Quantum control papers on arXiv, selected and classified automatically by QOC. https://llorebaga.github.io/QOC/topics/gradient-free/ 2026-10-05T00:00:00Z QOC https://arxiv.org/abs/2610.06264 2026-10-05T00:00:00Z 2026-10-05T00:00:00Z Kuljeet KaurXuexin XuMohammad H. Ansari

Frequency crowding, the close spacing of qubit transitions, limits superconducting quantum processors through crosstalk, leakage, and spectator dependent errors. We introduce a Hamiltonian based framework that maps two- and three-body collision mechanisms onto a Max-k-Cut problem on qubit frequency allocation. In order to make larger lattices tractable, we introduce a cluster-stitching protocol that decomposes larger lattices into overlapping subgraphs and reconciles their local frequency assignments into globally consistent configurations. We tested our proposed framework across multiple qubit architectures to suppress dominant collision penalties by revealing a hardware residual crosstalk. We benchmark digitized counterdiabatic QAOA (DC-QAOA), standard QAOA, and simulated annealing. Counterdiabatic terms improve QAOA optimization near steep collision penalties, while simulated annealing achieves the lowest costs on the tested instances. These results indicate that our hardware-aware optimization connects microscopic interaction physics to processor-level frequency planning. These results enable a systematic solver for benchmarking of scalable software hardware co design.

https://arxiv.org/abs/2610.03788 2026-09-30T00:00:00Z 2026-09-30T00:00:00Z Ofer M. Shir

We revisit an established Quantum Control objective, characterize its exact local metric geometry, and reposition it as a rigorous benchmark for Search and Optimization. The trap-free topology of the landscape, established two decades ago, guarantees unhindered optimal pathways, but global topology alone does not govern convergence speed. Although the so-called Quantum Sphere is defined by the fourth power of the control field, we show that its yield gap admits an exact representation as a nonnegative linear combination of $\sin^2$ terms. From this single identity, we derive a two-sided quadratic sandwich with ratio $4/π^2$ on a certified, worst-case quarter-wave region, proving the optimal peak nondegenerate modulo its affine gauge and well-conditioned. For white-box optimization, on a halved (eighth-wave) region, the objective satisfies a Polyak-Łojasiewicz inequality with an explicit constant, granting Gradient Descent a linear rate of $1 - 16/(π^4κ_r)$, where $κ_r$ is the condition number of the relevant Hessian block. For black-box optimization, we prove that the elitist single-parent Evolution Strategy converges linearly, almost surely and in expected hitting time, from a certified sublevel set, despite the landscape's continuous gauge symmetry and its lattice of periodic optima; the certified level is explicit whenever the landscape is trap-free, and near the optimum the strategy provably sees only the quadratic tangent of the peak. Systematic numerical simulations corroborate the theory. Finally, we show that additive input (actuator) noise reweights each term of the representation by a Debye-Waller factor without disrupting the landscape's structure, and outline open challenges: output (signal) noise, far-field plateau escape, angular-synchronization certificates, and another physically scalable benchmark (SHG with Glass).

https://arxiv.org/abs/2609.11197 2026-09-10T00:00:00Z 2026-09-10T00:00:00Z Bereket Sitotaw KidaneMd Samiul Haque MotayedShuo Wang

Optimal control in cancer chemotherapy is challenged by tumor heterogeneity and mutations, which complicate treatment effectiveness. Traditional methods, such as Pontryagin's maximum principle (PMP), are often hindered by their reliance on an initial guess for the costate equation, affecting accuracy and convergence. To address these limitations, this work introduces a hybrid quantum particle swarm optimization (QPSO) method based on regularization. QPSO is employed for global exploration to approximate the optimal control trajectory, followed by a regularization-based refinement to ensure smoothness and consistency with optimality conditions. The Hamiltonian function is used for first- and second-order optimality checks, verifying solution quality. Numerical case studies explore various drug effectiveness functions, demonstrating the role of periodic and localized drug delivery in achieving robust tumor suppression and providing insights into the impact of different drug combinations on optimal chemotherapy strategies.

