often observe only post-readout class probabilities (or finite-shot frequency estimates), while shot noise and limited trusted clean data can obscure the small, targeted distribution shifts induced by poisoning. A trigger-agnostic black-box detector for this setting is proposed. The method elicits dose-response evidence by mixing a trusted clean pool with a suspect pool at progressively larger mixing ratios and tracking class-wise uplifts in predicted-class rates. To separate trigger-induced anomalies from static discrepancies between a model under test and a clean reference, we apply a difference-in-differences baseline alignment and stabilize the resulting response curves via Monte Carlo resampling under finite-shot sampling. Detection is formulated as a family of distribution-free one-sided sign tests across ratios and classes, with Holm-Bonferroni correction controlling the family-wise false-alarm rate; an effect-size gate yields a Detected/Pass verdict and a target-class estimate. We further characterize detection power as a function of the (unknown) prevalence of triggered inputs in the suspect pool. Experiments on 110 trained instances spanning quantum fast gradient sign method, quantum universal adversarial perturbation (QUAP), and patch-based backdoors on four-class MNIST subsets achieve an area under the receiver operating characteristic curve of 0.954 and an average precision under the precision-recall curve of 0.986. At the default operating point, the detector attains 0.906 sensitivity and 0.840 specificity, with most residual false alarms concentrated in QUAP clean controls under benign distribution shift.
Designing superconducting quantum circuits involves optimizing the layout to achieve certain target parameters. This optimization process usually depends on iterative electromagnetic simulations, which are computationally expensive and require manual intervention to adjust the layout parameters. Here, we present a method to efficiently automate the optimization of superconducting circuits, which significantly reduces the need for manual intervention. The method's efficiency arises from approximate nonlinear model-driven (ANMod) parameter updates, which are constructed from the user's physical knowledge. Additionally, we provide a full implementation using the ANMod-method as an open-source Python package, QDesignOptimizer. The package automates the design workflow by combining high-accuracy electromagnetic simulations in ansys HFSS and energy participation ratio (pyEPR) analysis integrated with the design tool quantum-metal (formerly known as Qiskit-Metal). Our implementation supports modular and flexible subsystem-level analysis and is easily extensible to optimize for additional parameters. The ANMod-method is not specific to superconducting circuits; as such, it can be applied to a range of nonlinear optimization problems across science and technology.
Integrating quantum key distribution (QKD) into optical networks is a crucial step toward the adoption of quantum technologies in existing telecommunication fiber infrastructures. However, state-of-the-art solutions face significant challenges, including sensitivity to classical noise, particularly spontaneous Raman scattering, limited transmission distances, and varying network conditions. In this work, we present a novel discrete-variable QKD system running a time-bin BB84 protocol operating in the O-band (1295.56 nm), where the impact of dominant noise sources is effectively reduced. Combined with passive narrow-spectral filtering at the receiver, our system demonstrates robustness across diverse dense wavelength-division multiplexing scenarios, making it practical for real-world deployments. We validate its performance through extensive testing, showing stable key generation coexisting with different classical traffic conditions up to 17 dBm of total launch power. These results represent a significant advancement toward the integration of QKD into existing fiber networks, paving the way for secure quantum communication on a large scale.
Abstract We study the information physics of quantum trajectories based on weak measurements in order to address the optimal achievable performance in qubit configuration readout for two realistic models of single qubit readout: (i) Model I is informationally complete, but without intrinsic dynamics; (ii) Model II is informationally incomplete weak measurements with intrinsic dynamics. We use mutual information (MI) to characterize how much information about the initial state is encoded in the measurement record. Using a fixed discrete time-step formulation, we compute the MI while varying the measurement strength, duration of measurement record, and the relative strength of intrinsic dynamics in our measurement schemes. We observe and exploit the emergence of continuum scaling and the Stochastic master equation in the weak measurement limit. We develop a perturbative analytic expansion in the measurement efficiency parameter to calculate MI, which captures qualitative and quantitative features of the numerical data. Both models exhibit clear bounds on information extraction as limiting values of the scaling function. Our analysis obtains these bounds and also flags optimal conditions on measurement strength and/or duration required to saturate them, as determined by intrinsic precessional dynamics (in Model II). Our results should be useful both for quantum device operation and optimization and also, possibly, for improving the performance of recent machine learning approaches for qubit and multiqubit configuration readout in current Noisy intermediate-scale quantum experiment regimes.
