Distribution of Non-Locality On Quantum Random Circuits

• URL: http://arxiv.org/abs/2405.01650v3
• Date: Tue, 04 Nov 2025 11:34:11 GMT
• Title: Distribution of Non-Locality On Quantum Random Circuits
• Authors: Andrés Camilo Granda Arango, Federico Hernán Holik, Roberto Giuntini, Hector Freytes, Giuseppe Sergioli,
• Abstract summary: We explore how different types of resources are distributed among the states generated by quantum random circuits (QRC)<n>We focus on multipartite non-locality, but we also analyze quantum correlations by appealing to different entanglement and non-classicality measures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we explore how different types of resources are distributed among the states generated by quantum random circuits (QRC). We focus on multipartite non-locality, but we also analyze quantum correlations by appealing to different entanglement and non-classicality measures. We analyze the violation of Mermin and Svetlichny inequalities in order to get a glance at the distribution of nonlocality and genuine multipartite nonlocality. Next, we compare universal vs non-universal sets of gates, to gain insight into the problem of explaining quantum advantage. By comparing the results obtained with ideal (noiseless) vs noisy intermediate-scale quantum (NISQ) devices, we lay the basis of a certification protocol, which aims to quantify how robust is the resources distribution among the states that a given device can generate. We have implemented our non-locality-based benchmark on actual quantum processors with different architectures, in order to assess up to which point they are capable of reproducing the ideal results.

Related papers

• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• When Quantum Nonlocality Does Not Play Dice [3.722125357019147]
  Bell inequality violations are often taken as evidence that quantum nonlocality guarantees intrinsic randomness.<n>We show that there exist nontrivial Bell inequalities that are maximally violated by quantum correlations yet fail to certify randomness for any fixed input pair.<n>We construct maximally nonlocal quantum correlations that are deterministic for every fixed input pair.
  arXiv  Detail & Related papers  (2024-08-07T10:07:39Z)
• Guarantees on the structure of experimental quantum networks [105.13377158844727]
  Quantum networks connect and supply a large number of nodes with multi-party quantum resources for secure communication, networked quantum computing and distributed sensing. As these networks grow in size, certification tools will be required to answer questions regarding their properties. We demonstrate a general method to guarantee that certain correlations cannot be generated in a given quantum network.
  arXiv  Detail & Related papers  (2024-03-04T19:00:00Z)
• Extracting randomness from magic quantum states [3.9000096678531606]
  We show that when a subsystem of a quantum state is measured, the resultant unmeasured part of the system can exhibit a high degree of randomness.<n>Our findings suggest an approach to quantifying correlations within magic quantum states beyond the conventional paradigm of entanglement.
  arXiv  Detail & Related papers  (2024-02-15T18:33:21Z)
• Quantum Nonlocality: Multi-copy Resource Inter-convertibility & Their Asymptotic Inequivalence [0.0]
  We present instances of quantum nonlocal correlations that are incomparable in the strongest sense. Remarkably, these incomparable quantum correlations can be obtained even in the simplest Bell scenario. Our result challenges the notion of a 'unique gold coin', often referred to as the'maximally resourceful state'
  arXiv  Detail & Related papers  (2023-10-25T05:57:44Z)
• Enhancing Quantum Computation via Superposition of Quantum Gates [1.732837834702512]
  We present different protocols, which we denote as "superposed quantum error mitigation" We show that significant noise suppression can be achieved for most kinds of decoherence and standard experimental parameter regimes. We analyze our approach for gate-based, measurement-based and interferometric-based models.
  arXiv  Detail & Related papers  (2023-04-17T18:01:05Z)
• Majorization-based benchmark of the complexity of quantum processors [105.54048699217668]
  We numerically simulate and characterize the operation of various quantum processors. We identify and assess quantum complexity by comparing the performance of each device against benchmark lines. We find that the majorization-based benchmark holds as long as the circuits' output states have, on average, high purity.
  arXiv  Detail & Related papers  (2023-04-10T23:01:10Z)
• Randomness-free Test of Non-classicality: a Proof of Concept [0.0]
  Existing schemes to certify such non-classical resources in a device-independent manner require seed randomness. We propose and experimentally implement a semi-device independent certification technique for both quantum correlations and non-projective measurements without seed randomness.
  arXiv  Detail & Related papers  (2023-03-13T10:44:16Z)
• Complete characterization of quantum correlations by randomized measurements [0.832184180529969]
  We provide a method to measure any locally invariant property of quantum states using locally randomized measurements. We implement these methods experimentally using pairs of entangled photons, characterizing their usefulness for quantum teleportation. Our results can be applied to various quantum computing platforms, allowing simple analysis of correlations between arbitrary distant qubits.
  arXiv  Detail & Related papers  (2022-12-15T15:22:28Z)
• Characterizing arbitrary quantum networks in the noisy intermediate-scale quantum era [0.0]
  We provide a systematic approach to tackle with arbitrary quantum networks in the NISQ era. One application of our method is to witness the progress of essential elements in quantum networks.
  arXiv  Detail & Related papers  (2022-10-25T03:36:02Z)
• Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy [67.02951777522547]
  We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs) We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution. Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
  arXiv  Detail & Related papers  (2022-05-10T08:05:59Z)
• Learnability of the output distributions of local quantum circuits [53.17490581210575]
  We investigate, within two different oracle models, the learnability of quantum circuit Born machines. We first show a negative result, that the output distributions of super-logarithmic depth Clifford circuits are not sample-efficiently learnable. We show that in a more powerful oracle model, namely when directly given access to samples, the output distributions of local Clifford circuits are computationally efficiently PAC learnable.
  arXiv  Detail & Related papers  (2021-10-11T18:00:20Z)
• Experimental violations of Leggett-Garg's inequalities on a quantum computer [77.34726150561087]
  We experimentally observe the violations of Leggett-Garg-Bell's inequalities on single and multi-qubit systems. Our analysis highlights the limits of nowadays quantum platforms, showing that the above-mentioned correlation functions deviate from theoretical prediction as the number of qubits and the depth of the circuit grow.
  arXiv  Detail & Related papers  (2021-09-06T14:35:15Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)
• Estimation of correlations and non-separability in quantum channels via unitarity benchmarking [0.0]
  Correlation structures in quantum channels are less studied than those in quantum states. We develop a range of results for efficient estimation of correlations within a bipartite quantum channel. We show that correlated unitarity can be estimated in a SPAM-robust manner for any separable quantum channel.
  arXiv  Detail & Related papers  (2021-04-09T13:29:37Z)
• Preparing random states and benchmarking with many-body quantum chaos [48.044162981804526]
  We show how to predict and experimentally observe the emergence of random state ensembles naturally under time-independent Hamiltonian dynamics. The observed random ensembles emerge from projective measurements and are intimately linked to universal correlations built up between subsystems of a larger quantum system. Our work has implications for understanding randomness in quantum dynamics, and enables applications of this concept in a wider context.
  arXiv  Detail & Related papers  (2021-03-05T08:32:43Z)
• Semidefinite tests for quantum network topologies [0.9176056742068814]
  Quantum networks play a major role in long-distance communication, quantum cryptography, clock synchronization, and distributed quantum computing. The question of which correlations a given quantum network can give rise to, remains almost uncharted. We show that constraints on the observable covariances, previously derived for the classical case, also hold for quantum networks.
  arXiv  Detail & Related papers  (2020-02-13T22:36:46Z)
• Entanglement Classification via Neural Network Quantum States [58.720142291102135]
  In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
  arXiv  Detail & Related papers  (2019-12-31T07:40:23Z)

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.