https://arxiv.org/abs/2609.02372 2026-09-02T00:00:00Z 2026-09-02T00:00:00Z Andrea De LucaCarola CiaramellettiSimone Paganelli

Precise control of multi-qubit architectures remains a critical bottleneck in superconducting quantum processors. In this work, we investigate the synthesis of high-fidelity quantum operations and state transfer protocols within an extended superconducting linear chain, scaling from three to seven sites. Using Floquet theory, we model the periodic drive as a train of delta-like pulses, mapping the quantum control problem onto quasi-energy resonance conditions. Combining the Baker-Campbell-Hausdorff expansion with Floquet spectral decomposition, we analytically identify optimal driving parameters, refined via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In the three-qubit architecture, this enables high-fidelity synthesis of the iSWAP gate. Extending to a seven-site chain, we implement periodic trains of finite-width Gaussian pulses to activate distinct double-excitation transport channels with ultra-short gate durations t_gate (~170 ns). This achieves a clear scale separation from energy-relaxation times (T1) typical of fixed-frequency transmon devices with tunable couplers, such as IBM Quantum hardware. Finally, we benchmark stability under realistic imperfections, revealing a heightened sensitivity to static parameter disorder at the sub-percent level (eta ~ 10^-3) driven by spectral crowding, and discuss how closed-loop topologies could mitigate this constraint. This framework bridges time-periodic control theory and practical quantum gate engineering.

https://arxiv.org/abs/2607.24725 2026-07-27T00:00:00Z 2026-07-27T00:00:00Z Lia KleyLudwig Mathey

Optimal control problems arise in a wide range of scientific disciplines, but the corresponding optimization algorithms often display a strong dependence on hyperparameters that significantly influence performance and convergence. For the optimal implementation of quantum algorithms, these challenges are further amplified by high-dimensional control landscapes and the need for high-fidelity operations. Here, we propose an algorithm for optimal control problems in quantum computing to efficiently generate high-fidelity control protocols for multi-qubit systems in a hyperparameter and gradient free manner. The method, referred to as Pulse Engineering via Projection of response functions at infinite nonlinear order (PEPRino), leverages the framework of response theory to navigate the control landscape to find high-fidelity implementations. This is achieved by determining the control landscape via response functions to infinite order, efficiently evaluated by resummation in terms of the first and second order response function. To demonstrate the approach, we apply it to quantum systems consisting of two and three qubits for the optimal implementation of the Quantum Fourier Transform (QFT). We benchmark the proposed algorithm against the Chopped Random Basis (CRAB) algorithm utilizing the Nelder-Mead method, focusing on the 2-qubit scenario. The results indicate faster convergence regarding iteration steps and computational time, highlighting the advantages of our approach.

https://arxiv.org/abs/2604.24467 2026-04-27T00:00:00Z 2026-04-27T00:00:00Z Zeki ZeybekRick MukherjeePeter Schmelcher

Quantum optimal control (QOC) provides a systematic framework for achieving high-fidelity operations in quantum systems and plays a central role in tasks such as gate synthesis, state transfer, and pulse design. Existing QOC methods broadly fall into two categories: gradient-based and gradient-free algorithms. The associated optimization landscape is often high-dimensional, non-convex, and populated by numerous local minima, making efficient gradient-free search strategies essential. To address this, we introduce a gradient-free matrix product state/tensor train (MPS/TT) sampling heuristic for discrete quantum optimal control. In our approach, the MPS defines a score function over the space of discrete control parameters, which in turn induces a sampling distribution over candidate control sequences. This distribution is iteratively refined through selection of better performing sequences and local tensor updates to bias the search toward high-performing sequences. We evaluate the method on a range of benchmark problems, including single-qubit state transfer, Bell-pair preparation, qutrit gate implementation, and open-system population transfer. Across these tasks, the method exhibits stable convergence behavior and competitive empirical performance relative to established gradient-free baselines. These results suggest that tensor network sampling offers a viable heuristic framework for discrete quantum control.

