Abstract Quantum circuit complexity is a pivotal concept in quantum information, quantum many-body physics, and high-energy physics. While extensively studied for closed systems, the characterization and dynamics of circuit complexity are little understood for the situation where the system is embedded within a larger system, which encompasses measurement-assisted state preparation. To address this gap, we introduce the notion of embedded complexity, which accounts for both system extensions and measurements. We study the complexity of projected states in a subsystem after measuring its complement and find that in random circuits, the embedded complexity is lower-bounded by the circuit volume---the total number of gates affecting both the subsystem and its complement. This finding indicates that the total cost of preparing the projected state cannot be reduced by leveraging ancillary qubits and measurements in general. Our result underscores the operational meaning of circuit volume, as it characterizes the embedded complexity of the generated state. Specifically, for random circuits or Clifford circuits, we demonstrate a spacetime conversion that concentrates circuit volume onto a subsystem via a random gate teleportation approach. In scenarios of deep thermalization where the system interacts extensively with a larger system, our analysis suggests that the resulting projected states exhibit high complexity. Additionally, we introduce a shadow tomography protocol that employs only ancillary random states and Bell state measurements, circumventing the need to evolve the input state and thereby simplifying experimental controls.
Abstract Quantum circuit complexity is a pivotal concept in quantum information, quantum many-body physics, and high-energy physics. While extensively studied for closed systems, the characterization and dynamics of circuit complexity are little understood for the situation where the system is embedded within a larger system, which encompasses measurement-assisted state preparation. To address this gap, we introduce the notion of embedded complexity, which accounts for both system extensions and measurements. We study the complexity of projected states in a subsystem after measuring its complement and find that in random circuits, the embedded complexity is lower-bounded by the circuit volume---the total number of gates affecting both the subsystem and its complement. This finding indicates that the total cost of preparing the projected state cannot be reduced by leveraging ancillary qubits and measurements in general. Our result underscores the operational meaning of circuit volume, as it characterizes the embedded complexity of the generated state. Specifically, for random circuits or Clifford circuits, we demonstrate a spacetime conversion that concentrates circuit volume onto a subsystem via a random gate teleportation approach. In scenarios of deep thermalization where the system interacts extensively with a larger system, our analysis suggests that the resulting projected states exhibit high complexity. Additionally, we introduce a shadow tomography protocol that employs only ancillary random states and Bell state measurements, circumventing the need to evolve the input state and thereby simplifying experimental controls.
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.
Quantum complexity has emerged as a natural probe of chaos, thermalization, and the black hole interior on timescales long after local observables have equilibrated. However, its time evolution has been studied almost exclusively in subsystems confined to a single connected region. We show, using complementary tools from holography and random quantum circuits, that noncontiguous subsystems composed of multiple disjoint regions give rise to qualitatively new physics compared to the contiguous case. First, at finite temperature, a subsystem occupying less than half of the total system can carry high complexity at late times, even as the complexity of its larger complement remains low. This inversion of the usual hierarchy is intrinsically thermal: it vanishes in the infinite-temperature limit, which is the regime modeled by random quantum circuits. Second, if a subsystem's complexity equilibrates at an early time, then fragmenting it into m disjoint components can further reduce this timescale by a factor of m, a phenomenon we exhibit in both holography and random quantum circuits. These results not only sharpen the correspondence between geometric and computational notions of complexity, but also motivate the search for novel complexity phenomena in quantum dynamics.
The quantum circuit complexity of an evolving many-body quantum system is believed to exhibit a sustained growth, maintained for timescales much longer than the onset of thermalization. Most previous works have focused on models which violate energy conservation, such as random unitary circuits. Here we study generic, local time-independent Hamiltonian dynamics. We unconditionally prove that for generic local Hamiltonians and typical initial product states at high effective temperature, the robust quantum circuit complexity must grow over a very long period of time, attaining an exponentially large value at late times. Our approach relies on two structural properties that we prove rigorously: (i) generic local Hamiltonians satisfy generalized spectral no-resonance conditions of arbitrary order, and (ii) typical high-temperature product states are effectively supported in exponentially many energy eigenstates. These properties have been widely assumed without proof in prior works. As corollaries of this result, we show the late-time state displays robust volume-law entanglement that is irremovable by polynomial-size circuits, and we establish a no fast-forwarding result for generic local Hamiltonians. We also lower bound the complexity of preparing sufficiently large subsystems with local quantum channels, in contrast with sufficiently small thermalizing regions which we show retain low complexity at late times.
