Quantum Compression for Distributed Entanglement
- URL: http://arxiv.org/abs/2605.04271v1
- Date: Tue, 05 May 2026 20:06:25 GMT
- Title: Quantum Compression for Distributed Entanglement
- Abstract summary: We show that a joint design of the resource state and a family of compression schemes can increase the entanglement across partitions under a fixed transmission budget.<n>In the bipartite case, we propose practical constructions that closely approach the derived upper bound.
- Score: 42.24731280472663
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study compression strategies for multipartite entanglement distribution under uncertainty in the partitioning of the quantum state. When the partition is not known at the time of state preparation, we show that a joint design of the resource state and a family of compression schemes can increase the entanglement across partitions under a fixed transmission budget. We formulate this as a source coding problem and derive non-asymptotic upper and lower bounds on the achievable average entanglement subject to an average coding rate. We furthermore design an efficient method for jointly optimizing states and lossless compression maps by exploiting the inherent symmetry of weighted Dicke states. In the bipartite case, we propose practical constructions that closely approach the derived upper bound, and more generally we provide practical constructions for multipartite settings.
Related papers
- Fitting Unknown Number of Hyperplanes with Manifold Optimization [57.48093263119306]
Fitting unknown number of linearplanes to data is a fundamental yet challenging problem in machine learning.<n>Existing approaches often struggle with optimum optima or lack geometric consistency.
arXiv Detail & Related papers (2026-05-27T14:02:20Z) - Gaussian superpositions for bosonic encodings [0.0]
Non-Gaussian bosonic states are ubiquitous in interacting light--matter systems, many-body platforms, and quantum field settings.<n>We introduce an exact finite-manifold encoding for states supported on a finite span of Gaussian branches.<n>As demonstrations, we obtain closed-form and numerically stable evaluations of entropies and relative-entropy non-Gaussianity.
arXiv Detail & Related papers (2026-03-16T13:25:17Z) - Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance [55.01966743652196]
We propose a novel algorithm for distributed gradient descent (SGD) with compressed gradient communication in the parameter-server framework.
Our gradient compression technique, named flattened one-bit gradient descent (FO-SGD), relies on two simple algorithmic ideas.
arXiv Detail & Related papers (2024-05-17T21:17:27Z) - Quantum circuit design for mixture and preparation of arbitrary pure and mixed quantum states [25.01488143369413]
This paper addresses the challenge of preparing arbitrary mixed quantum states.
Two circuit design methods are presented: one via a mixture of pure states and the other via purification.
arXiv Detail & Related papers (2024-03-28T06:37:00Z) - Exact and local compression of quantum bipartite states [0.0]
We study exact local compression of a quantum bipartite state, that is, applying local quantum operations to reduce the dimensions of the Hilbert spaces.<n>As an application, we consider the exact compression of quantum channels.
arXiv Detail & Related papers (2023-09-14T05:32:36Z) - Multipartite entanglement theory with entanglement-nonincreasing operations [55.2480439325792]
We extend the resource theory of entanglement for multipartite systems beyond the standard framework of local operations and classical communication.<n>We demonstrate that in this adjusted framework, the transformation rates between multipartite states are fundamentally dictated by the bipartite entanglement entropies of the respective quantum states.
arXiv Detail & Related papers (2023-05-30T12:53:56Z) - Fully and partially distributed Quantum Generalized Benders Decomposition for Unit Commitment Problems [12.20904743817675]
Hybrid quantum-classical generalized Benders decomposition (GBD) algorithms are proposed to address unit commitment (UC) problems.<n>In the centralized approach, the quantum GBD transforms the master problem (MP) into a quadratic unconstrained binary optimization form suitable for quantum computing.<n>For distributed systems, the distributed consensus quantum GBD employs an average consensus strategy to reformulate subproblems into local subproblems.
arXiv Detail & Related papers (2022-10-13T02:26:27Z) - A Linearly Convergent Algorithm for Decentralized Optimization: Sending
Less Bits for Free! [72.31332210635524]
Decentralized optimization methods enable on-device training of machine learning models without a central coordinator.
We propose a new randomized first-order method which tackles the communication bottleneck by applying randomized compression operators.
We prove that our method can solve the problems without any increase in the number of communications compared to the baseline.
arXiv Detail & Related papers (2020-11-03T13:35:53Z) - Linear Convergent Decentralized Optimization with Compression [50.44269451541387]
Existing decentralized algorithms with compression mainly focus on compressing DGD-type algorithms.
Motivated by primal-dual algorithms, this paper proposes first underlineLinunderlineEAr convergent.
underlineDecentralized with compression, LEAD.
arXiv Detail & Related papers (2020-07-01T04:35:00Z) - Bidirectional compression in heterogeneous settings for distributed or
federated learning with partial participation: tight convergence guarantees [9.31522898261934]
Artemis is a framework to tackle the problem of learning in a distributed setting with communication constraints and device partial participation.
It improves on existing algorithms that only consider unidirectional compression (to the server), or use very strong assumptions on the compression operator, and often do not take into account devices partial participation.
arXiv Detail & Related papers (2020-06-25T17:37:45Z)
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.