Quantum control papers on arXiv, selected and classified automatically by QOC. https://llorebaga.github.io/QOC/topics/crab/ 2026-07-27T00:00:00Z QOC 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/2607.23074 2026-07-25T00:00:00Z 2026-07-25T00:00:00Z Kiran ThengilVincenzo Roberto ArezzoGiuseppe E. Santoro

We investigate the role of local counter-diabatic (CD) terms in enhancing the performance of discrete-time digital protocols for a frustrated Ising ring, a system with an exponentially small spectral gap that acts as a bottleneck for conventional quantum annealing. The techniques investigated range from a digitised version of a fixed-schedule protocol, including lowest-order analytical CD terms, all the way to a full-fledged Quantum Approximate Optimisation Algorithm (QAOA), including variational CD terms (DC-QAOA). By analyzing the resulting residual energy, we show that DC-QAOA combined with Chopped RAndom Basis (CRAB) quantum control techniques for optimizing the circuit parameters, can outperform all the other strategies. By monitoring the ground-state population during the dynamics we learn that DC-QAOA finds effective shortcuts towards the target state through intermediate excited states, a process that is unexpectedly enhanced by the inclusion of local CD unitaries. Our results highlight the importance of flexible variational control of CD dynamics and demonstrate that digital optimization can explore operator dynamics that remain inaccessible to standard analytical CD constructions.

https://arxiv.org/abs/2607.12567 2026-07-14T00:00:00Z 2026-07-14T00:00:00Z Zhao-Wei WangKai-Yu YuanFeng-Hua RenZhao-Ming Wang

Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems, in which these two tasks are typically decoupled in traditional strategies. However, most work remains confined to Markovian environments. When applied to non-Markovian systems, standard PINN architectures fail to converge reliably due to multi-objective optimization conflicts arising from the coupled differential equations. To address this fundamental limitation, we extend our previously proposed forked PINN (FPINN) by incorporating a dedicated control branch. By decoupling the optimization objectives at the gradient level via selective gradient flow, our method turns a previously intractable multi-task optimization into a well-conditioned one, allowing simulation and control to be optimized jointly without compromise. Numerical simulations on a two-qubit Heisenberg XXX model confirm that our framework faithfully reproduces the features of non-Markovian dynamics, including decoherence and information backflow. Taking a state-preparation task on the same model as an example, our FPINN achieves higher fidelity than gradient ascent pulse engineering, chopped random basis, and standard PINNs, with the advantage becoming more pronounced as the environment becomes more dissipative and more Markovian. The generated pulses are also noticeably smoother, which is advantageous for experimental implementation. Our framework thus provides a unified, end-to-end differentiable paradigm for simulation and control of open quantum systems, with potential implications for quantum computing, simulation, and control.

https://arxiv.org/abs/2512.24406 2025-12-30T00:00:00Z 2026-07-23T00:00:00Z Vladimir M. StojanovicTommaso CalarcoAndrea Muratori

We explore the feasibility of realizing Dicke states in qubit arrays with always-on isotropic Heisenberg coupling between adjacent qubits, assuming a single Zeeman-type control acting in the $z$ direction on an actuator qubit. The Lie-algebraic criteria of controllability imply that such an array is not completely controllable, but satisfies the conditions for subspace controllability on any subspace with a fixed number of excitations. Therefore, a qubit array described by the model under consideration is state-to-state controllable for an arbitrary choice of initial and final states that have the same Hamming weight. This limited controllability is exploited here for the time-efficient dynamical generation of an $a$-excitation Dicke state $|D^{N}_{a}\rangle$ ($a=1,2,\ldots, N-1$) in a linear array with $N$ qubits starting from a generic Hamming-weight-$a$ product state. To dynamically generate the desired Dicke states -- including $W$ states $|W_{N}\rangle$ as their special ($a=1$) case -- in the shortest possible time with a single local $Z$ control, we employ an optimal-control scheme based on the {\em dressed chopped random basis} (dCRAB) algorithm. We optimize the target-state fidelity over the expansion coefficients of smoothly-varying control fields in a truncated random Fourier basis; this is done by combining Nelder-Mead-type local optimizations with the multistart-based clustering algorithm that facilitates searches for global extrema. In this manner, we obtain the optimal control fields for Dicke-state preparation in arrays with up to $9$ qubits. Based on our numerical results, we find that the shortest possible state-preparation times scale as $\mathcal{O}(N^{2.08})$ for $W$ states and $\mathcal{O}(N^{1.78})$ for $a=2$ Dicke states.