https://arxiv.org/abs/2601.06303 2026-01-09T00:00:00Z 2026-01-09T00:00:00Z Sofía Perón SantanaAriel FiuriMartín DomínguezOmar Osenda

In recent years, control methods based on different optimization techniques have shed light on the possibilities of processing information in many quantum systems. When exploring the transmission of quantum states, faster transmission times are mandatory to avoid the deleterious effects of multiple sources of decoherence that spoil the transmission process. In particular, using Reinforcement Learning to devise sequences of step-wise external controls provides good transfer policies at short transmission times. We present two approaches to control the transmission of quantum states in qubit chains using external controls to force the dynamical evolution of the chain state. The first approach relies on the well-known Genetic Algorithm to generate a sequence of external controls, while the second approach uses a variant of Reinforcement Learning. The Genetic algorithm achieves excellent transmission fidelity at as short transmission times as Reinforcement Learning, surpassing the fidelities achieved by the latter method. Nevertheless, the Reinforcement Learning method offers robust control policies when the control pulses are noisy enough, owing to an imperfect timing of the pulses, deficient control devices, or other sources of phase decoherence. We present the regime where each method is best suited to control the transmission of arbitrary qubit states.

https://arxiv.org/abs/2512.10290 2025-12-11T00:00:00Z 2025-12-11T00:00:00Z Oleg V. Morzhin

This article (II) continues the research described in [Morzhin O.V. Gradient projection method and stochastic search for some optimal control models with spin chains. I (submitted)] (Article I), derives the needed finite-dimensional gradients corresponding to the infinite-dimensional gradients obtained in Article I, both for transfer and keeping problems at a certain $N$-dimensional spin chain, and correspondingly adapts a projection-type condition for optimality, gradient projection method (GPM). For the case $N=3$, the given in this article examples together with Example 3 in Article I show that: a) the adapted GPM and genetic algorithm (GA) successfully solved numerically the considered transfer and keeping problems; b) the two- and three-step GPM forms significantly surpass the one-step GPM. Moreover, GA and a special class of controls were successfully used in such the transfer problem that $N=20$ and the final time is not assigned.

https://arxiv.org/abs/2512.10093 2025-12-10T00:00:00Z 2025-12-10T00:00:00Z Oleg V. Morzhin

This article (I) considers the known optimal control model of a quantum information transfer along a spin chain with controlled external parabolic magnetic field, with an arbitrary length. The article adds certain lower and upper pointwise constraints on controls, adds the problem of keeping the signal at the last spin, considers various classes of controls. For these problems under piecewise continuous controls, the projection-type linearized Pontryagin maximum principle, gradient projection method's constructions in its one- and two- and three-step forms were adapted by analogy with [Morzhin O.V., Pechen A.N. J. Phys. A: Math. Theor., 2025]. Moreover, an example with a genetic algorithm's successful use under a special class of controls is given.

https://arxiv.org/abs/2509.08555 2025-09-10T00:00:00Z 2026-04-13T00:00:00Z Kevin PackShai MachnesFrank K. Wilhelm

We present the results of a comprehensive study of optimization algorithms for the calibration of quantum devices. As part of our ongoing efforts to automate bring-up, tune-up, and system identification procedures, we investigate a broad range of optimizers within a simulated environment designed to closely mimic the challenges of real-world experimental conditions. Our benchmark includes widely used algorithms such as Nelder-Mead and the state-of-the-art Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We evaluate performance in both low-dimensional settings, representing simple pulse shapes used in current optimal control protocols with a limited number of parameters, and high-dimensional regimes, which reflect the demands of complex control pulses with many parameters. Based on our findings, we recommend the CMA-ES algorithm and provide empirical evidence for its superior performance across all tested scenarios.

https://arxiv.org/abs/2507.20777 2025-07-28T00:00:00Z 2025-07-28T00:00:00Z Bruno Oziel FernandezRodrigo BlootMarcelo Moret

