Bayesian optimization for state engineering of quantum gases

• URL: http://arxiv.org/abs/2404.18234v1
• Date: Sun, 28 Apr 2024 16:24:28 GMT
• Title: Bayesian optimization for state engineering of quantum gases
• Authors: Gabriel Müller, V. J. Martínez-Lahuerta, Ivan Sekulic, Sven Burger, Philipp-Immanuel Schneider, Naceur Gaaloul,
• Abstract summary: State engineering of quantum objects is a central requirement in most implementations. We propose Bayesian optimization based on multi-output Gaussian processes to learn the quantum state's physical properties from few simulations only. This paves the way to efficient state engineering of complex quantum systems.
• Score: 1.6144007597015433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State engineering of quantum objects is a central requirement in most implementations. In the cases where the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theoretically proposed and experimentally realized. For more complex systems, however, such as multi-component quantum gases, simplifying assumptions do not apply anymore and the optimization techniques become computationally impractical. Here, we propose Bayesian optimization based on multi-output Gaussian processes to learn the quantum state's physical properties from few simulations only. We evaluate its performance on an optimization study case of diabatically transporting a Bose-Einstein condensate while keeping it in its ground state, and show that within only few hundreds of executions of the underlying physics simulation, we reach a competitive performance with other protocols. While restricting this benchmarking to well known approximations for straightforward comparisons, we expect a similar performance when employing more involving models, which are computationally more challenging. This paves the way to efficient state engineering of complex quantum systems.

Related papers

• Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms [10.941053143198092]
  There has been a growing interest in qumodes due to advancements in the field and their potential applications. This paper aims to bridge this gap by providing performance benchmarks of various parameters used in state preparation with Variational Quantum Algorithms.
  arXiv  Detail & Related papers  (2024-05-07T17:15:58Z)
• Surrogate optimization of variational quantum circuits [1.0546736060336612]
  Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE.
  arXiv  Detail & Related papers  (2024-04-03T18:00:00Z)
• Bayesian Optimization for Robust State Preparation in Quantum Many-Body Systems [0.0]
  We apply Bayesian optimization to a state-preparation protocol recently implemented in an ultracold-atom system. Compared to manual ramp design, we demonstrate the superior performance of our optimization approach in a numerical simulation. The proposed protocol and workflow will pave the way toward the realization of more complex many-body quantum states in experiments.
  arXiv  Detail & Related papers  (2023-12-14T18:59:55Z)
• Sparse Quantum State Preparation for Strongly Correlated Systems [0.0]
  In principle, the encoding of the exponentially scaling many-electron wave function onto a linearly scaling qubit register offers a promising solution to overcome the limitations of traditional quantum chemistry methods. An essential requirement for ground state quantum algorithms to be practical is the initialisation of the qubits to a high-quality approximation of the sought-after ground state. Quantum State Preparation (QSP) allows the preparation of approximate eigenstates obtained from classical calculations, but it is frequently treated as an oracle in quantum information.
  arXiv  Detail & Related papers  (2023-11-06T18:53:50Z)
• Importance sampling for stochastic quantum simulations [68.8204255655161]
  We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
  arXiv  Detail & Related papers  (2022-12-12T15:06:32Z)
• Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization [58.720142291102135]
  We introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model.
  arXiv  Detail & Related papers  (2022-09-05T07:23:03Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• Efficient State Preparation for Quantum Amplitude Estimation [0.951828574518325]
  Quantum Amplitude Estimation can achieve a quadratic speed-up for applications classically solved by Monte Carlo simulation. Currently known efficient techniques require problems based on log-concave probability distributions, involve learning an unknown distribution from empirical data, or fully rely on quantum arithmetic. We introduce an approach to simplify state preparation, together with a circuit optimization technique, both of which can help reduce the circuit complexity for QAE state preparation significantly.
  arXiv  Detail & Related papers  (2020-05-15T18:00:01Z)

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.