Probing many-body Bell correlation depth with superconducting qubits
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Abstract (arXiv)
Quantum nonlocality describes a stronger form of quantum correlation than that of entanglement. It refutes Einstein's belief of local realism and is among the most distinctive and enigmatic features of quantum mechanics. It is a crucial resource for achieving quantum advantages in a variety of practical applications, ranging from cryptography and certified random number generation via self-testing to machine learning. Nevertheless, the detection of nonlocality, especially in quantum many-body systems, is notoriously challenging. Here, we report an experimental certification of genuine multipartite Bell correlations, which signal nonlocality in quantum many-body systems, up to 24 qubits with a fully programmable superconducting quantum processor. In particular, we employ energy as a Bell correlation witness and variationally decrease the energy of a many-body system across a hierarchy of thresholds, below which an increasing Bell correlation depth can be certified from experimental data. As an illustrating example, we variationally prepare the low-energy state of a two-dimensional honeycomb model with 73 qubits and certify its Bell correlations by measuring an energy that surpasses the corresponding classical bound with up to 48 standard deviations. In addition, we variationally prepare a sequence of low-energy states and certify their genuine multipartite Bell correlations up to 24 qubits via energies measured efficiently by parity oscillation and multiple quantum coherence techniques. Our results establish a viable approach for preparing and certifying multipartite Bell correlations, which provide not only a finer benchmark beyond entanglement for quantum devices, but also a valuable guide towards exploiting multipartite Bell correlation in a wide spectrum of practical applications.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 79 references that are in the map. First posted on arXiv 25 Jun 2024.
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References · 10
- 2020Variational Quantum Algorithmscited by 687
- 2018Barren plateaus in quantum neural network training landscapescited by 624
- 2019Quantum Natural Gradientcited by 104
- 2022Exploiting symmetry in variational quantum machine learningcited by 68
- 2022Group-Invariant Quantum Machine Learningcited by 56
- 2021Enhancing Generative Models via Quantum Correlationscited by 40
- 2022Experimental quantum adversarial learning with programmable superconducting qubitscited by 38
- 2021Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ans"atzecited by 29
- 2023Expressibility-induced Concentration of Quantum Neural Tangent Kernelscited by 7
- 2024Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noisescited by 3
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