Quantum algorithms are of great interest for their possible use in optimization problems. In particular, variational algorithms that use classical counterparts to optimize parameters hold promise for use in currently existing devices. However, convergence stagnation phenomena pose a challenge for such algorithms. Seeking to avoid such difficulties, we present a framework based on circuits with variable topology with two approaches, one based on ansatz-free evolutionary method known from literature and the other using an introduction of an ansatz with circuital structure inspired by the physics of the Hamiltonian related to the problem, considering a, named here, pseudo-counterdiabatic evolutionary term. The efficiency of the proposed framework was tested on several instances of the set partitioning problem. The two approaches were compared with the Variational Quantum Eigensolver in noisy and non-noisy scenarios. The results demonstrated that optimization using circuits with variable topology presented very encouraging results. Notably, the strategy employing a pseudo-counterdiabatic evolutionary term exhibited remarkable performance, avoiding convergence stagnation in most instances considered. This framework circumvents the need for classical optimizers, and, as a consequence, this procedure based on circuits with variable topology indicates an interesting path in the search for algorithms to solve integer optimization problems targeting efficient applications in larger-scale scenarios.

https://arxiv.org/abs/2507.09770 2025-07-13T00:00:00Z 2026-02-05T00:00:00Z Daniel TuryanskyYehonatan ZoltiYuval CohenAdi Pick

We present a robust pulse optimization method for adiabatic population transfer and adiabatic quantum computation. The approach relies on identifying control pulses that keep the evolving quantum system close to its instantaneous ground state. By combining advanced gradient-free optimization tools with specialized cost functions for adiabatic control, it achieves both efficiency and robustness. To demonstrate its generality, we apply the method to three examples involving both atomic and superconducting qubits. We test different optimization cost functions and discretization bases, showing that the approach outperforms ensemble optimization. Finally, to verify its performance on real quantum hardware, we implement digitized adiabatic qubit control using the optimized pulses on the IBM Quantum cloud.

https://arxiv.org/abs/2503.03164 2025-03-05T00:00:00Z 2025-03-05T00:00:00Z Hailan MaBo QiIan R. PetersenRe-Bing WuHerschel RabitzDaoyi Dong

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

https://arxiv.org/abs/2502.05228 2025-02-06T00:00:00Z 2026-03-26T00:00:00Z Kaichen OuyangMingyang YuZong KeJun ZhangYi ChenHuiling Chen

Physics-informed Neural Networks (PINNs) show that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. Similar to optimizing loss functions in machine learning, evolutionary algorithms iteratively optimize objective functions by simulating natural selection processes. Inspired by this principle, we ask a natural question: can physical information be similarly embedded into the fitness function of evolutionary algorithms? In this work, we propose Physics-informed Evolution (PIE), a novel framework that incorporates physical information derived from governing physical laws into the evolutionary fitness landscape, thereby extending Physics-informed artificial intelligence methods from machine learning to the broader domain of evolutionary computation. As a concrete instantiation, we apply PIE to quantum control problems governed by the Schrödinger equation, where the goal is to find optimal control fields that drive quantum systems from initial states to desired target states. We validate PIE on three representative quantum control benchmarks: state preparation in V-type three-level systems, entangled state generation in superconducting quantum circuits, and two-atom cavity QED systems. Within the PIE framework, we systematically compare the performance of ten single-objective and five multi-objective evolutionary algorithms. Experimental results demonstrate that by embedding physical information into the fitness function, PIE effectively guides evolutionary search, yielding control fields with high fidelity, low state deviation, and robust performance across different scenarios. Our findings further suggest that the Physics-informed principle extends naturally beyond neural network training to the broader domain of evolutionary computation.

https://arxiv.org/abs/2501.14007 2025-01-23T00:00:00Z 2025-01-23T00:00:00Z William Aguilar-CalvoSantiago Núñez-Corrales

