Learning Gaussian Operations and the Matchgate Hierarchy
- URL: http://arxiv.org/abs/2407.12649v2
- Date: Wed, 24 Jul 2024 09:04:00 GMT
- Title: Learning Gaussian Operations and the Matchgate Hierarchy
- Authors: Joshua Cudby, Sergii Strelchuk,
- Abstract summary: We introduce an infinite family of unitary gates, called the Matchgate Hierarchy, with a similar structure to the Clifford Hierarchy.
We show that the Clifford Hierarchy is contained within the Matchgate Hierarchy and how operations at any level of the hierarchy can be efficiently learned.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Learning an unknown quantum process is a central task for validation of the functioning of near-term devices. The task is generally hard, requiring exponentially many measurements if no prior assumptions are made on the process. However, an interesting feature of the classically-simulable Clifford group is that unknown Clifford operations may be efficiently determined from a black-box implementation. We extend this result to the important class of fermionic Gaussian operations. These operations have received much attention due to their close links to fermionic linear optics. We then introduce an infinite family of unitary gates, called the Matchgate Hierarchy, with a similar structure to the Clifford Hierarchy. We show that the Clifford Hierarchy is contained within the Matchgate Hierarchy and how operations at any level of the hierarchy can be efficiently learned.
Related papers
- Entangling gates from cabling of knots [1.450261153230204]
We discuss how to construct an efficient realization of a two qubit gate in topological quantum computer.
We present some examples of these operations for different parameters of the theory.
arXiv Detail & Related papers (2024-12-30T13:21:21Z) - OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation [69.37484603556307]
Un Semantic segmenting (USS) involves segmenting images without relying on predefined labels.
We introduce a novel approach called Optimally Matched Hierarchy (OMH) to simultaneously address the above issues.
Our OMH yields better unsupervised segmentation performance compared to existing USS methods.
arXiv Detail & Related papers (2024-03-11T09:46:41Z) - Characterising semi-Clifford gates using algebraic sets [0.0]
We study the sets of gates of the third-level of the Clifford hierarchy and their distinguished subsets of nearly diagonal' semi-Clifford gates.
Semi-Clifford gates are important because they can be implemented with far more efficient use of these resource states.
arXiv Detail & Related papers (2023-09-26T18:41:57Z) - A Rubik's Cube inspired approach to Clifford synthesis [0.14504054468850663]
The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis.
We develop a machine learning approach for Clifford synthesis based on learning an approximation to the distance to the identity.
arXiv Detail & Related papers (2023-07-17T17:46:08Z) - A graph-state based synthesis framework for Clifford isometries [2.048226951354646]
We tackle the problem of synthesizing a Clifford isometry into an executable quantum circuit.
We propose a simple framework for synthesis that exploits the elementary properties of the Clifford group and one equation of the symplectic group.
We also propose practical synthesis algorithms for Clifford isometries with a focus on Clifford operators, graph states and codiagonalization of Pauli rotations.
arXiv Detail & Related papers (2022-12-13T22:50:24Z) - Guarantees for Epsilon-Greedy Reinforcement Learning with Function
Approximation [69.1524391595912]
Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to explore efficiently in some reinforcement learning tasks.
This paper presents a theoretical analysis of such policies and provides the first regret and sample-complexity bounds for reinforcement learning with myopic exploration.
arXiv Detail & Related papers (2022-06-19T14:44:40Z) - Deep Hierarchy in Bandits [51.22833900944146]
Mean rewards of actions are often correlated.
To maximize statistical efficiency, it is important to leverage these correlations when learning.
We formulate a bandit variant of this problem where the correlations of mean action rewards are represented by a hierarchical Bayesian model.
arXiv Detail & Related papers (2022-02-03T08:15:53Z) - Simulating quench dynamics on a digital quantum computer with
data-driven error mitigation [62.997667081978825]
We present one of the first implementations of several Clifford data regression based methods which are used to mitigate the effect of noise in real quantum data.
We find in general Clifford data regression based techniques are advantageous in comparison with zero-noise extrapolation.
This is the largest systems investigated so far in a study of this type.
arXiv Detail & Related papers (2021-03-23T16:56:14Z) - Visualizing Kraus operators for dephasing noise during application of
the $\sqrt{\mathrm{\mathrm{SWAP}}}$ quantum gate [0.0]
We derive optimized Kraus operators for a quantum gate in the presence of noise.
We show how to visualize the time evolution of each Kraus operator as a curve in a three-dimensional Euclidean space.
arXiv Detail & Related papers (2021-03-18T17:02:08Z) - A Trainable Optimal Transport Embedding for Feature Aggregation and its
Relationship to Attention [96.77554122595578]
We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference.
Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
arXiv Detail & Related papers (2020-06-22T08:35:58Z) - Hierarchical Reinforcement Learning as a Model of Human Task
Interleaving [60.95424607008241]
We develop a hierarchical model of supervisory control driven by reinforcement learning.
The model reproduces known empirical effects of task interleaving.
The results support hierarchical RL as a plausible model of task interleaving.
arXiv Detail & Related papers (2020-01-04T17:53:28Z)
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.