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arXiv:2603.13020

Structured Quantum Optimal Control under Bandwidth and Smoothness Constraints-An Inexact Proximal-ADMM Approach for Low-Complexity Pulse Synthesis

Ziwen Song

quant-phphysics.atom-phSubmitted Mar 13, 2026• Announced Mar 16, 2026• 0 citations
v1 · Mar 13, 2026v2 · Jul 11, 2026
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Abstract

Quantum optimal control is often judged by nominal fidelity alone, even though realistic pulse-design studies must also account for bandwidth, amplitude, and smoothness constraints. I study this structured-control regime with an inexact Proximal-ADMM framework that combines gate-infidelity minimization with $L_1$ sparsity, total-variation regularization, explicit band-limit projection, and box constraints in a single loop. The method is benchmarked against GRAPE, standard Krotov, and L-BFGS-B on a single-qubit $X$ gate, a leakage-prone qutrit task, and a two-qubit entangling gate. Across ten random seeds, Pareto scans, ablations, filtered-baseline fairness checks, significance analysis with false-discovery-rate correction, and robustness tests, the method is not a universal winner in either nominal fidelity or wall-clock cost. Its value is instead to expose and stabilize a low-complexity frontier of the fidelity-complexity landscape. After retuning the PADMM budgets and warm-start lengths, the qutrit and two-qubit structured fidelities rise to 0.6672 +- 0.0001 and 0.6342 +- 0.0003, respectively, while preserving markedly lower complexity than unconstrained quasi-Newton solutions. These values remain well below deployment-grade gate thresholds, so the contribution should still be read as a numerical framework for constrained pulse synthesis rather than as a finished route to immediately deployable high-fidelity gates. Training-time robust optimization yields only task-dependent gains, with the clearest effect appearing in qutrit drift robustness and amounting to a small absolute improvement. The results therefore position PADMM as a constraint-native framework for low-complexity frontier exploration, not as a replacement for unconstrained high-fidelity solvers.