Noise remains a fundamental challenge in quantum computing, significantly affecting pulse fidelity and overall circuit performance. This paper introduces an adaptive algorithm for pulse-level quantum error mitigation, designed to enhance fidelity by dynamically responding to noise conditions without modifying circuit gates. By targeting pulse parameters directly, this method reduces the impact of various noise sources, improving algorithm resilience in quantum circuits. We show the latter by applying our protocol to Grover's and Deutsch-Jozsa algorithms. Experimental results show that this pulse-level strategy provides a flexible and efficient solution for increasing fidelity during the noisy execution of quantum circuits. Our work contributes to advancements in error mitigation techniques, essential for robust quantum computing.

https://arxiv.org/abs/2410.15375 2024-10-20T00:00:00Z 2025-11-22T00:00:00Z Yiming ZhaoLi-Bo ChenYong WangHong-Yang MaXiaolong Zhao

We introduce a novel strategy employing an adaptive genetic algorithm (GA) for iterative optimization of control sequences to generate quantum nonclassical states. Its efficacy is demonstrated by preparing spin-squeezed states in an open collective spin model governed by a linear control field. Inspired by Darwinian evolution, the algorithm iteratively refines control sequences using crossover, mutation, and elimination strategies, starting from a coherent spin state within a dissipative and dephasing environment. We rigorously benchmark our method against constant control protocols and reinforcement learning, demonstrating competitive and robust performance. Furthermore, we showcase the GA's versatility by directly optimizing for metrologically relevant squeezing, achieving scalable performance, even in the presence of dissipation and thermal noise. The proposed strategy demonstrates a high state-preparation fidelity, exceeding 0.99, and provides a long time window for maintaining the spin squeezed state, even under dissipative conditions. We discuss feasible experimental implementations and potential extensions to alternative quantum systems, and the adaptability of the GA module. This research establishes the foundation for utilizing GA-like strategies in controlling quantum systems and achieving desired nonclassical states.

https://arxiv.org/abs/2208.08261 2022-08-17T00:00:00Z 2022-08-17T00:00:00Z Yang-Yang XieFeng-Hua RenRun-Hong HeArapat AblimitZhao-Ming Wang

Precise and efficient control of quantum systems is essential to perform quantum information processing tasks. In terms of adiabatic speedup via leakage elimination operator approach, for a closed system, the ideal pulse control conditions have been theoretically derived by P-Q partitioning technique. However, it is a challenge to design the corresponding control pulses for an open system, which requires to tackle noisy environments. In this paper, we apply the stochastic search procedures to an open qutrit system and successfully obtain the optimal control pulses for significant adiabatic speedup. The calculation results show that these optimal pulses allow us to acquire higher fidelities than the ideal pulses. The improvement of fidelity is large for relatively strong system-bath coupling strength and high bath temperature. For certain coupling strength and bath temperature, the maximal improvement can be achieved for a critical characteristic frequency which represents the memory time of the environment. Our investigation indicates that the stochastic search procedures are powerful tools to design control pulses for combating the detrimental effects of the environment.

https://arxiv.org/abs/2206.14681 2022-06-29T00:00:00Z 2023-01-25T00:00:00Z Jonathon BrownMauro PaternostroAlessandro Ferraro

We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. The qubit-resonator couplings are assumed to be in the resonant regime and tunable in time. A genetic algorithm is used in order to find the functional time-dependence of the couplings that optimise the fidelity between the evolved state and a variety of targets, including three-qubit GHZ and Dicke states and four-qubit graph states. We observe high quantum fidelities (above 0.96 in the worst case setting of a system of effective dimension 96) and resilience to noise, despite the algorithm being trained in the ideal noise-free setting. These results show that the genetic algorithms represent an effective approach to control quantum systems of large dimensions.

https://arxiv.org/abs/2205.06165 2022-05-12T00:00:00Z 2023-07-04T00:00:00Z Mateo LondoñoJulio C. Arce

We propose an optical control scheme for driving a polar molecule from a high-lying vibrational level to a target low-lying one, within the same electronic state. The scheme utilizes an infrared chirped laser pulse with an analytical shape, whose parameters are optimized by means of a heuristic formulation of quantum optimal control based on a genetic algorithm. We illustrate this methodology computationally for a KRb Feshbach molecule in the lowest triplet electronic state.