Solid-state quantum technologies, including qubits and quantum metrology circuits, demand milli-Kelvin operation to preserve fragile quantum states from classical noise. While the negligible electron-phonon coupling is the major impediment, reaching 50 mK electron temperature is further suffered by the high electrical resistance and sub-micron-scale dimensions of typical devices, limiting conventional heat dissipation. Though the phonons are effectively frozen, thermoelectric techniques could offer a viable path for heat management.This work explores thermally driven electrical transport in a gated quantum dot (QD) on a GaAs-AlGaAs two-dimensional electron gas (2DEG), to control heat flow between the source and drain reservoirs.By exploiting the QD's discrete energy spectrum and tuneable tunnel rates, a precise control over the polarity and magnitude of the resulting thermoelectric current is demonstrated. A temperature difference of 650 mK is maintained across the QD, a separation of 400 nm, by tuning the tunnel-rates. An experimental gate pulsing method is also introduced to directly measure the electron temperature differences across the QD, bypassing the need for any theoretical fits. The results presented here show that tuneable tunnel barriers can be used for local heat control, and could lead to advanced quantum refrigerators that work efficiently in mesoscopic circuits.
Satellite quantum key distribution technology has developed rapidly using near-infrared wavelengths and is expected to enable global quantum communication. However, link availability is still hampered by detrimental effects in the free-space channel, such as background noise from solar radiation and attenuation from turbulence and weather such as haze and fog. One potential mitigation technique is to move to the mid-infrared atmospheric transmission window (3–5 µ ms) where background noise and turbulence effects are significantly reduced. While mid-infrared quantum technology is not as well developed, advancements in mid-infrared entangled photon pair generation and nonlinear upconversion single-photon detectors could be poised to enable daytime satellite downlinks with increased reliability. This review compares the state of the art for quantum transmitters and receivers in the mid-infrared to the more established near-infrared technology. The goal is to identify gaps in transmitter and/or receiver technology in the mid-infrared, and to determine if the mid-infrared can offer significant advantages over the near infrared for quantum communication.
As a critical infrastructure for quantum internet, quantum entanglement distribution networks enable diverse quantum information applications. Among the existing architectures, the pump-management entanglement distribution network scheme exhibits remarkable scalability, functionality, and reconfigurability. However, its performance is hampered by the noise photons from concurrent spontaneous four-wave mixing (SFWM) processes and the unbalanced secure key rates (SKRs) across the network. Here, we propose a polarization manipulation scheme for pump-management entanglement distribution networks, enabling active control over the polarization states of the pump lasers and the generated single photons to optimize network performance. By utilizing orthogonally polarized pumps and polarization selection, the noise photons from other SFWM processes are effectively suppressed while preserving target entangled pairs, thereby significantly enhancing the SKR. Furthermore, our approach leverages the intrinsic efficiency differences of SFWM processes and a time-sharing method to achieve key rate equalization across the network. The performance enhancement is analyzed through theoretical analysis and numerical simulation. Our work resolves key bottlenecks in pump-management entanglement distribution networks, as well as establishes a powerful paradigm for performance optimization in future large-scale quantum networks.
The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data, by bringing together techniques from gradient-based optimization from machine learning with efficient quantum state representations in terms of tensor networks. Our approach is highly practical, experimentally friendly, and intrinsically scalable to allow for system sizes of above 100 spins. In particular, we demonstrate on synthetic data that the algorithm works even if one is restricted to one simple initial state, a small number of single-qubit observables, and time evolution up to relatively short times. For the concrete example of the one-dimensional Heisenberg model our algorithm exhibits an error constant in the system size and scaling as the inverse square root of the size of the data set.
