Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation

• URL: http://arxiv.org/abs/2411.19914v2
• Date: Fri, 24 Jan 2025 20:11:41 GMT
• Title: Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation
• Authors: Daniel Alcalde Puente, Matteo Rizzi,
• Abstract summary: This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits. By combining conditionalive measurements with feedback, the protocol learns state preparation strategies that extend beyond unitary-only methods.
• Score: 0.0
• License:
• Abstract: This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns state preparation strategies that extend beyond unitary-only methods, leveraging measurement-based shortcuts to reduce circuit depth. Using the spin-1 Affleck-Kennedy-Lieb-Tasaki state as a benchmark, the protocol learns high-fidelity state preparation by overcoming a family of measurement induced local minima through adjustments of parameter update frequencies and ancilla regularization. Despite these efforts, optimization remains challenging due to the highly non-convex landscapes inherent to variational circuits. The approach is extended to larger systems using translationally invariant ans\"atze and recurrent neural networks for feedback, demonstrating scalability. Additionally, the successful preparation of a specific AKLT state with desired edge modes highlights the potential to discover new state preparation protocols where none currently exist. These results indicate that integrating measurement and feedback into variational quantum algorithms provides a promising framework for quantum state preparation.

Related papers

• Machine Learning for Ground State Preparation via Measurement and Feedback [0.0]
  We present a recurrent neural network-based approach for ground state preparation utilizing mid-circuit measurement and feedback. We demonstrate that performance systematically improves as a larger fraction of ancilla qubits are utilized for measurement and feedback.
  arXiv  Detail & Related papers  (2025-02-10T14:37:34Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Efficient preparation of the AKLT State with Measurement-based Imaginary Time Evolution [2.5938976557097715]
  We propose a method to prepare the ground state of the Affleck-Lieb-Kennedy-Tasaki (AKLT) model deterministically using a measurement-based imaginary time evolution (MITE) approach. We show that the procedure is compatible with qubit-based simulators, and show that using a variational quantum algorithm for circuit recompilation, the measurement operator required for MITE can be well approximated by a circuit with a much shallower circuit depth.
  arXiv  Detail & Related papers  (2023-10-09T18:00:03Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Randomized adaptive quantum state preparation [0.0]
  A cost function is minimized to prepare a desired quantum state through an adaptively constructed quantum circuit. We provide theoretical arguments and numerical evidence that convergence to the target state can be achieved for almost all initial states.
  arXiv  Detail & Related papers  (2023-01-10T20:32:49Z)
• Deterministic constant-depth preparation of the AKLT state on a quantum processor using fusion measurements [0.2007262412327553]
  The ground state of the spin-1 Affleck, Kennedy, Lieb and TasakiAKLT model is a paradigmatic example of both a matrix product state and a symmetry-protected topological phase. Having a nonzero correlation length, the AKLT state cannot be exactly prepared by a constant-depth unitary circuit composed of local gates. We demonstrate that this no-go limit can be evaded by augmenting a constant-depth circuit with fusion measurements.
  arXiv  Detail & Related papers  (2022-10-31T17:58:01Z)
• Potential and limitations of quantum extreme learning machines [55.41644538483948]
  We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
  arXiv  Detail & Related papers  (2022-10-03T09:32:28Z)
• 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)
• Gradient Ascent Pulse Engineering with Feedback [0.0]
  We introduce feedback-GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong measurements. Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise.
  arXiv  Detail & Related papers  (2022-03-08T18:46:09Z)
• Dynamical learning of a photonics quantum-state engineering process [48.7576911714538]
  Experimentally engineering high-dimensional quantum states is a crucial task for several quantum information protocols. We implement an automated adaptive optimization protocol to engineer photonic Orbital Angular Momentum (OAM) states. This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.
  arXiv  Detail & Related papers  (2022-01-14T19:24:31Z)
• 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)

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.