Adaptive quantum state tomography with iterative particle filtering
- URL: http://arxiv.org/abs/2010.12867v2
- Date: Wed, 15 Sep 2021 07:17:00 GMT
- Title: Adaptive quantum state tomography with iterative particle filtering
- Authors: Syed Muhammad Kazim and Ahmad Farooq and Junaid ur Rehman and Hyundong
Shin
- Abstract summary: We present an adaptive particle filter based QST protocol which improves the scaling of fidelity compared to nonadaptive Bayesian schemes for arbitrary multi-qubit states.
Numerical examples and implementation on IBM quantum devices demonstrate improved performance for arbitrary quantum states.
- Score: 7.943024117353315
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Several Bayesian estimation based heuristics have been developed to perform
quantum state tomography (QST). Their ability to quantify uncertainties using
region estimators and include a priori knowledge of the experimentalists makes
this family of methods an attractive choice for QST. However, specialized
techniques for pure states do not work well for mixed states and vice versa. In
this paper, we present an adaptive particle filter (PF) based QST protocol
which improves the scaling of fidelity compared to nonadaptive Bayesian schemes
for arbitrary multi-qubit states. This is due to the protocol's unabating
perseverance to find the states's diagonal bases and more systematic handling
of enduring problems in popular PF methods relating to the subjectivity of
informative priors and the invalidity of particles produced by resamplers.
Numerical examples and implementation on IBM quantum devices demonstrate
improved performance for arbitrary quantum states and the application readiness
of our proposed scheme.
Related papers
- Capturing many-body correlation effects with quantum and classical
computing [40.7853309684189]
We show the efficiency of Quantum Phase Estor (QPE) in identifying core-level states relevant to x-ray photoelectron spectroscopy.
We compare and validate the QPE predictions with exact diagonalization and real-time equation-of-motion coupled cluster formulations.
arXiv Detail & Related papers (2024-02-18T01:26:45Z) - Ansatz-Agnostic Exponential Resource Saving in Variational Quantum
Algorithms Using Shallow Shadows [5.618657159109373]
Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage.
We present a protocol based on shallow shadows that achieves similar levels of savings for almost any shallow ansatz studied in the literature.
We show that two important applications in quantum information for which VQAs can be a powerful option, namely variational quantum state preparation and variational quantum circuit synthesis.
arXiv Detail & Related papers (2023-09-09T11:00:39Z) - Fermionic Adaptive Sampling Theory for Variational Quantum Eigensolvers [0.0]
ADAPT-VQE suffers from a significant measurement overhead when estimating the importance of operators in the wave function.
We proposeFAST-VQE, a method for selecting operators based on importance metrics solely derived from the populations of Slater determinants in the wave function.
arXiv Detail & Related papers (2023-03-13T18:57:18Z) - Demonstration of machine-learning-enhanced Bayesian quantum state
estimation [0.0]
We experimentally realize an approach for defining custom prior distributions that are automatically tuned using machine learning.
We show that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution.
arXiv Detail & Related papers (2022-12-15T18:41:15Z) - 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) - State Preparation Boosters for Early Fault-Tolerant Quantum Computation [0.0]
We introduce the method of ground state boosting, which uses a limited-depth quantum circuit to reliably increase the overlap with the ground state.
This circuit, which we call a booster, can be used to augment an ansatz from VQE or be used as a stand-alone state preparation method.
arXiv Detail & Related papers (2022-02-14T19:00:13Z) - Bayesian homodyne and heterodyne tomography [0.2446672595462589]
Continuous-variable (CV) photonic states are of increasing interest in quantum information science.
We introduce a complete Bayesian quantum state tomography workflow capable of inferring generic CV states measured by homodyne or heterodyne detection.
arXiv Detail & Related papers (2022-02-07T20:26:16Z) - Robust preparation of Wigner-negative states with optimized
SNAP-displacement sequences [41.42601188771239]
We create non-classical states of light in three-dimensional microwave cavities.
These states are useful for quantum computation.
We show that this way of creating non-classical states is robust to fluctuations of the system parameters.
arXiv Detail & Related papers (2021-11-15T18:20:38Z) - Quantum Error Mitigation Relying on Permutation Filtering [84.66087478797475]
We propose a general framework termed as permutation filters, which includes the existing permutation-based methods as special cases.
We show that the proposed filter design algorithm always converges to the global optimum, and that the optimal filters can provide substantial improvements over the existing permutation-based methods.
arXiv Detail & Related papers (2021-07-03T16:07:30Z) - On exploring practical potentials of quantum auto-encoder with
advantages [92.19792304214303]
Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of dimensionality encountered in quantum physics.
We prove that QAE can be used to efficiently calculate the eigenvalues and prepare the corresponding eigenvectors of a high-dimensional quantum state.
We devise three effective QAE-based learning protocols to solve the low-rank state fidelity estimation, the quantum Gibbs state preparation, and the quantum metrology tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - 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.