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Linear growth of quantum circuit complexity

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arxiv 2106.05305 v3 pith:LRCWLOWY submitted 2021-06-09 quant-ph hep-thmath-phmath.MP

classification quant-phhep-thmath-phmath.MP
keywords complexityquantumcircuitgatesrandomunitarycircuitsexact
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantifying quantum states' complexity is a key problem in various subfields of science, from quantum computing to black-hole physics. We prove a prominent conjecture by Brown and Susskind about how random quantum circuits' complexity increases. Consider constructing a unitary from Haar-random two-qubit quantum gates. Implementing the unitary exactly requires a circuit of some minimal number of gates - the unitary's exact circuit complexity. We prove that this complexity grows linearly with the number of random gates, with unit probability, until saturating after exponentially many random gates. Our proof is surprisingly short, given the established difficulty of lower-bounding the exact circuit complexity. Our strategy combines differential topology and elementary algebraic geometry with an inductive construction of Clifford circuits.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum Simulation of Random Unitaries from Clebsch-Gordan Transforms

    quant-ph 2025-09 accept novelty 7.0 of 10

    Clebsch-Gordan transforms give exact compressed oracles for Haar-random unitary group actions, with efficient circuits for U(d).

  2. Nonlocal Nonstabilizerness from Holographic Schwinger Pair Production

    hep-th 2026-05 unverdicted novelty 6.0 of 10

    In holographic Schwinger pair production, the excess capacity of entanglement is √λ(d−2)/(d−1)³ — positive for d>2, zero for d=2 — so the produced pair carries nonlocal magic for d>2.

  3. Thermalization with partial information

    quant-ph 2025-08 unverdicted novelty 6.0 of 10

    A maximum channel entropy principle, backed by a microcanonical-style derivation, identifies the canonical noisy channel that models thermalization under partial information.

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