Wavefunction branches demand a definition!

TL;DR

This paper examines the challenge of precisely defining wavefunction branches in quantum systems, comparing recent complexity-based approaches and discussing their strengths, weaknesses, and open questions.

Contribution

It analyzes and compares two recent proposals for formalizing wavefunction branches using quantum complexity, highlighting their differences and identifying open research questions.

Findings

Both approaches use quantum complexity to characterize branches.

Branches tend to persist due to linear growth of state complexity.

Open questions remain about the precise definition and practical identification of branches.

Abstract

Under unitary evolution, a typical macroscopic quantum system is thought to develop wavefunction branches: a time-dependent decomposition into orthogonal components that (1) form a tree structure forward in time, (2) are approximate eigenstates of quasiclassical macroscopic observables, and (3) exhibit effective collapse of feasibly measurable observables. If they could be defined precisely, wavefunction branches would extend the theory of decoherence beyond the system-environment paradigm and could supplant anthropocentric measurement in the quantum axioms. Furthermore, when such branches have bounded entanglement and can be effectively identified numerically, sampling them would allow asymptotically efficient classical simulation of quantum systems. I consider a promising recent approach to formalizing branches on the lattice by Taylor & McCulloch [Quantum 9, 1670 (2025), arXiv:2308.04494], and compare it to prior work from Weingarten [Found. Phys. 52, 45 (2022), arXiv:2105.04545]. Both proposals are based on quantum complexity and argue that, once created, branches persist for long times due to the generic linear growth of state complexity. Taylor & McCulloch characterize branches by a large difference in the unitary complexity necessary to interfere vs. distinguish them. Weingarten takes branches as the components of the decomposition that minimizes a weighted sum of expected squared complexity and the Shannon entropy of squared norms. I discuss strengths and weaknesses of these approaches, and identify tractable open questions.