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Quantum optimal control is often judged by nominal fidelity alone, even though realistic pulse-design studies must also account for bandwidth, amplitude, and smoothness constraints. I study this structured-control regime with an inexact Proximal-ADMM framework that combines gate-infidelity minimization with L1L_1L1​ sparsity, total-variation regularization, explicit band-limit projection, and box constraints in a single loop. The method is benchmarked against GRAPE, standard Krotov, and L-BFGS-B on a single-qubit XXX gate, a leakage-prone qutrit task, and a two-qubit entangling gate. Across ten random seeds, Pareto scans, ablations, filtered-baseline fairness checks, significance analysis with false-discovery-rate correction, and robustness tests, the method is not a universal winner in either nominal fidelity or wall-clock cost. Its value is instead to expose and stabilize a low-complexity frontier of the fidelity-complexity landscape. After retuning the PADMM budgets and warm-start lengths, the qutrit and two-qubit structured fidelities rise to 0.6672 +- 0.0001 and 0.6342 +- 0.0003, respectively, while preserving markedly lower complexity than unconstrained quasi-Newton solutions. These values remain well below deployment-grade gate thresholds, so the contribution should still be read as a numerical framework for constrained pulse synthesis rather than as a finished route to immediately deployable high-fidelity gates. Training-time robust optimization yields only task-dependent gains, with the clearest effect appearing in qutrit drift robustness and amounting to a small absolute improvement. The results therefore position PADMM as a constraint-native framework for low-complexity frontier exploration, not as a replacement for unconstrained high-fidelity solvers.
We present a variational quantum framework for finite-horizon quantum control based on hardware-efficient ansätze. The objective is to steer a quantum system from a given initial state to a desired target state over a fixed time horizon by minimizing a terminal cost defined in terms of state fidelity. Instead of explicitly synthesizing time-dependent control fields or enforcing Hamiltonian reachability constraints, the proposed method reformulates the control objective as a variational optimization problem in which a hardware-efficient parameterized quantum circuit provides a surrogate parameterization of the terminal evolution. The circuit consists of alternating layers of single-qubit rotations and entangling gates, whose parameters are optimized using classical routines to minimize the terminal infidelity. This formulation avoids reliance on problem-specific or physics-inspired ansätze, providing a flexible and implementation-friendly approach compatible with near-term quantum devices. Numerical experiments on multi-qubit state-transfer benchmarks demonstrate high-fidelity state transfer while highlighting the trade-off between ansatz expressivity, optimization complexity, and scalability with respect to system size and circuit depth.
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Quantum optimal control can produce fast and robust multi-qubit gates, but experimentally calibrating the resulting high-dimensional waveforms remains challenging because direct searches over large parameter spaces converge slowly. Building on the low-rank structure of quantum-control landscapes, we develop and benchmark a Hessian-based calibration method for optimal-control gates. The method identifies the few waveform directions that affect fidelity to leading order, with the number of directions set by the accessible leakage and coherent error channels, and optimizes only within this principal space using closed-loop experimental feedback. We apply this approach to an amplitude-robust controlled-Z gate on metastable-state 171Yb nuclear-spin qubits. Experimentally, we verify the predicted Hessian-sensitive directions and demonstrate rapid convergence of the optimization protocol. The optimized gate reaches a raw fidelity of 0.9959(2), increasing to 0.99902(7) after postselection on no detected loss, and the performance is essentially unchanged under laser-power variations of up to 20%. We further show that the same fidelity Hessian directions can correct certain Hamiltonian parameter errors. These results establish low-rank Hessian optimization as an efficient and physically motivated calibration strategy for high-dimensional optimal-control gates, which is broadly applicable to many qubit types.
Dynamic control via optimized, piecewise-constant pulses is a common paradigm for open-loop control to implement quantum gates. While numerous methods exist for the synthesis of such controls, there are many open questions regarding the robustness of the resulting control schemes in the presence of model uncertainty; unlike in classical control, there are generally no analytical guarantees on the control performance with respect to inexact modeling of the system. In this paper a new robustness measure based on the differential sensitivity of the gate fidelity error to parametric (structured) uncertainties is introduced, and bounds on the differential sensitivity to parametric uncertainties are used to establish performance guarantees for optimal controllers for a variety of quantum gate types, system sizes, and control implementations. Specifically, it is shown how a maximum allowable perturbation over a set of Hamiltonian uncertainties that guarantees a given fidelity error, can be reliably computed. This measure of robustness is inversely proportional to the upper bound on the differential sensitivity of the fidelity error evaluated under nominal operating conditions. Finally, the results show that the nominal fidelity error and differential sensitivity upper bound are positively correlated across a wide range of problems and control implementations, suggesting that in the high-fidelity control regime, rather than there being a trade-off between fidelity and robustness, higher nominal gate fidelities are positively correlated with increased robustness of the controls in the presence of parametric uncertainties.
Achieving fast and high-fidelity qubit operations is crucial for unlocking the potential of quantum computers. In particular, reaching low gate errors in two-qubit gates has been a long-standing challenge in the field of superconducting qubits due to their typically long duration relative to coherence times. To realize fast gates, we use the hybridization between fixed-frequency superconducting qubits with a strongly interacting coupler mode that is tunable in frequency. To reduce population leakage during required adiabatic passages through avoided level crossings, we employ a sensitivity-adaptive closed-loop optimization method to design complex pulse shapes. We compare the performance of Gaussian-square, Fourier-series, and piecewise-constant-slope pulse parametrizations and are able to reach 99.9% controlled- Z -gate fidelity using a 64-ns-long Fourier-series pulse defined by only seven parameters. These high-fidelity values are achieved by analyzing the optimized pulse shapes to identify and systematically mitigate signal-line distortions in the experiment. To improve the convergence speed of the optimization, we implement an adaptive cost function, which continuously maximizes the sensitivity. The demonstrated method can be used for tune-up and recalibration of superconducting quantum processors.
High-fidelity quantum gates are essential for scalable quantum computation. However, at short durations, short-timescale waveform distortions break the time-reflection symmetry of control pulses, preventing the precise closure of cyclic evolution. This mechanism renders conventional symmetric protocols intrinsically over-constrained. Conventional strategies typically rely on smoothing the pulse envelopes or embedding the interaction pulse within a longer qubit pulse to bypass short-timescale distortions, which inevitably leads to a persistent speed-fidelity trade-off. To overcome this limitation, we introduce a cyclic control strategy based on parameter-space expansion, which restores controllability by incorporating an additional degree of freedom. We experimentally demonstrate this approach in a superconducting controlled-Z gate, achieving robust suppression of coherent errors without increasing gate duration, reducing the average coherent error from 0.27% to 0.12% across multiple two-qubit gates, as validated by cross-entropy benchmarking. Our results establish a general route to fast, high-fidelity cyclic quantum gates beyond the conventional speed-fidelity trade-off.