ABSTRACT A confinement‐engineered design framework is developed for CdSe quantum‐rod qubits by treating the radius and aspect ratio as independent control parameters. The qubit is encoded in the two lowest axially confined orbital states; finite‐barrier and dielectric‐mismatch corrections () shift the coherence ridge by only nm without altering the Pareto topology, as confirmed by a new corrected sub‐panel (Figure 3b). Phonon‐induced relaxation is modeled through deformation‐potential, piezoelectric, and Fröhlich channels within a Born–Markov framework whose validity is verified throughout the design space. Mapping the full plane reveals a coherence ridge near nm, , where the phonon‐relaxation‐limited approaches 100 ns at 4 K as an upper bound. The optically driven Rabi frequency increases with rod length, producing a Pareto trade‐off between coherence and gate speed. A four‐parameter sensitivity analysis (Table and Figure ) confirms the ridge and Pareto topology are robust to parameter variations. A Monte Carlo analysis with log‐normal size distributions (Figure ) validates the fabrication robustness metric and confirms the target nm, . A speed‐favored Pareto knee near nm, achieves GHz with ns and gate fidelities –0.99 after DRAG pulse shaping. An experimental control protocol and scalability limitations are discussed.
ABSTRACT A confinement‐engineered design framework is developed for CdSe quantum‐rod qubits by treating the radius and aspect ratio as independent control parameters. The qubit is encoded in the two lowest axially confined orbital states; finite‐barrier and dielectric‐mismatch corrections () shift the coherence ridge by only nm without altering the Pareto topology, as confirmed by a new corrected sub‐panel (Figure 3b). Phonon‐induced relaxation is modeled through deformation‐potential, piezoelectric, and Fröhlich channels within a Born–Markov framework whose validity is verified throughout the design space. Mapping the full plane reveals a coherence ridge near nm, , where the phonon‐relaxation‐limited approaches 100 ns at 4 K as an upper bound. The optically driven Rabi frequency increases with rod length, producing a Pareto trade‐off between coherence and gate speed. A four‐parameter sensitivity analysis (Table and Figure ) confirms the ridge and Pareto topology are robust to parameter variations. A Monte Carlo analysis with log‐normal size distributions (Figure ) validates the fabrication robustness metric and confirms the target nm, . A speed‐favored Pareto knee near nm, achieves GHz with ns and gate fidelities –0.99 after DRAG pulse shaping. An experimental control protocol and scalability limitations are discussed.
Operating superconducting qubits at dynamical sweet spots (DSSs) suppresses decoherence from low-frequency flux noise. A key open question is how long coherence can be extended under this strategy and what fundamental limits constrain it. Here we introduce a fully parameterized, multi-objective periodic-flux modulation framework that simultaneously optimizes energy relaxation T1 and pure dephasing Tφ, thereby quantifying the tradeoff between them. For fluxonium qubits with realistic noise spectra, our method enhances Tφ by a factor of 3-5 compared with existing DSS strategies while maintaining T1 in the hundred-microsecond range. We further prove that, although DSSs eliminate first-order sensitivity to low-frequency noise, relaxation rate cannot be reduced arbitrarily close to zero, establishing an upper bound on achievable T1. At the optimized working points, we identify double-DSS regions that are insensitive to both DC and AC flux, providing robust operating bands for experiments. As applications, we design single- and two-qubit control protocols at these operating points and numerically demonstrate high-fidelity gate operations. These results establish a general and useful framework for Pareto-front engineering of DSSs that substantially improves coherence and gate performance in superconducting qubits.
