Optimized cross-resonance gate for coupled transmon systems

Kirchhoff, Susanna; Keßler, Torsten; Liebermann, Per J.; Assémat, Elie; Machnes, Shai; Motzoi, Felix; Wilhelm, Frank K.

doi:10.1103/physreva.97.042348

Cited by 63 publications

(52 citation statements)

References 53 publications

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“…Most commonly, GRAPE is applied to piecewise constant pulses, but it can be modified to include filtering [60,61], as we also do here. Each time-step is divided into a number of substeps (giving the resolution) and the filtered pulse is then approximated as being constant within each substep.…”

Section: Constrained Analog Pulsesmentioning

confidence: 99%

Global optimization of quantum dynamics with AlphaZero deep exploration

et al. 2020

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While a large number of algorithms for optimizing quantum dynamics for different objectives have been developed, a common limitation is the reliance on good initial guesses, being either random or based on heuristics and intuitions. Here we implement a tabula rasa deep quantum exploration version of the Deepmind AlphaZero algorithm for systematically averting this limitation. AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters. AlphaZero achieves substantial improvements in both the quality and quantity of good solution clusters compared to earlier methods. It is able to spontaneously learn unexpected hidden structure and global symmetry in the solutions, going beyond even human heuristics. arXiv:1907.05672v1 [quant-ph]

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Section: Constrained Analog Pulsesmentioning

confidence: 99%

Global optimization of quantum dynamics with AlphaZero deep exploration

et al. 2020

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“…That is, T 1 of the system qubit should be larger than ∼ 10 µs, the physical classifier response time for the proper functionality of the physical model. Recent studies report that energy relaxation time ranges between T 1 ∼ 20 − 60 µs depending on the coupling and the optimal noise suppressing pulse shape techniques of the transmon qubits [38,40].…”

Section: Physical Modelmentioning

confidence: 99%

A steady state quantum classifier

2019

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We report that under some specific conditions a single qubit model weakly interacting with information environments can be referred to as a quantum classifier. We exploit the additivity and the divisibility properties of the completely positive (CP) quantum dynamical maps in order to obtain an open quantum classifier. The steady state response of the system with respect to the input parameters was numerically investigated and it's found that the response of the open quantum dynamics at steady state acts non-linearly with respect to the input data parameters. We also demonstrate the linear separation of the quantum data instances that reflects the success of the functionality of the proposed model both for ideal and experimental conditions. Superconducting circuits were pointed out as the physical model to implement the theoretical model with possible imperfections.

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“…This method has found application in unitary engineering [45], as it provides an accurate and efficient tool for evaluating partial derivatives of U(T). We also note that the first order version the method outlined here has been observed in [46,47] for evaluating functional derivatives of U(T) with respect to control amplitudes.…”

Section: T T T U T a T U T U T A T U Tmentioning

confidence: 97%

Engineering effective Hamiltonians

et al. 2019

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In the field of quantum control, effective Hamiltonian engineering is a powerful tool that utilizes perturbation theory to mitigate or enhance the effect that a variation in the Hamiltonian has on the evolution of the system. Here, we provide a general framework for computing arbitrary timedependent perturbation theory terms, as well as their gradients with respect to control variations, enabling the use of gradient methods for optimizing these terms. In particular, we show that effective Hamiltonian engineering is an instance of a bilinear control problem-the same general problem class as that of standard unitary design-and hence the same optimization algorithms apply. We demonstrate this method in various examples, including decoupling, recoupling, and robustness to control errors and stochastic errors. We also present a control engineering example that was used in experiment, demonstrating the practical feasibility of this approach.

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Optimized cross-resonance gate for coupled transmon systems

Cited by 63 publications

References 53 publications

Global optimization of quantum dynamics with AlphaZero deep exploration

Global optimization of quantum dynamics with AlphaZero deep exploration

A steady state quantum classifier

Engineering effective Hamiltonians

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