https://arxiv.org/abs/2108.03185 2021-08-06T00:00:00Z 2021-11-30T00:00:00Z Pratibha Raghupati HegdeGianluca PassarelliAnnarita ScoccoProcolo Lucignano

The study of optimal control of quantum annealing by modulating the pace of evolution and by introducing a counterdiabatic potential has gained significant attention in recent times. In this work, we present a numerical approach based on genetic algorithms to improve the performance of quantum annealing, which evades the Landau-Zener transitions to navigate to the ground state of the final Hamiltonian with high probability. We optimize the annealing schedules starting from polynomial ansatz by treating their coefficients as chromosomes of the genetic algorithm. We also explore shortcuts to adiabaticity by computing a practically feasible $k$-local optimal driving operator, showing that even for $k=1$ we achieve substantial improvement of the fidelity over the standard annealing solution. With these genetically optimized annealing schedules and/or optimal driving operators, we are able to perform quantum annealing in relatively short time-scales and with larger fidelity compared to traditional approaches.

https://arxiv.org/abs/2105.09824 2021-05-20T00:00:00Z 2021-05-20T00:00:00Z S. Ashwin RenganathanJeffrey LarsonStefan M. Wild

We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate stochastic oracle. We are interested in the decision that maximizes the oracle at a finite time horizon, given a limited budget of noisy evaluations of the oracle that can be performed before the horizon. Our recursive two-step lookahead acquisition function for Bayesian optimization makes nonmyopic decisions at every stage by maximizing the expected utility at the specified time horizon. Specifically, we propose a generalized two-step lookahead framework with a customizable \emph{value} function that allows users to define the utility. We illustrate how lookahead versions of classic acquisition functions such as the expected improvement, probability of improvement, and upper confidence bound can be obtained with this framework. We demonstrate the utility of our proposed approach on several carefully constructed synthetic cases and a real-world quantum optimal control problem.

https://arxiv.org/abs/2104.12699 2021-04-26T00:00:00Z 2021-04-27T00:00:00Z Boris VolkovOleg V. MorzhinAlexander Pechen

In this work, we consider the problem of ultrafast controlled generation of single-qubit phase shift quantum gates. Globally optimal control is a control which realizes the gate with maximal possible fidelity. Trap is a control which is optimal only locally but not globally. It was shown before that traps do not exist for controlled generation of arbitrary single-qubit quantum gates for sufficiently long times, as well as for fast control of quantum gates other than phase shift gates. Ultrafast generation of phase-shift gates was missed in the previous analysis. In this work we show, combining analytical and numerical optimization methods such as Gradient Ascent Pulse Engineering (GRAPE), differential evolution, and dual annealing, that control landscape for ultrafast generation of phase shift gates is also free of traps. Mathematical analysis of quantum control landscapes, which aims to prove either absence or existence of traps for quantum control objective functionals, is an important topic in quantum control. In this work, we provide a rigorous analysis of quantum control landscapes for ultrafast generation of single-qubit quantum gates and show, combining analytical methods based on a sophisticated analysis of spectrum of the Hessian, and numerical optimization methods such as Gradient Ascent Pulse Engineering (GRAPE), differential evolution, and dual annealing, that control landscape for ultrafast generation of phase shift gates is free of traps.

https://arxiv.org/abs/2102.11813 2021-02-23T00:00:00Z 2021-02-23T00:00:00Z Andrew KoswaraVaibhav BhutoriaRaj Chakrabarti