Quantum computing is rapidly evolving from theoretical frameworks to functional hardware, with physical qubits now being realized across multiple experimental platforms. Precision nanofabrication has enabled the development of low-loss, high-coherence quantum devices. Among the various approaches, superconducting circuits and trapped-ion systems have emerged as the most advanced and experimentally successful platforms for realizing controllable and scalable qubits. This review presents an overview of nanoscale fabrication techniques employed in these two architectures. We first outline the operating principles that define qubit behavior, followed by a discussion of representative device designs and fabrication workflows. In superconducting circuits, we examine processes such as thin-film deposition, lithographic patterning, and Josephson-junction fabrication that determine key parameters including qubit frequency, anharmonicity, and coherence. For trapped-ion systems, we describe the transition from bulk electrode assemblies to microfabricated surface traps produced using photolithography, metal deposition, and dielectric layering, where electrode geometry and surface treatment directly influence ion confinement and motional heating. We further discuss fabrication-related challenges, including dielectric loss, surface contamination, and material defects that limit coherence and reproducibility, along with emerging mitigation strategies such as advanced cleaning, low-loss materials, and improved surface engineering. By drawing parallels between these two leading platforms, this review highlights that continued progress in nanofabrication remains central to realizing scalable, high-fidelity quantum processors.
Multi‐objective optimization in quantum systems poses formidable challenges stemming from the high dimensionality, complex constraints, and conflicting performance metrics inherent to quantum control tasks and circuit synthesis. This study presents a comprehensive exploration of genetic AI algorithms—evolutionary strategies inspired by natural selection—tailored specifically for multi‐objective optimization in quantum computing contexts. We propose a hybrid framework that synergistically combines the elitist, Pareto‐based selection mechanics of NSGA-II with quantum‐inspired crossover and mutation operators designed to preserve entanglement structures and exploit hardware‐specific gate sets. In this framework, candidate solutions encoding circuit topologies or control‐pulse parameters undergo iterative evolution, guided by fitness vectors that balance gate fidelity, circuit depth, and resource overhead. To benchmark performance, we apply the algorithm to canonical quantum computing problems: Fourier transform circuits, QAOA ansätze for MaxCut instances, and random Clifford+T circuits. Through extensive simulation on noise-modelled quantum devices, we evaluate Pareto front coverage, convergence speed, diversity maintenance, and robustness under decoherence. Our results demonstrate that the proposed genetic AI algorithm outperforms classical single‐objective genetic methods and established multi‐objective evolutionary algorithms (e.g., SPEA2) by achieving broader Pareto fronts and faster convergence rates, while maintaining solution diversity. Furthermore, noise-resilience experiments show graceful degradation, indicating practical applicability on near-term quantum hardware. These findings substantiate the potential of evolutionary computation paradigms—augmented with quantum‐inspired operators—to address the intricate trade-off surfaces that define NISQ-era optimization problems, paving the way toward automated quantum circuit design and control parameter tuning for real-world quantum computing applications.
Atom-like emitters in solids have emerged as promising platforms for quantum sensing and information processing. Among the major challenges are inhomogeneities in emitter fine structure, which complicates quantum control. Here, we introduce a framework that leverages this emitter diversity to simplify the experimental resources needed to create optically heralded spin cluster states across N q emitters from the conventional order O ( N q ) to O ( 1 ) within ensembles of N q ∼ 10 – 100 . Specifically, the optimized pulse sequence simultaneously corrects parameter variations (pulse-length error and frequency detuning error), achieving single-qubit gate fidelities exceeding 99.99% for errors (normalized relative to Rabi-drive strength) up to 0.3, while maintaining fidelities above 99% even for errors as large as 0.4. Applying this optimized pulse sequence in the form of a Carr-Purcell-Meiboom-Gill (CPMG) based dynamical decoupling protocol to the dominant noise spectral density of silicon-vacancy centers in diamond, our approach enhances ensemble-average coherence times by more than a factor of 7 relative to interleaved bang-bang-based CPMG. For state-of-the-art dilution refrigeration systems, we further estimate sharply reduced heating when driving a global resonant optimal dynamical decoupling across N q silicon-vacancy spins, potentially resolving the current trade-off between spin coherence and scaling to N q ≫ 1 . We further introduce a modified single-photon entanglement protocol with an efficient algorithm for deterministic entanglement compilation. Depending on the decoupling window, our method yields order O ( 10 2 – 10 4 ) more entanglement links than bang-bang sequences, with theoretical guarantees of order Ω ( N q ) unique links—improvable via control-parameter tuning. Our approach thus offers enhanced fidelity, scalability, and robustness. Together, these techniques provide foundational tools—including global unitary control, phase denoising, remote entanglement, and compilation—for scalable quantum computing architectures based on heterogeneous spin ensembles.
