Efficient cavity control with SNAP gates

Fösel, T.; Krastanov, Stefan; Marquardt, Florian; Jiang, Liang

doi:10.48550/arxiv.2004.14256

Cited by 14 publications

(21 citation statements)

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“…Recently it was demonstrated that SNAP can be made first-order path-independent with respect to ancilla qubit decay [65,66]. Furthermore, a linear scaling of the circuit depth T with the state size n can be achieved for this approach [67], while many interesting experimentally achievable states can be prepared with just T ∼ 5. Inspired by this finding, we parametrize our open-loop control circuit as D † (α) SNAP(ϕ) D(α), see Fig.…”

Section: Resultsmentioning

confidence: 99%

“…An automated method for selecting the circuit depth was proposed in Ref. [67], and it can be utilized here to make an educated guess of T .…”

Section: A Preparation Of Oscillator Fock Statesmentioning

confidence: 99%

“…In the partially selective case χτ = 0.4 (pink) the performance is drastically worse. Note that optimization with any other simulation-based approach assuming ideal SNAP, such as [63,67], would exhibit a similar degradation.…”

Section: Preparation Of Arbitrary Statesmentioning

confidence: 99%

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Model-Free Quantum Control with Reinforcement Learning

Sivak,

Eickbusch,

Liu

et al. 2021

Preprint

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Model bias is an inherent limitation of the current dominant approach to optimal quantum control, which relies on a system simulation for optimization of control policies. To overcome this limitation, we propose a circuit-based approach for training a reinforcement learning agent on quantum control tasks in a model-free way. Given a continuously parameterized control circuit, the agent learns its parameters through trial-and-error interaction with the quantum system, using measurements as the only source of information about the quantum state. By focusing on the task of quantum state preparation in a harmonic oscillator coupled to an ancilla qubit, we show how to reward the learning agent using measurements of experimentally available observables. We demonstrate by numerical simulations preparation of arbitrary states using both open-and closed-loop control through adaptive quantum feedback. Our work is of immediate relevance to superconducting circuits and trapped ions platforms where such training can be implemented real-time in an experiment, allowing complete elimination of model bias and the adaptation of quantum control policies to the specific system in which they are deployed.

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Section: Resultsmentioning

confidence: 99%

“…An automated method for selecting the circuit depth was proposed in Ref. [67], and it can be utilized here to make an educated guess of T .…”

Section: A Preparation Of Oscillator Fock Statesmentioning

confidence: 99%

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Model-Free Quantum Control with Reinforcement Learning

Sivak,

Eickbusch,

Liu

et al. 2021

Preprint

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“…It should be noted that the general framework that we employed, pulse engineering via QOC, while proven powerful [27] is not the only known approach to achieve universal synthesis of unitary quantum gates defined in the Fock space for these kind of systems. For instance, the use of selective numberdependent arbitrary phase (SNAP) protocol [28,29] or echoed conditional displacement [30] are strong candidates for the universal control of a singlemode system. However, working with large photonnumber states comes with additional complications in terms of decoherence which are still theoretically not entirely understood [31,32].…”

Section: Discussion and Outlookmentioning

confidence: 99%

Numerical Gate Synthesis for Quantum Heuristics on Bosonic Quantum Processors

Özgüler,

Venturelli

2022

Preprint

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There is a recent surge of interest and insights regarding the interplay of quantum optimal control and variational quantum algorithms. We study the framework in the context of qudits which are, for instance, definable as controllable electromagnetic modes of a superconducting cavity system coupled to a transmon. By employing recent quantum optimal control approaches described in (Petersson and Garcia, 2021), we showcase control of single-qudit operations up to eight states, and two-qutrit operations, mapped respectively onto a single mode and two modes of the resonator. We discuss the results of numerical pulse engineering on the closed system for parametrized gates useful to implement qudit quantum approximate optimization (QAOA) algorithms. The results show that high fidelity (> 0.99) is achievable with sufficient computational effort on HPC for most cases under study, and extension to multiple modes and open, noisy systems are possible. The tailored pulses can be stored and used as calibrated primitives for a future compiler in cQED systems.

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“…During the learning phase, the agent discovers from scratch novel strategies, in a systematic procedure which at first resembles trial and error but later begins to build on insights acquired earlier. That RL in general and deep RL in particular is a powerful approach in quantum physics has by now been demonstrated in a variety of tasks in different areas: in most works so far, these tasks did not yet require real-time feedback involving decision-making * riccardo.porotti@mpl.mpg.de based on physical measurements, but RL already proved itself to be a versatile tool even in those settings [15][16][17][18][19][20][21][22][23][24][25][26]. These publications are part of a larger drive towards the use of machine learning tools for quantum experiments (e.g.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements

Porotti,

Essig,

Huard

et al. 2021

Preprint

Self Cite

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Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in this regard. It may provide new answers to difficult questions, such as whether nonlinear measurements can compensate for linear, constrained control. Here we show that reinforcement learning can successfully discover such feedback strategies, without prior knowledge. We illustrate this for state preparation in a cavity subject to quantum-non-demolition detection of photon number, with a simple linear drive as control. Fock states can be produced and stabilized at very high fidelity. It is even possible to reach superposition states, provided the measurement rates for different Fock states can be controlled as well.

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Efficient cavity control with SNAP gates

Cited by 14 publications

References 0 publications

Model-Free Quantum Control with Reinforcement Learning

Model-Free Quantum Control with Reinforcement Learning

Numerical Gate Synthesis for Quantum Heuristics on Bosonic Quantum Processors

Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements

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