Despite significant progress in theoretical and laboratory quantum control, engineering quantum systems remains principally challenging due to manifestation of noise and uncertainties associated with the field and Hamiltonian parameters. In this paper, we extend and generalize the asymptotic quantum control robustness analysis method -- which provides more accurate estimates of quantum control objective moments than standard leading order techniques -- to diverse quantum observables, gates and moments thereof, and also introduce the Pontryagin Maximum Principle for quantum robust control. In addition, we present a Pareto optimization framework for achieving robust control via evolutionary open loop (model-based) and closed loop (model-free) approaches with the mechanisms of robustness and convergence described using asymptotic quantum control robustness analysis. In the open loop approach, a multiobjective genetic algorithm is used to obtain Pareto solutions in terms of the expectation and variance of the transition probability under Hamiltonian parameter uncertainty. The set of numerically determined solutions can then be used as a starting population for model-free learning control in a feedback loop. The closed loop approach utilizes real-coded genetic algorithm with adaptive exploration and exploitation operators in order to preserve solution diversity and dynamically optimize the transition probability in the presence of field noise. Together, these methods provide a foundation for high fidelity adaptive feedback control of quantum systems wherein open loop control predictions are iteratively improved based on data from closed loop experiments.

https://arxiv.org/abs/2008.03874 2020-08-10T00:00:00Z 2021-01-20T00:00:00Z Xiaodong YangChristian ArenzIstván PelczerQi-Ming ChenRe-Bing WuXinhua PengHerschel Rabitz

Designing a high-quality control is crucial for reliable quantum computation. Among the existing approaches, closed-loop leaning control is an effective choice. Its efficiency depends on the learning algorithm employed, thus deserving algorithmic comparisons for its practical applications. Here, we assess three representative learning algorithms, including GRadient Ascent Pulse Engineering (GRAPE), improved Nelder-Mead (NMplus) and Differential Evolution (DE), by searching for high-quality control pulses to prepare the Bell state. We first implement each algorithm experimentally in a nuclear magnetic resonance system and then conduct a numerical study considering the impact of some possible significant experimental uncertainties. The experiments report the successful preparation of the high-fidelity target state with different convergence speeds by the three algorithms, and these results coincide with the numerical simulations when potential uncertainties are negligible. However, under certain significant uncertainties, these algorithms possess distinct performance with respect to their resulting precision and efficiency. This study provides insight to aid in the practical application of different closed-loop learning algorithms in realistic physical scenarios.

https://arxiv.org/abs/2007.00368 2020-07-01T00:00:00Z 2021-02-24T00:00:00Z Davide CastaldoMarta RosaStefano Corni

We develop an hybrid quantum-classical algorithm to solve an optimal population transfer problem for a molecule subject to a laser pulse. The evolution of the molecular wavefunction under the laser pulse is simulated on a quantum computer, while the optimal pulse is iteratively shaped via a machine learning (evolutionary) algorithm. A method to encode on the quantum computer the n-electrons wavefunction is discussed, the circuits accomplishing its quantum simulation are derived and the scalability in terms of number of operations is discussed. Performance on Noisy Intermediate-Scale Quantum devices (IBM Q X2) is provided to assess the current technological gap. Furthermore the hybrid algorithm is tested on a quantum emulator to compare performance of the evolutionary algorithm with standard ones. Our results show that such algorithms are able to outperform the optimization with a downhill simplex method and provide performance comparable to more advanced (but quantum-computer unfriendly) algorithms such as Rabitz's.

https://arxiv.org/abs/2001.03520 2020-01-10T00:00:00Z 2020-05-12T00:00:00Z Rick MukherjeeFrederic SauvageHarry XieRobert LoewFlorian Mintert

Ultra-cold atomic gases are unique in terms of the degree of controllability, both for internal and external degrees of freedom. This makes it possible to use them for the study of complex quantum many-body phenomena. However in many scenarios, the prerequisite condition of faithfully preparing a desired quantum state despite decoherence and system imperfections is not always adequately met. To path the way to a specific target state, we explore quantum optimal control framework based on Bayesian optimization. The probabilistic modeling and broad exploration aspects of Bayesian optimization is particularly suitable for quantum experiments where data acquisition can be expensive. Using numerical simulations for the superfluid to Mott-insulator transition for bosons in a lattice as well for the formation of Rydberg crystals as explicit examples, we demonstrate that Bayesian optimization is capable of finding better control solutions with regards to finite and noisy data compared to existing methods of optimal control.