Preparing long-range entangled states is critical for quantum information processing and many-body physics, but typically requires impractically deep circuits. Recent progress on adaptive circuits, which incorporate mid-circuit measurements and classical feedback, shows that adaptivity can greatly reduce depth and even prepare arbitrary states in constant depth. This advantage, however, comes with a trade-off between depth and ancillary qubits, motivating the study of adaptive state complexity: the minimal depth and number of ancillas needed to prepare a given state. In this work, we investigate adaptive state complexity for generic mixed states and general circuit architectures. Our approach analyzes the growth of correlation range in adaptive circuits and shows that insufficient depth or ancilla resources strictly limit the formation of long-range correlations. This yields rigorous bounds on approximate state and gate complexity given the correlation value. We demonstrate the generality and applicability of our approach with representative examples, including permutation-invariant states, Gibbs states of many-body Hamiltonians, and multi-qubit gates like the Toffoli gate.
Quantum state tomography is a fundamental technique for characterizing quantum systems, but its scalability remains a significant challenge. This paper investigates an efficient alternative using classical shadows to reconstruct a Bell state with high fidelity. Classical shadows provide a compressed representation of a quantum state using randomized measurements, reducing the measurement complexity compared to full quantum tomography when full state reconstruction is not required. We employed a quantum circuit to generate a Bell state and utilize 1000 snapshots to construct its classical shadow. The reconstructed density matrix is evaluated using fidelity and norm difference metrics against the ideal Bell state. Our analysis demonstrates that as the number of snapshots increases, the fidelity of reconstruction stabilizes around 0.98-1.0, with the norm difference decreasing accordingly. The results also confirm the convergence of reconstructed states towards the ideal Bell state, highlighting the efficiency and accuracy of classical shadows in quantum state estimation. Shallow shadow tomography uses shallow random circuits to estimate global quantum state properties on noisy hardware. By combining low-depth entangling gates with randomized measurements and noise-aware postprocessing, it reduces sample complexity compared to Pauli shadows while remaining experimentally feasible. Recent experiments on superconducting processors confirmed up to fivefold measurement savings.
The intersection of physics and information theory has advanced rapidly in the past few decades. Information-theoretic concepts have been employed to formulate physical laws, while quantum information processing, such as quantum computation and communication, has been developed both theoretically and experimentally. Simultaneously, understanding the universal or specific behaviors of quantum many-body systems has remained a central challenge since shortly after the inception of quantum mechanics. This is studied in part by statistical mechanics, which models complex many-body systems using simple ensembles. In this thesis, we investigate information-theoretic problems in quantum many-body dynamics that lie at the intersection of these avenues. To begin with, we investigate the extent of randomness, captured by unitary designs, in random quantum circuits. Quantum circuits serve as discrete models for random many-body quantum dynamics. Specifically, we study how the internal structure of random gates influences the generation of randomness in quantum circuits. We demonstrate that the spectral gap of structured random circuits can be larger than that of unstructured ones, implying that the former can converge to designs faster than the latter. In this analysis, free-fermionic techniques play a crucial role in obtaining exact results on the spectrum of second-moment operators. We then investigate the effect of quantum measurements on the quantum complexity in random circuits. Here, the quantum complexity of a state is defined as the minimum number of local gates required for its exact preparation in quantum circuits. We prove that in monitored random quantum circuits, the complexity undergoes a measurement-induced phase transition by leveraging techniques from percolation theory. Above the critical measurement rate, the complexity saturates quickly. Below the threshold, it grows at least linearly for an exponentially long time. Finally, we discuss quantum communication scenarios in noisy quantum dynamics, where we simulate the identity channel by combining a given noisy quantum dynamics and our quantum operational strategies. Specifically, we consider multi-time noisy processes and optimize our strategies to simulate near-noiseless channels, both of which are mathematically formulated using quantum combs. We introduce formal concepts of simulation and capacity for quantum combs, and we derive lower-bounds on the minimum simulation error in terms of semidefinite programming (SDP). We impose certain conditions on our strategies, such as the positive partial transpose (PPT) preserving constraint. We then analyze these SDPs for simple models, such as the depolarizing process, to determine their fundamental communication limits.