mlsanityData to 7 Oct 2026

Architectures and random properties of symplectic quantum circuits

arXiv 2405.10264 ↗First posted 16 May 2024quant-ph cs.LGcited by 1

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Abstract (arXiv)

Parametrized and random unitary (or orthogonal) n-qubit circuits play a central role in quantum information. As such, one could naturally assume that circuits implementing symplectic transformations would attract similar attention. However, this is not the case, as mathbbSP (d/2) -- the group of d× d unitary symplectic matrices -- has thus far been overlooked. In this work, we aim at starting to fill this gap. We begin by presenting a universal set of generators G for the symplectic algebra mathfraksp(d/2), consisting of one- and two-qubit Pauli operators acting on neighboring sites in a one-dimensional lattice. Here, we uncover two critical differences between such set, and equivalent ones for unitary and orthogonal circuits. Namely, we find that the operators in G cannot generate arbitrary local symplectic unitaries and that they are not translationally invariant. We then review the Schur-Weyl duality between the symplectic group and the Brauer algebra, and use tools from Weingarten calculus to prove that Pauli measurements at the output of Haar random symplectic circuits can converge to Gaussian processes. As a by-product, such analysis provides us with concentration bounds for Pauli measurements in circuits that form t-designs over mathbbSP(d/2). To finish, we present tensor-network tools to analyze shallow random symplectic circuits, and we use these to numerically show that computational-basis measurements anti-concentrate at logarithmic depth.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 105 references that are in the map. First posted on arXiv 16 May 2024.

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17 of 105 references.

References · 17

  1. 2018Barren plateaus in quantum neural network training landscapescited by 624
  2. 2021Quantum advantage in learning from experimentscited by 186
  3. 2024Barren Plateaus in Variational Quantum Computingcited by 130
  4. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
  5. 2021Theory of overparametrization in quantum neural networkscited by 100
  6. 2023Does provable absence of barren plateaus imply classical simulability?cited by 78
  7. 2022Exploiting symmetry in variational quantum machine learningcited by 68
  8. 2022Theory for Equivariant Quantum Neural Networkscited by 60
  9. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  10. 2021Exponential separations between learning with and without quantum memorycited by 50
  11. 2022Equivariant quantum circuits for learning on weighted graphscited by 41
  12. 2022Representation Theory for Geometric Quantum Machine Learningcited by 40
5 more
  1. 2021Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ans"atzecited by 29
  2. 2023Quantum Deep Hedgingcited by 22
  3. 2023Quantum neural networks form Gaussian processescited by 11
  4. 2023On the universality of Sn-equivariant k-body gatescited by 7
  5. 2024Trained quantum neural networks are Gaussian processescited by 5

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