Robust manipulation of superconducting qubits in the presence of fluctuations

Hybrid Filtering for a Class of Nonlinear Quantum Systems Subject to Classical Stochastic Disturbances

A hybrid quantum-classical filtering problem, where a qubit system is disturbed by a classical stochastic process, is investigated. The strategy is to model the classical disturbance by using an optical cavity. The relations between classical disturbances and the cavity analog system are analyzed. The dynamics of the enlarged quantum network system, which includes a qubit system and a cavity system, are derived. A stochastic master equation for the qubit-cavity hybrid system is given, based on which estimates for the state of the cavity system and the classical signal are obtained. The quantum-extended Kalman filter is employed to achieve efficient computation. The numerical results are presented to illustrate the effectiveness of our methods.

Learning-Based Quantum Robust Control: Algorithm, Applications, and Experiments

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH₂BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks.

Robust Learning Control Design for Quantum Unitary Transformations

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

Quantum Genetic Learning Control of Quantum Ensembles with Hamiltonian Uncertainties

In this paper, a new method for controlling a quantum ensemble that its members have uncertainties in Hamiltonian parameters is designed. Based on combining the sampling-based learning control (SLC) and a new quantum genetic algorithm (QGA) method, the control of an ensemble of a two-level quantum system with Hamiltonian uncertainties is achieved. To simultaneously transfer the ensemble members to a desired state, an SLC algorithm is designed. For reducing the transfer error significantly, an optimization problem is defined. Considering the advantages of QGA and the nature of the problem, the optimization problem by using the QGA method is solved. For this purpose, N samples through sampling of the uncertainty parameters via uniform distribution are generated and an augmented system is also created. By using QGA in the training step, the best control signal is obtained. To test the performance and validation of the method, the obtained control is implemented for some random selected samples. A couple of examples are simulated for investigating the proposed model. The results of the simulations indicate the effectiveness and the advantages of the proposed method.

Learning robust pulses for generating universal quantum gates

Constructing a set of universal quantum gates is a fundamental task for quantum computation. The existence of noises, disturbances and fluctuations is unavoidable during the process of implementing quantum gates for most practical quantum systems. This paper employs a sampling-based learning method to find robust control pulses for generating a set of universal quantum gates. Numerical results show that the learned robust control fields are insensitive to disturbances, uncertainties and fluctuations during the process of realizing universal quantum gates.

Sampling-based robust control in synchronizing collision with shaped laser pulses: an application in charge transfer for H+ + D → H + D+

In this paper, we show that robust laser pulses can be obtained by a sampling-based method to achieve a desired charge transfer probability with limited sensitivity to the arrival time of laser pulses.