Quantum proportional-integral (PI) control
- URL: http://arxiv.org/abs/2007.13853v2
- Date: Sun, 13 Dec 2020 15:23:36 GMT
- Title: Quantum proportional-integral (PI) control
- Authors: Hui Chen, Hanhan Li, Felix Motzoi, Leigh S. Martin, K. Birgitta
Whaley, Mohan Sarovar
- Abstract summary: We apply formalism to two canonical quantum feedback control problems.
For entanglement generation the best strategy can be a combined PI strategy when the measurement efficiency is less than one.
For harmonic state stabilization we find that P feedback shows the best performance when actuation of both position and momentum feedback is possible.
- Score: 6.207388432844965
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Feedback control is an essential component of many modern technologies and
provides a key capability for emergent quantum technologies. We extend existing
approaches of direct feedback control in which the controller applies a
function directly proportional to the output signal (P feedback), to strategies
in which feedback determined by an integrated output signal (I feedback), and
to strategies in which feedback consists of a combination of P and I terms. The
latter quantum PI feedback constitutes the analog of the widely used
proportional-integral feedback of classical control. All of these strategies
are experimentally feasible and require no complex state estimation. We apply
the resulting formalism to two canonical quantum feedback control problems,
namely, generation of an entangled state of two remote qubits, and
stabilization of a harmonic oscillator under thermal noise under conditions of
arbitrary measurement efficiency. These two problems allow analysis of the
relative benefits of P, I, and PI feedback control. We find that for the
two-qubit remote entanglement generation the best strategy can be a combined PI
strategy when the measurement efficiency is less than one. In contrast, for
harmonic state stabilization we find that P feedback shows the best performance
when actuation of both position and momentum feedback is possible, while when
only actuation of position is available, I feedback consistently shows the best
performance, although feedback delay is shown to improve the performance of a P
strategy here.
Related papers
- FOCQS: Feedback Optimally Controlled Quantum States [0.0]
Feedback-based quantum algorithms, such as FALQON, avoid fine-tuning problems but at the cost of additional circuit depth and a lack of convergence guarantees.
We develop an analytic framework to use it to perturbatively update previous control layers.
This perturbative methodology, which we call Feedback Optimally Controlled Quantum States (FOCQS), can be used to improve the results of feedback-based algorithms.
arXiv Detail & Related papers (2024-09-23T18:00:06Z) - Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency.
In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution.
Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
arXiv Detail & Related papers (2024-04-05T17:58:37Z) - Robust Quantum Control via a Model Predictive Control Strategy [4.197316670989004]
This article presents a robust control strategy for a two-level quantum system subject to bounded uncertainties.
We present theoretical results to guarantee the stability of the TOMPC algorithm.
Numerical simulations demonstrate that, in the presence of uncertainties, our quantum TOMPC algorithm enhances the robustness and steers the state to the desired state with high fidelity.
arXiv Detail & Related papers (2024-02-12T04:05:54Z) - Real-time feedback protocols for optimizing fault-tolerant two-qubit
gate fidelities in a silicon spin system [0.2981781876202281]
Several groups have demonstrated two-qubit gate fidelities in semiconductor spin qubit systems above 99%.
We present several single- and two-qubit parameter feedback protocols, optimised for and implemented in state-of-the-art fast FPGA hardware.
We use wavelet-based analysis on the collected feedback data to gain insight into the different sources of noise in the system.
arXiv Detail & Related papers (2023-09-21T23:45:13Z) - Stabilizing two-qubit entanglement with dynamically decoupled active
feedback [0.0]
We analyze a protocol for stabilizing a maximally entangled state of two noninteracting qubits.
We show that robust stabilization with near-unit fidelity can be achieved even in the presence of realistic nonidealities.
arXiv Detail & Related papers (2023-08-07T21:59:36Z) - Optimal State Manipulation for a Two-Qubit System Driven by Coherent and
Incoherent Controls [77.34726150561087]
State preparation is important for optimal control of two-qubit quantum systems.
We exploit two physically different coherent control and optimize the Hilbert-Schmidt target density matrices.
arXiv Detail & Related papers (2023-04-03T10:22:35Z) - Age of Semantics in Cooperative Communications: To Expedite Simulation
Towards Real via Offline Reinforcement Learning [53.18060442931179]
We propose the age of semantics (AoS) for measuring semantics freshness of status updates in a cooperative relay communication system.
We derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework.
We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset.
arXiv Detail & Related papers (2022-09-19T11:55:28Z) - Robust and Adaptive Temporal-Difference Learning Using An Ensemble of
Gaussian Processes [70.80716221080118]
The paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning.
The OS-GPTD approach is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs.
To alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme.
arXiv Detail & Related papers (2021-12-01T23:15:09Z) - Stochastic optimization for learning quantum state feedback control [16.4432244108711]
We present a general framework for training deep feedback networks for open quantum systems with quantum nondemolition measurement.
We demonstrate that this method is efficient due to inherent parallelizability, robust to open system interactions, and outperforms landmark state feedback control results in simulation.
arXiv Detail & Related papers (2021-11-18T19:00:06Z) - Reinforcement learning-enhanced protocols for coherent
population-transfer in three-level quantum systems [50.591267188664666]
We deploy a combination of reinforcement learning-based approaches and more traditional optimization techniques to identify optimal protocols for population transfer.
Our approach is able to explore the space of possible control protocols to reveal the existence of efficient protocols.
The new protocols that we identify are robust against both energy losses and dephasing.
arXiv Detail & Related papers (2021-09-02T14:17:30Z) - Gaussian Process-based Min-norm Stabilizing Controller for
Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
We propose a novel compound kernel that captures the control-affine nature of the problem.
We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
arXiv Detail & Related papers (2020-11-14T01:27:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.