https://arxiv.org/abs/2505.16163 2025-05-22T00:00:00Z 2025-05-22T00:00:00Z Tianlai YangMo XiongMing XueXinwei LiJinbin Li

Integer factorization remains a significant challenge for classical computers and is fundamental to the security of RSA encryption. Adiabatic quantum algorithms present a promising solution, yet their practical implementation is limited by the short coherence times of current NISQ devices and quantum simulators. In this work, we apply the chopped random-basis (CRAB) optimization technique to enhance adiabatic quantum factorization algorithms. We demonstrate the effectiveness of CRAB by applying it to factor the integers ranging from 21 to 2479, achieving significantly improved fidelity of the target state when the evolution time exceeds the quantum speed limit. Notably, this performance improvement shows resilience in the presence of dephasing noise, highlighting CRAB's practical utility in noisy quantum systems. Our findings suggest that CRAB optimization can serve as a powerful tool for advancing adiabatic quantum algorithms, with broader implications for quantum information processing tasks.

https://arxiv.org/abs/2504.04986 2025-04-07T00:00:00Z 2025-04-07T00:00:00Z Chris WhittyEugene Ya. ShermanXi ChenYue Ban

We consider subspace transfer within the time-dependent one-dimensional quantum transverse Ising model, with random nearest-neighbor interactions and a transverse field. We run numerical simulations using a variational approach and the numerical GRAPE (gradient-ascent pulse engineering) and dCRAB (dressed chopped random basis) quantum control algorithms.

https://arxiv.org/abs/2504.00279 2025-03-31T00:00:00Z 2025-03-31T00:00:00Z Minjun JeonZhenyu Cai

Quantum optimal control plays a vital role in many quantum technologies, including quantum computation. One of the most important control parameters to optimise for is the evolution time (pulse duration). However, most existing works focus on finding the shortest evolution time theoretically possible without offering explicit pulse constructions under practical constraints like noise in the system. This paper addresses these limitations by introducing an efficient method to perform the Chopped Random Basis (CRAB) optimisation in the presence of noise, specifically when the noise commutes with the gate Hamiltonian. This noise-aware approach allows for direct optimisation of the evolution time alongside other control parameters, significantly reducing the computational cost compared to full noisy simulations. The protocol is demonstrated through numerical simulations on state-to-state transfer and gate compilation problems under several noise models. Results show that the optimised fidelity has a strong dependence on evolution time due to noise, drift Hamiltonian, and local traps in optimisation, highlighting the necessity of optimising evolution time in practical settings that can lead to a substantial gain in the fidelity. Our pulse optimisation protocol can consistently reach the global optimal time and fidelity in all of our examples. We hope that our protocol can be the start of many more works on the crucial topic of control pulse time optimisation in practical settings.

https://arxiv.org/abs/2502.17015 2025-02-24T00:00:00Z 2025-02-24T00:00:00Z Ruiyi WangVincenzo Roberto ArezzoKiran ThengilGiovanni PecciGiuseppe E. Santoro

Exponentially small spectral gaps are known to be the crucial bottleneck for traditional Quantum Annealing (QA) based on interpolating between two Hamiltonians, a simple driving term and the complex problem to be solved, with a linear schedule in time. One of the simplest models showing exponentially small spectral gaps was introduced by Roberts et al., PRA 101, 042317 (2020): a ferromagnetic Ising ring with a single frustrating antiferromagnetic bond. A previous study of this model (Côté et al., QST 8, 045033 (2023)) proposed a continuous-time diabatic QA, where optimized non-adiabatic annealing schedules provided good solutions, avoiding exponentially large annealing times. In our work, we move to a digital framework of Variational Quantum Algorithms, and present two main results: 1) we show that the model is digitally controllable with a scaling of resources that grows quadratically with the system size, achieving the exact solution using the Quantum Approximate Optimization Algorithm (QAOA); 2) We combine a technique of quantum control -- the Chopped RAndom Basis (CRAB) method -- and digitized quantum annealing (dQA) to construct smooth digital schedules yielding optimal solutions with very high accuracy.

https://arxiv.org/abs/2412.16920 2024-12-22T00:00:00Z 2026-08-31T00:00:00Z Samir DasShishira MahuntaNikhil GuptVictor MukherjeeArnab Ghosh

