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arXiv:2205.05668

Short Proofs of Linear Growth of Quantum Circuit Complexity

Zhi Li

Published May 11, 2022• 2 citations
arXivHTMLPDF

Abstract

The complexity of a quantum gate, defined as the minimal number of elementary gates to build it, is an important concept in quantum information and computation. It is shown recently that the complexity of quantum gates built from random quantum circuits almost surely grows linearly with the number of building blocks. In this article, we provide two short proofs of this fact. We also discuss a discrete version of quantum circuit complexity growth.

DOI
10.48550/arxiv.2205.05668

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The complexity of a quantum gate, defined as the minimal number of elementary gates to build it, is an important concept in quantum information and computation. It is shown recently that the complexity of quantum gates built from random quantum circuits almost surely grows linearly with the number of building blocks. In this article, we provide two short proofs of this fact. We also discuss a discrete version of quantum circuit complexity growth.
Abstract The complexity of quantum states has become a key quantity of interest across various subfields of physics, from quantum computing to the theory of black holes. The evolution of generic quantum systems can be modelled by considering a collection of qubits subjected to sequences of random unitary gates. Here we investigate how the complexity of these random quantum circuits increases by considering how to construct a unitary operation from Haar-random two-qubit quantum gates. Implementing the unitary operation exactly requires a minimal number of gates—this is the operation’s exact circuit complexity. We prove a conjecture that this complexity grows linearly, before saturating when the number of applied gates reaches a threshold that grows exponentially with the number of qubits. Our proof overcomes difficulties in establishing lower bounds for the exact circuit complexity by combining differential topology and elementary algebraic geometry with an inductive construction of Clifford circuits.
We prove new lower bounds on the growth of robust quantum circuit complexity -- the minimal number of gates Cδ(U)C_δ(U)Cδ​(U) to approximate a unitary UUU up to an error of δδδ in operator norm distance. More precisely we show two bounds for random quantum circuits with local gates drawn from a subgroup of SU(4)SU(4)SU(4). First, for δ=Θ(2−n)δ=Θ(2^{-n})δ=Θ(2−n), we prove a linear growth rate: Cδ≥d/poly(n)C_δ\geq d/\mathrm{poly}(n)Cδ​≥d/poly(n) for random quantum circuits on nnn qubits with d≤2n/2d\leq 2^{n/2}d≤2n/2 gates. Second, for $ δ=Ω(1)$, we prove a square-root growth of complexity: Cδ≥d/poly(n)C_δ\geq \sqrt{d}/\mathrm{poly}(n)Cδ​≥d​/poly(n) for all d≤2n/2d\leq 2^{n/2}d≤2n/2. Finally, we provide a simple conjecture regarding the Fourier support of randomly drawn Boolean functions that would imply linear growth for constant δδδ. While these results follow from bounds on the moments of random quantum circuits, we do not make use of existing results on the generation of unitary ttt-designs. Instead, we bound the moments of an auxiliary random walk on the diagonal unitaries acting on phase states. In particular, our proof is comparably short and self-contained.
A random unitary quantum circuit is expected to be incompressible for exponentially long times. We show that the constant-error circuit complexity of a random unitary circuit grows almost linearly with time as Ω(T/log⁡T)Ω(T/\log T)Ω(T/logT). The bound holds for all 2≤T≤4n2\leq T\leq 4^n2≤T≤4n where nnn is the system size, and involves no other nnn-dependence. This improves previous lower bounds derived from spectral gaps and unitary designs by a factor of poly(n)\mathrm{poly}(n)poly(n). Drawing on insights from stochastic calculus, geometric functional analysis, and randomized linear algebra, our approach exploits the circuit's response to variations of individual gates and requires no control over convergence to high-order unitary designs.
Notions of circuit complexity and cost play a key role in quantum computing and simulation where they capture the (weighted) minimal number of gates that is required to implement a unitary. Similar notions also become increasingly prominent in high energy physics in the study of holography. While notions of entanglement have in general little implications for the quantum circuit complexity and the cost of a unitary, in this work, we discuss a simple such relationship when both the entanglement of a state and the cost of a unitary take small values, building on ideas on how values of entangling power of quantum gates add up. This bound implies that if entanglement entropies grow linearly in time, so does the cost. The implications are twofold: It provides insights into complexity growth for short times. In the context of quantum simulation, it allows us to compare digital and analog quantum simulators. The main technical contribution is a continuous-variable small incremental entangling bound.
The concept of quantum complexity has far-reaching implications spanning theoretical computer science, quantum many-body physics, and high-energy physics.The quantum complexity of a unitary transformation or quantum state is defined as the size of the shortest quantum computation that executes the unitary or prepares the state.It is reasonable to expect that the complexity of a quantum state governed by a chaotic many-body Hamiltonian grows linearly with time for a time that is exponential in the system size; however, because it is hard to rule out a shortcut that improves the efficiency of a computation, it is notoriously difficult to derive lower bounds on quantum complexity for particular unitaries or states without making additional assumptions.To go further, one may study more generic models of complexity growth.We provide a rigorous connection between complexity growth and unitary k-designs, ensembles that capture the randomness of the unitary group.This connection allows us to leverage existing results about design growth to draw conclusions about the growth of complexity.We prove that local random quantum circuits generate unitary transformations whose complexity grows linearly for a long time, mirroring the behavior one expects in chaotic quantum systems and verifying conjectures by Brown and Susskind.Moreover, our results apply under a strong definition of quantum complexity based on optimal distinguishing measurements.
Quantum circuit complexity is a fundamental concept whose importance permeates quantum information, computation, many-body physics and high-energy physics. While extensively studied in closed systems, its characterization and behaviors in the widely important setting where the system is embedded within a larger one -- encompassing measurement-assisted state preparation -- lack systematic understanding. We introduce the notion of embedded complexity that characterizes the complexity of projected states and measurement operators in this general setting incorporating auxiliary systems and measurements. For random circuits and certain strongly interacting time-independent Hamiltonian dynamics, we show that the embedded complexity is lower-bounded by the circuit volume -- the total number of gates acting on both the subsystem and its complement. This strengthens the complexity linear growth theorems, enriches the understanding of deep thermalization, and indicates that measurement-assisted methods generically cannot yield significant advantages in state preparation cost, contrary to expectations. We further demonstrate a spacetime conversion of certain circuit models that concentrates circuit volume onto a subsystem, and showcase applications for random circuit sampling and shadow tomography. Our theory establishes a unified framework for space and time aspects of quantum circuit complexity, yielding profound new insights and applications across quantum information and physics.