https://arxiv.org/abs/1912.04368 2019-12-09T00:00:00Z 2019-12-09T00:00:00Z Murphy Yuezhen NiuVadim SmelyanskyiPaul V. KlimovSergio BoixoRami BarendsJulian KellyYu ChenKunal AryaBrian BurkettDave BaconZijun ChenBen ChiaroRoberto CollinsAndrew DunsworthBrooks FoxenAustin G. FowlerCraig GidneyMarissa GiustinaRob GraffTrent HuangEvan JeffreyDavid LandhuisErik LuceroAnthony MegrantJosh MutusXiao MiOfer NaamanMatthew NeeleyCharles NeillChris QuintanaPedram RoushanJohn M. MartinisHartmut Neven

Two-level-system (TLS) defects in amorphous dielectrics are a major source of noise and decoherence in solid-state qubits. Gate-dependent non-Markovian errors caused by TLS-qubit coupling are detrimental to fault-tolerant quantum computation and have not been rigorously treated in the existing literature. In this work, we derive the non-Markovian dynamics between TLS and qubits during a SWAP-like two-qubit gate and the associated average gate fidelity for frequency-tunable Transmon qubits. This gate dependent error model facilitates using qubits as sensors to simultaneously learn practical imperfections in both the qubit's environment and control waveforms. We combine the-state-of-art machine learning algorithm with Moiré-enhanced swap spectroscopy to achieve robust learning using noisy experimental data. Deep neural networks are used to represent the functional map from experimental data to TLS parameters and are trained through an evolutionary algorithm. Our method achieves the highest learning efficiency and robustness against experimental imperfections to-date, representing an important step towards in-situ quantum control optimization over environmental and control defects.

https://arxiv.org/abs/1802.07521 2018-02-21T00:00:00Z 2018-02-21T00:00:00Z Jens Jakob SørensenMikel AranburuTill HeinzelJacob Sherson

We propose a Global-Local optimization algorithm for quantum control that combines standard local search methodologies with evolutionary algorithms. This allows us to find faster solutions to a set of problems relating to ultracold control of Bose-Einstein condensates.

https://arxiv.org/abs/1802.07509 2018-02-21T00:00:00Z 2018-08-20T00:00:00Z Jens Jakob SørensenMikel AranburuTill HeinzelJacob Sherson

We discuss quantum optimal control of Bose-Einstein condensates trapped in magnetic microtraps. The objective is to transfer a condensate from the ground state to the first-excited state. This type of control problem is typically solved using derivative-based methods in a high-dimensional control space such as gradient-ascent pulse engineering (GRAPE) and Krotov's method or derivative-free methods in a reduced control space such as Nelder-Mead with a chopped random basis (CRAB). We discuss how these methods can be combined in gradient optimization using parametrization (GROUP) including the finite bandwidth of the control electronics. We compare these methods and find that GROUP converges much faster than Nelder-Mead with CRAB and achieves better results than GRAPE and Krotov's method on the control problem presented here.

https://arxiv.org/abs/1704.01455 2017-04-05T00:00:00Z 2017-07-31T00:00:00Z Vincent C. GregoricXinyue KangZhimin Cheryl LiuZoe A. RowleyThomas J. CarrollMichael W. Noel

Quantum control of the pathway along which a Rydberg electron field ionizes is experimentally and computationally demonstrated. Selective field ionization is typically done with a slowly rising electric field pulse. The $(1/n^*)^4$ scaling of the classical ionization threshold leads to a rough mapping between arrival time of the electron signal and principal quantum number of the Rydberg electron. This is complicated by the many avoided level crossings that the electron must traverse on the way to ionization, which in general leads to broadening of the time-resolved field ionization signal. In order to control the ionization pathway, thus directing the signal to the desired arrival time, a perturbing electric field produced by an arbitrary waveform generator is added to a slowly rising electric field. A genetic algorithm evolves the perturbing field in an effort to achieve the target time-resolved field ionization signal.