Abstract Fabrication-induced variation in Josephson junction parameters ---shunt capacitance~C, normal-state resistance~$\RN$, and superconducting gap~$\Dgap$---introduces uncertainty in transmon qubit frequencies and leads to residual ZZ crosstalk that limits multi-qubit gate fidelity. This work presents a quantitative, end-to-end simulation framework that maps junction-level disorder to GHZ state fidelity in a three-qubit fixed-coupling transmon array. Five concrete results are obtained. (1)~An analytic pipeline is constructed linking $(C,\RN,\Dgap)$ to qubit frequency ω01 and detuning Δ, enabling direct propagation of fabrication noise into gate-level error rates. (2)~Monte Carlo analysis with 2\% parameter variation shows that normal-state resistance dominates detuning uncertainty, producing σΔ=0.113\,GHz---approximately 1.4× larger than variations from C or $\Dgap$. (3)~Two error models are implemented and compared: a probabilistic depolarizing model and a coherent ZZ model using the anharmonicity-corrected dispersive shift ζZZ=g2(1/Δ−1/(Δ+α)). Because Model 1 encodes only the \emph{magnitude} of the coupling-induced error while Model 2 encodes its \emph{signed, oscillatory phase}, the two models are not interchangeable estimates of the same quantity: their predictions diverge by up to ∣ΔF∣≈0.66 in GHZ fidelity near the boundary of the perturbation-validity exclusion zone, with Model 2 alone capturing the periodic revivals in fidelity that a magnitude-only stochastic model cannot represent by construction. (4)~The coherent model identifies the breakdown of second-order perturbation theory at Δ≈∣α∣=0.248\,GHz, which, combined with fabrication-induced detuning spread, yields a conservative model-avoidance region Δ∈[0.02,0.47]\,GHz within which non-perturbative treatment is required for quantitative characterization. (5)~Model-selection guidance is established: the probabilistic model provides a conservative bound for threshold estimation, while the coherent model is required for accurate hardware characterization and mitigation design. Finally, the framework is structured to allow systematic extension to more realistic noise channels, including leakage, correlated fabrication disorder, and non-perturbative multilevel dynamics, providing a pathway toward experimentally validated device-level error modeling.
ABSTRACT Quantum‐dot qubits offer strong tunability, but their sensitivity to geometry, external fields, and model parameters makes decoherence prediction computationally demanding. Here, we present a physics‐informed surrogate modeling framework to construct geometry‐aware decoherence maps for tunable quantum‐dot qubits. Using an effective two‐level model with parabolic and triangular confinement and a physically motivated phenomenological Lindblad‐rate model, we compute the transition frequency, an entropy‐based information metric, and a coherence‐time proxy over a controlled multidimensional parameter sweep. The resulting 250‐sample dataset is used to train two compact, toolbox‐free surrogate models: a quadratic polynomial model and a Gaussian radial basis function surrogate. Both reproduce the physics model with under 5‐fold cross‐validation and provide 260–950 faster predictions than direct diagonalization. A composite operating‐point score is evaluated across 66 weighting choices to assess robustness. Within this simplified model, near‐parabolic confinement yields a more stable spectral response and broader robust operating region, whereas triangular distortion enhances electric‐field tunability but narrows the coherence‐preserving window. The electric field is the dominant control parameter, while the magnetic field has a weaker secondary role. These results illustrate a transferable surrogate‐assisted strategy for rapid qubit design‐space exploration.