We use Full Counting Statistics to study fluctuations and optimal control in a three-terminal Floquet quantum thermal transistor. We model the setup using three qubits (termed as the emitter, collector and base) coupled to three thermal baths. As shown in Phys. Rev. E 106, 024110 (2022), one can achieve significant change in the emitter and collector currents through a small change in the base current, thereby achieving a thermal transistor operation. Using sinusoidal and pi-flip modulations of the base qubit frequency, we show that the variance of the base current is much less compared to those of the emitter and collector currents, while the opposite is true in case of the Fano factor. We then apply optimal control through the Chopped Random Basis optimization protocol, in order to significantly enhance the amplification obtained in the transistor. In contrast, a reduction in the Fano factor of the setup through optimal control is associated with a large base current, thereby suggesting a trade-off between precision and base current. We expect our results will be relevant for developing heat modulation devices in near-term quantum technologies.

https://arxiv.org/abs/2405.08630 2024-05-14T00:00:00Z 2024-05-14T00:00:00Z Giovanni PecciRuiyi WangPietro TortaGlen Bigan MbengGiuseppe Santoro

Quantum Annealing (QA) relies on mixing two Hamiltonian terms, a simple driver and a complex problem Hamiltonian, in a linear combination. The time-dependent schedule for this mixing is often taken to be linear in time: improving on this linear choice is known to be essential and has proven to be difficult. Here, we present different techniques for improving on the linear-schedule QA along two directions, conceptually distinct but leading to similar outcomes: 1) the first approach consists of constructing a Trotter-digitized QA (dQA) with schedules parameterized in terms of Fourier modes or Chebyshev polynomials, inspired by the Chopped Random Basis algorithm (CRAB) for optimal control in continuous time; 2) the second approach is technically a Quantum Approximate Optimization Algorithm (QAOA), whose solutions are found iteratively using linear interpolation or expansion in Fourier modes. Both approaches emphasize finding smooth optimal schedule parameters, ultimately leading to hybrid quantum-classical variational algorithms of the alternating Hamiltonian Ansatz type. We apply these techniques to MaxCut problems on weighted 3-regular graphs with N = 14 sites, focusing on hard instances that exhibit a small spectral gap, for which a standard linear-schedule QA performs poorly. We characterize the physics behind the optimal protocols for both the dQA and QAOA approaches, discovering shortcuts to adiabaticity-like dynamics. Furthermore, we study the transferability of such smooth solutions among hard instances of MaxCut at different circuit depths. Finally, we show that the smoothness pattern of these protocols obtained in a digital setting enables us to adapt them to continuous-time evolution, contrarily to generic non-smooth solutions. This procedure results in an optimized quantum annealing schedule that is implementable on analog devices.

https://arxiv.org/abs/2208.13377 2022-08-29T00:00:00Z 2023-04-30T00:00:00Z Xikun Li

A systematic scheme is proposed to numerically estimate the quantum speed limit and temporal shape of optimal control in two-level and three-level quantum systems with bounded amplitude. For the two-level system, two quantum state transitions are studied as illustration. Comparisons between numerical and analytical results are made, and deviations are significantly small. For the three-level system, two critical time points are determined with high accuracy, and optimal controls are obtained for different durations. The shape of optimized control field is simple and does not switch frequently, thus are easy to implement in experiment. In addition, we compare our method with the chopped random basis (CRAB), and the performance of our method is much better than that of CRAB. Our scheme is of importance in estimating the quantum speed limit and optimal control for cases in which analytical solution is absent.

https://arxiv.org/abs/2207.13447 2022-07-27T00:00:00Z 2022-07-27T00:00:00Z Hervè Atsè CortiLeonardo BanchiAlessandro Cidronali

We numerically study the implementation of a universal two-qubit gate set, composed of CNOT, Hadamard, phase and $π/8$ gates, for transmon-based systems. The control signals to implement such gates are obtained using the Chopped Random Basis optimal control technique, with a target gate infidelity of $10^{-2}$. During the optimization processes we account for the leakage toward non-computational states, an important non-ideality affecting transmon qubits. We also test and benchmark the optimal control solutions against the introduction of Gaussian white noise and spectral distortion, two key non-idealities that affect the control signals in transmon systems.

https://arxiv.org/abs/2204.14005 2022-04-29T00:00:00Z 2023-08-01T00:00:00Z Arpan DasShishira MahuntaBijay Kumar AgarwallaVictor Mukherjee

