Memory effects in repeated uses of quantum channels

• URL: http://arxiv.org/abs/2511.05661v1
• Date: Fri, 07 Nov 2025 19:00:14 GMT
• Title: Memory effects in repeated uses of quantum channels
• Authors: Hayden Zammit, Roberto Salazar, Gianluca Valentino, Johann A. Briffa, Tony J. G. Apollaro,
• Abstract summary: Quantum Information Processing tasks can be efficiently formulated in terms of quantum dynamical maps.<n>A key QIP task is quantum state transfer (QST) aimed at sharing quantum information between distant nodes of a quantum network.<n>We show that even relatively small readout timing errors give rise to memory effects which have a highly detrimental impact on subsequent QST tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Information Processing (QIP) tasks can be efficiently formulated in terms of quantum dynamical maps, whose formalism is able to provide the appropriate mathematical representation of the evolution of open quantum systems. A key QIP task is quantum state transfer (QST) aimed at sharing quantum information between distant nodes of a quantum network, enabling, e.g. quantum key distribution and distributed quantum computing. QST has primarily been addressed insofar by resetting the quantum channel after each use, thus giving rise to memoryless channels. Here we consider the case where the quantum channel is continuously used, without implementing time- and resource- consuming resetting operations. We derive a general, analytical expression for the $n^{\mathrm{th}}$-use average QST fidelity for $U(1)$-symmetric channels and apply our formalism to a perfect QST channel in the presence of imperfect readout timing. We show that even relatively small readout timing errors give rise to memory effects which have a highly detrimental impact on subsequent QST tasks.

Related papers

• Distributed Quantum Computation via Entanglement Forging and Teleportation [13.135604356093193]
  Distributed quantum computation is a practical method for large-scale quantum computation on quantum processors with limited size. In this paper, we demonstrate the methods to implement a nonlocal quantum circuit on two quantum processors without any quantum correlations.
  arXiv  Detail & Related papers  (2024-09-04T08:10:40Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Assisted quantum simulation of open quantum systems [0.0]
  We introduce the quantum-assisted quantum algorithm, which reduces the circuit depth of UQA via NISQ technology. We present two quantum-assisted quantum algorithms for simulating open quantum systems.
  arXiv  Detail & Related papers  (2023-02-26T11:41:02Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• 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)
• Quantum Semantic Communications for Resource-Efficient Quantum Networking [52.3355619190963]
  This letter proposes a novel quantum semantic communications (QSC) framework exploiting advancements in quantum machine learning and quantum semantic representations. The proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.
  arXiv  Detail & Related papers  (2022-05-05T03:49:19Z)
• Quantum Noise Sensing by generating Fake Noise [5.8010446129208155]
  We propose a framework to characterize noise in a realistic quantum device. Key idea is to learn about the noise by mimicking it in a way that one cannot distinguish between the real (to be sensed) and the fake (generated) one. We find that, when applied to the benchmarking case of Pauli channels, the SuperQGAN protocol is able to learn the associated error rates even in the case of spatially and temporally correlated noise.
  arXiv  Detail & Related papers  (2021-07-19T09:42:37Z)
• 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)
• Benchmarking Quantum State Transfer on Quantum Devices using Spatio-Temporal Steering [0.0]
  Quantum state (QST) provides a method to send arbitrary quantum states from one system to another. Standard benchmark of QST is the average fidelity between the prepared and received states. We provide a benchmark which reveals the non-classicality of QST based ontemporal steering (STS)
  arXiv  Detail & Related papers  (2020-09-16T02:19:26Z)
• Attention-based Quantum Tomography [9.818293236208413]
  "Attention-based Quantum Tomography" is a quantum state reconstruction using an attention mechanism-based generative network. We show AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer.
  arXiv  Detail & Related papers  (2020-06-22T17:50:12Z)

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.