Communication-Efficient Distributed Inverse Quantum Fourier Transform
- URL: http://arxiv.org/abs/2605.10710v1
- Date: Mon, 11 May 2026 15:22:03 GMT
- Title: Communication-Efficient Distributed Inverse Quantum Fourier Transform
- Abstract summary: We propose a distributed formulation of the iQFT over a quantum network composed of $P$ nodes, each hosting $Q$ qubits.<n>By reducing the number of inter-node quantum interactions, the proposed approach significantly lowers the quantum communication requirements of the distributed iQFT.
- Score: 1.376615688087685
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The scalability of quantum computing is currently limited by physical, technological, and architectural constraints that hinder the integration of a large number of qubits within a single quantum processor. Distributed quantum computing (DQC) has therefore emerged as a viable alternative, aiming to interconnect multiple smaller quantum processing units (QPUs) to jointly operate on a global quantum state. While this paradigm enables scalable architectures, it introduces significant communication overhead due to the cost of non-local quantum operations across distant nodes. In this work we propose a distributed formulation of the iQFT over a quantum network composed of $P$ nodes, each hosting $Q$ qubits, enabling the execution on a logical register of size $n = P \cdot Q$. Furthermore, we introduce a communication-efficient variant based on a threshold-driven pruning strategy, referred to as a \emph{communication horizon}, which exploits the exponentially decreasing significance of controlled-phase rotations to safely omit remote gates with negligible impact. By reducing the number of inter-node quantum interactions, the proposed approach significantly lowers the quantum communication requirements of the distributed iQFT while preserving its functional correctness. Crucially, we show that this approach fundamentally alters the scaling of the algorithm: the entanglement resource consumption per node saturates to a constant value, reducing the global communication complexity from quadratic $\mathcal{O}(P^2)$ to linear $\mathcal{O}(P)$. As the iQFT constitutes a critical building block in many quantum algorithms, the techniques presented in this paper directly contribute to improving the practicality and scalability of distributed quantum computation.
Related papers
- Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing [10.919776355400282]
Distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network.<n>To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs.<n>We propose a time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead.<n>Our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies.
arXiv Detail & Related papers (2026-03-04T14:43:10Z) - Time-Aware Qubit Assignment and Circuit Optimization for Distributed Quantum Computing [6.812818096174995]
In distributed quantum computing, large circuits are divided into smaller subcircuits such that they can be executed individually and simultaneously on multiple QPUs.<n>We address the problem of assigning qubits to QPUs to minimize communication costs in two different ways.<n>First by applying time-aware algorithms that take into account the changing connectivity of a given circuit as well as the underlying network topology.<n>In another approach, we propose an evolutionary-based quantum circuit optimization algorithm that adjusts the circuit itself rather than the schedule to reduce the overall communication cost.
arXiv Detail & Related papers (2025-07-15T20:20:29Z) - Efficient Gate Reordering for Distributed Quantum Compiling in Data Centers [0.0]
It is crucial to develop methods and software that minimize the number of inter-QPU communications.<n>Here we describe key features of the quantum compiler araQne, which is designed to minimize distribution cost.<n>We establish the crucial role played by circuit reordering strategies, which strongly reduce the distribution cost compared to a baseline approach.
arXiv Detail & Related papers (2025-07-01T18:00:02Z) - Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures [1.8781124875646162]
This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based approach for efficient quantum circuit compilation and mapping.
In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn theses for a specific multi-core architecture.
arXiv Detail & Related papers (2024-06-17T12:09:11Z) - Revisiting the Mapping of Quantum Circuits: Entering the Multi-Core Era [2.465579331213113]
We introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications.
Our evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a $4.9times$ and $1.6times$ improvement in terms of execution time and non-local communications.
arXiv Detail & Related papers (2024-03-25T21:31:39Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - 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) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - Quantum communication complexity beyond Bell nonlocality [87.70068711362255]
Efficient distributed computing offers a scalable strategy for solving resource-demanding tasks.
Quantum resources are well-suited to this task, offering clear strategies that can outperform classical counterparts.
We prove that a new class of communication complexity tasks can be associated to Bell-like inequalities.
arXiv Detail & Related papers (2021-06-11T18:00:09Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z)
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.