Optimal control approach to dynamical suppression of decoherence of a qubit

Jirari, H.

doi:10.1209/0295-5075/87/40003

Cited by 19 publications

(26 citation statements)

References 25 publications

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“… 12 − 14 For example, AD has been used for quantum control in open-quantum systems to find an optimal time-dependent field to obtain a specific state. 15 , 16 Furthermore, new libraries developed in the context of deep learning 9 , 17 − 19 make AD techniques even more accessible to a broader community. For instance, Leung et.…”

Section: Introductionmentioning

confidence: 99%

Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree–Fock

et al. 2018

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Automatic differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, without the need to explicitly add any additional functions. Thus, AD has great potential in quantum chemistry, where gradients are omnipresent but also difficult to obtain, and researchers typically spend a considerable amount of time finding suitable analytical forms when implementing derivatives. Here, we demonstrate that AD can be used to compute gradients with respect to any parameter throughout a complete quantum chemistry method. We present DiffiQult, a Hartree–Fock implementation, entirely differentiated with the use of AD tools. DiffiQult is a software package written in plain Python with minimal deviation from standard code which illustrates the capability of AD to save human effort and time in implementations of exact gradients in quantum chemistry. We leverage the obtained gradients to optimize the parameters of one-particle basis sets in the context of the floating Gaussian framework.

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Section: Introductionmentioning

confidence: 99%

Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree–Fock

et al. 2018

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“…The second integral penalizes the field fluency E = tF 0 dt ε 2 (t) with weight ν > 0. In principle one can use the Pontryagin's minimum principle to treat our optimal control problem and derive the gradient for the cost functional J(ε) [15][16][17][18][19][20]. However, for the off-diagonal spin-boson model studied here, the response of the system to the variation of the control ε(t) is determined by the master equation Eq.…”

Section: Quantum Optimal Control Problemmentioning

confidence: 99%

Optimal state transfer of a single dissipative two-level system

Jirari¹,

2016

Eur. Phys. J. B

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Optimal state transfer of a single two-level system (TLS) coupled to an Ohmic boson bath via off-diagonal TLS-bath coupling is studied by using optimal control theory. In the weak system-bath coupling regime where the time-dependent Bloch-Redfield formalism is applicable, we obtain the Bloch equation to probe the evolution of the dissipative TLS in the presence of a time-dependent external control field. By using the automatic differentiation technique to compute the gradient for the cost functional, we calculate the optimal transfer integral profile that can achieve an ideal transfer within a dimer system in the Fenna-Matthews-Olson (FMO) model. The robustness of the control profile against temperature variation is also analyzed.

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“…Open algorithms include open versions of GRAPE [23] and Krotov methods [24]. Density matrix centered algorithms were successfully used in some applications of small system size [28][29][30][31]. While traditional propagation * abdelhafez@uchicago.edu requires consistent matrix multiplications and exponentials of superoperators of dimension d 2 × d 2 , some more recent approaches rely on propagating the density matrix through different integration methods [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation

2019

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We present a gradient-based optimal-control technique for open quantum systems that utilizes quantum trajectories to simulate the quantum dynamics during optimization. Using trajectories allows for optimizing open systems with less computational cost than the regular density matrix approaches in most realistic optimization problems. We introduce an improved-sampling algorithm which minimizes the number of trajectories needed per optimization iteration. Together with employing stochastic gradient descent techniques, this reduces the complexity of optimizing many realistic open quantum systems to the complexity encountered with closed systems. Our optimizer harnesses automatic differentiation to provide flexibility in optimization and to suit the different constraints and diverse parameter regimes of real-life experiments. We utilize the optimizer in a variety of applications to demonstrate how the use of quantum trajectories significantly reduces the computation complexity while achieving a multitude of simultaneous optimization targets. Demonstrated targets include high state-transfer fidelities despite dissipation, faster gate times and maximization of qubit-readout fidelity while maintaining the quantum non-demolition nature of the measurement and allowing for subsequent fast resonator reset. arXiv:1901.05541v1 [quant-ph]

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Optimal control approach to dynamical suppression of decoherence of a qubit

Cited by 19 publications

References 25 publications

Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree–Fock

Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree–Fock

Optimal state transfer of a single dissipative two-level system

Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation

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