Solving quantum optimal control problems using projection-operator-based
Newton steps
- URL: http://arxiv.org/abs/2305.17630v2
- Date: Mon, 8 Jan 2024 21:35:10 GMT
- Title: Solving quantum optimal control problems using projection-operator-based
Newton steps
- Authors: Jieqiu Shao, Mantas Naris, John Hauser and Marco M. Nicotra
- Abstract summary: The paper significantly improves prior versions of the quantum projection operator by introducing a regulator that stabilizes the solution estimate at every iteration.
This modification is shown to not only improve the convergence rate of the algorithm, but also steer the solver towards better local minima.
- Score: 0.25602836891933073
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Quantum Projection Operator-Based NewtonMethod for Trajectory
Optimization (Q-PRONTO) is a numerical method for solving quantum optimal
control problems. This paper significantly improves prior versions of the
quantum projection operator by introducing a regulator that stabilizes the
solution estimate at every iteration. This modification is shown to not only
improve the convergence rate of the algorithm, but also steer the solver
towards better local minima compared to the unregulated case. Numerical
examples showcase how Q-PRONTO can be used to solve multi-input quantum optimal
control problems featuring time-varying costs and undesirable populations that
ought to be avoided during the transient.
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) - Feedback-Based Quantum Algorithm for Constrained Optimization Problems [0.6554326244334868]
We introduce a new operator that encodes the problem's solution as its ground state.
We show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit.
arXiv Detail & Related papers (2024-06-12T12:58:43Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Binary Control Pulse Optimization for Quantum Systems [2.887393074590696]
Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations.
We apply different optimization algorithms and techniques to improve computational efficiency and solution quality.
Our algorithms can obtain high-quality control results, as demonstrated by numerical studies on diverse quantum control examples.
arXiv Detail & Related papers (2022-04-12T12:58:55Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - A Projection Operator-based Newton Method for the Trajectory
Optimization of Closed Quantum Systems [0.0]
This paper develops a new general purpose solver for quantum optimal control based on the PRojection Operator Newton method for Trajectory Optimization, or PRONTO.
Specifically, the proposed approach uses a projection operator to incorporate the Schr"odinger equation directly into the cost function, which is then minimized using a quasi-Newton method.
The resulting method guarantees monotonic convergence at every iteration and quadratic convergence in proximity of the solution.
arXiv Detail & Related papers (2021-11-16T21:49:23Z) - Quantum Approximate Optimization Algorithm with Adaptive Bias Fields [4.03537866744963]
The quantum approximate optimization algorithm (QAOA) transforms a simple many-qubit wavefunction into one which encodes a solution to a difficult classical optimization problem.
In this paper, the QAOA is modified by updating the operators themselves to include local fields, using information from the measured wavefunction at the end of one step to improve the operators at later steps.
arXiv Detail & Related papers (2021-05-25T13:51:09Z) - Direct Optimal Control Approach to Laser-Driven Quantum Particle
Dynamics [77.34726150561087]
We propose direct optimal control as a robust and flexible alternative to indirect control theory.
The method is illustrated for the case of laser-driven wavepacket dynamics in a bistable potential.
arXiv Detail & Related papers (2020-10-08T07:59:29Z) - Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.
We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT)
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
arXiv Detail & Related papers (2020-10-01T18:14:11Z) - Space-efficient binary optimization for variational computing [68.8204255655161]
We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem.
We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
arXiv Detail & Related papers (2020-09-15T18:17:27Z) - Cross Entropy Hyperparameter Optimization for Constrained Problem
Hamiltonians Applied to QAOA [68.11912614360878]
Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications.
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
arXiv Detail & Related papers (2020-03-11T13:52:41Z)
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.