We use Floquet formalism to study fluctuations in periodically modulated continuous quantum thermal machines. We present a generic theory for such machines, followed by specific examples of sinusoidal, optimal, and circular modulations respectively. The thermodynamic uncertainty relations (TUR) hold for all modulations considered. Interestingly, in the case of sinusoidal modulation, the TUR ratio assumes a minimum at the heat engine to refrigerator transition point, while the Chopped Random Basis (CRAB) optimization protocol allows us to keep the ratio small for a wide range of modulation frequencies. Furthermore, our numerical analysis suggests that TUR can show signatures of heat engine to refrigerator transition, for more generic modulation schemes. We also study bounds in fluctuations in the efficiencies of such machines; our results indicate that fluctuations in efficiencies are bounded from above for a refrigerator, and from below for an engine. Overall, this study emphasizes the crucial role played by different modulation schemes in designing practical quantum thermal machines.

https://arxiv.org/abs/2104.07687 2021-04-15T00:00:00Z 2021-04-15T00:00:00Z Matthias M. MüllerRessa S. SaidFedor JelezkoTommaso CalarcoSimone Montangero

The Chopped RAndom Basis (CRAB) ansatz for quantum optimal control has been proven to be a versatile tool to enable quantum technology applications, quantum computing, quantum simulation, quantum sensing, and quantum communication. Its capability to encompass experimental constraints -- while maintaining an access to the usually trap-free control landscape -- and to switch from open-loop to closed-loop optimization (including with remote access -- or RedCRAB) is contributing to the development of quantum technology on many different physical platforms. In this review article we present the development, the theoretical basis and the toolbox for this optimization algorithm, as well as an overview of the broad range of different theoretical and experimental applications that exploit this powerful technique.

https://arxiv.org/abs/2007.07381 2020-07-14T00:00:00Z 2020-07-22T00:00:00Z Luca InnocentiGabriele De ChiaraMauro PaternostroRicardo Puebla

The fast and faithful preparation of the ground state of quantum systems is a challenging task but crucial for several applications in the realm of quantum-based technologies. Decoherence poses a limit to the maximum time-window allowed to an experiment to faithfully achieve such desired states. This is of particular significance in critical systems, where the vanishing energy gap challenges an adiabatic ground state preparation. We show that a bang-bang protocol, consisting of a time evolution under two different values of an externally tunable parameter, allows for a high-fidelity ground state preparation in evolution times no longer than those required by the application of standard optimal control techniques, such as the chopped-random basis quantum optimization. In addition, owing to their reduced number of variables, such bang-bang protocols are very well suited to optimization purposes, reducing the increasing computational cost of other optimal control protocols. We benchmark the performance of such approach through two paradigmatic models, namely the Landau-Zener and the Lipkin-Meshkov-Glick model. Remarkably, the critical ground state of the latter model can be prepared with a high fidelity in a total evolution time that scales slower than the inverse of the vanishing energy gap.

https://arxiv.org/abs/2006.16113 2020-06-29T00:00:00Z 2023-01-20T00:00:00Z Matthias M. MüllerStefano GherardiniTommaso CalarcoSimone MontangeroFilippo Caruso

Accurate manipulations of an open quantum system require a deep knowledge of its controllability properties and the information content of the implemented control fields. By using tools of information and quantum optimal control theory, we provide analytical bounds (information-time bounds) to characterize our capability to control the system when subject to arbitrary sources of noise. Moreover, since the presence of an external noise field induces open quantum system dynamics, we also show that the results provided by the information-time bounds are in very good agreement with the Kofman-Kurizki universal formula describing decoherence processes. Finally, we numerically test the scaling of the control accuracy as a function of the noise parameters, by means of the dressed chopped random basis (dCRAB) algorithm for quantum optimal control.

https://arxiv.org/abs/1904.04712 2019-04-09T00:00:00Z 2019-04-09T00:00:00Z Vegard B. SørdalJoakim Bergli

Machine learning techniques based on artificial neural networks have been successfully applied to solve many problems in science. One of the most interesting domains of machine learning, reinforcement learning, has natural applicability for optimization problems in physics. In this work we use deep reinforcement learning and Chopped Random Basis optimization, to solve an optimization problem based on the insertion of an off-center barrier in a quantum Szilard engine. We show that using designed protocols for the time dependence of the barrier strength, we can achieve an equal splitting of the wave function (1/2 probability to find the particle on either side of the barrier) even for an asymmetric Szilard engine in such a way that no information is lost when measuring which side the particle is found. This implies that the asymmetric non-adiabatic Szilard engine can operate with the same efficiency as the traditional Szilard engine, with adiabatic insertion of a central barrier. We compare the two optimization methods, and demonstrate the advantage of reinforcement learning when it comes to constructing robust and noise-resistant protocols.

https://arxiv.org/abs/1903.12651 2019-03-29T00:00:00Z 2019-03-29T00:00:00Z Shu-Hao WuMayra AmezcuaHailin Wang

We report theoretical studies of adiabatic population transfer using dressed spin states. Quantum optimal control using the algorithm of Chopped Random Basis (CRAB) has been implemented in a negatively charged diamond nitrogen vacancy center that is coupled to a strong and resonant microwave field. We show that the dressed spin states are highly effective in suppressing effects of spin dephasing on adiabatic population transfer. The numerical simulation also demonstrates that CRAB-based quantum optimal control can enable an efficient and robust adiabatic population transfer.

https://arxiv.org/abs/1809.09562 2018-09-25T00:00:00Z 2019-04-30T00:00:00Z Oleg V. MorzhinAlexander Pechen

Mathematical problems of optimal control in quantum systems attract high interest in connection with fundamental questions and existing and prospective applications. An important problem is the development of methods for constructing controls for quantum systems. One of the commonly used methods is the Krotov method initially proposed beyond quantum control in the articles by V.F.~Krotov and I.N.~Feldman (1978, 1983). The method was used to develop a novel approach for finding optimal controls for quantum systems in [D.J. Tannor, V. Kazakov, V. Orlov, In: Time-Dependent Quantum Molecular Dynamics, Boston, Springer, 347--360 (1992)] and [J.~Somlói, V.A.~Kazakov, D.J.~Tannor, Chem. Phys., 172:1, 85--98 (1993)], and in many works of various scientists, as described in details in this review. The review discusses mathematical aspects of this method for optimal control of closed quantum systems. It outlines various modifications with respect to defining the improvement function (which in most cases is linear or linear-quadratic), constraints on control spectrum and on the states of a quantum system, regularizers, etc. The review describes applications of the Krotov method to control of molecular dynamics, manipulation of Bose-Einstein condensate, quantum gate generation. We also discuss comparison with GRAPE (GRadient Ascent Pulse Engineering), CRAB (Chopped Random-Basis), the Zhu---Rabitz and the Maday---Turinici methods.

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/1506.04601 2015-06-15T00:00:00Z 2016-04-06T00:00:00Z Niklas RachMatthias M. MüllerTommaso CalarcoSimone Montangero

In quantum optimal control theory the success of an optimization algorithm is highly influenced by how the figure of merit to be optimized behaves as a function of the control field, i.e. by the control landscape. Constraints on the control field introduce local minima in the landscape --false traps-- which might prevent an efficient solution of the optimal control problem. Rabitz et al. [Science 303, 1998 (2004)] showed that local minima occur only rarely for unconstrained optimization. Here, we extend this result to the case of bandwidth-limited control pulses showing that in this case one can eliminate the false traps arising from the constraint. Based on this theoretical understanding, we modify the Chopped Random Basis (CRAB) optimal control algorithm and show that this development exploits the advantages of both (unconstrained) gradient algorithms and of truncated basis methods, allowing to always follow the gradient of the unconstrained landscape by bandwidth-limited control functions. We study the effects of additional constraints and show that for reasonable constraints the convergence properties are still maintained. Finally, we numerically show that this approach saturates the theoretical bound on the minimal bandwidth of the control needed to optimally drive the system.

https://arxiv.org/abs/1504.02858 2015-04-11T00:00:00Z 2015-04-11T00:00:00Z Matthias M. MüllerUlrich PoschingerTommaso CalarcoSimone MontangeroFerdinand Schmidt-Kaler

We propose a protocol for measurement of the phonon number distribution of a harmonic oscillator based on selective mapping to a discrete spin-1/2 degree of freedom. We consider a system of a harmonically trapped ion, where a transition between two long lived states can be driven with resolved motional sidebands. The required unitary transforms are generated by amplitude-modulated polychromatic radiation fields, where the time-domain ramps are obtained from numerical optimization by application of the Chopped RAndom Basis (CRAB) algorithm. We provide a detailed analysis of the scaling behavior of the attainable fidelities and required times for the mapping transform with respect to the size of the Hilbert space. As one application we show how the mapping can be employed as a building block for experiments which require measurement of the work distribution of a quantum process.

https://arxiv.org/abs/1103.0855 2011-03-04T00:00:00Z 2011-08-22T00:00:00Z Tommaso CanevaTommaso CalarcoSimone Montangero

In this work we describe in detail the "Chopped RAndom Basis" (CRAB) optimal control technique recently introduced to optimize t-DMRG simulations [arXiv:1003.3750]. Here we study the efficiency of this control technique in optimizing different quantum processes and we show that in the considered cases we obtain results equivalent to those obtained via different optimal control methods while using less resources. We propose the CRAB optimization as a general and versatile optimal control technique.