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.
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.
Adaptive quantum circuits, leveraging measurements and classical feedback, significantly expand the landscape of realizable quantum states compared to their non-adaptive counterparts, enabling the preparation of long-range entangled states and topological phases at constant depths. However, the ancilla overhead for preparing arbitrary states can be prohibitive, raising a fundamental question: which states can be efficiently realized with limited ancilla and low depth? Addressing this question requires a rigorous quantitative characterization of state complexity, or the minimum depth and ancillas, to realize a state in adaptive circuits. In this work, we tackle this problem by introducing two properties of quantum states: state weight and anti-shallowness, connected to the correlation range and correlation strength within a state, respectively. We prove that these quantities are bounded under limited circuit resources, thereby providing rigorous bounds on the approximate complexity of state preparation and gate implementation. Illustrative examples include the GHZ state, W state, QLDPC code states, and the Toffoli gate. Besides complexity, we show that states within the same quantum phase, defined by a set of quantum states connected with constant-depth circuits, must share the same scaling of weight or anti-shallowness. This establishes these quantities as indicators of quantum phases and their essential roles in many-body physics.
Adaptive quantum circuits employ unitary gates assisted by mid-circuit measurement, classical computation on the measurement outcome, and the conditional application of future unitary gates based on the result of the classical computation. In this paper, we experimentally demonstrate that even a noisy adaptive quantum circuit of constant depth can achieve a task that is impossible for any purely unitary quantum circuit of identical depth: the preparation of long-range entangled topological states with high fidelity. We prepare a particular toric code ground state with fidelity of at least 76.9±1.3% using a constant depth (d=4) adaptive circuit, and rigorously show that no unitary circuit of the same depth and connectivity could prepare this state with fidelity greater than 50%.
A central question in quantum information theory is the circuit complexity of states arising from standard many-body models. We study this question for quantum p-spin glasses, random Hamiltonians whose interactions act on p-tuples of qubits through Pauli strings. Anschuetz, Gamarnik, and Kiani (arXiv:2404.07231) showed that the optimum energy is separated from the best energy achievable by product states. This leaves open whether shallow circuits can close the gap, since even depth-one circuits can generate entanglement.
We show that the entanglement needed to close the product-state gap cannot be generated at shallow depth. When the average interaction degree grows with n, we prove that, for all sufficiently large fixed p, any circuit preparing an n-qubit state whose normalized energy is within a fixed positive constant of the optimum must have depth Ωp(logn). In the bounded-average-degree regime, we prove a fixed-depth obstruction: for every fixed D, a sufficiently large degree prefactor rules out depth-D preparation of near-ground states. Both results hold uniformly over circuits with an arbitrary number of ancilla qubits.
Our results give an obstruction in the spirit of the No Low-Energy Trivial States problem of Freedman and Hastings (arXiv:1301.1363), but for random quantum spin glasses rather than code-based Hamiltonians such as those of Anshu, Breuckmann, and Nirkhe (arXiv:2206.13228), whose ground states admit polynomial-size preparation circuits. This setting opens a probabilistic route to NLTS-like questions: we recast state-preparation lower bounds for random quantum Hamiltonians as uniform control of Gaussian processes indexed by shallow circuits.
Adaptive quantum circuits, which combine local unitary gates, midcircuit measurements, and feedforward operations, have recently emerged as a promising avenue for efficient state preparation, particularly on near-term quantum devices limited to shallow-depth circuits. Matrix product states (MPS) comprise a significant class of many-body entangled states, efficiently describing the ground states of one-dimensional gapped local Hamiltonians and finding applications in a number of recent quantum algorithms. Recently, it has been shown that the Affleck-Kennedy-Lieb-Tasaki state—a paradigmatic example of an MPS—can be exactly prepared with an adaptive quantum circuit of constant depth, an impossible feat with local unitary gates alone due to its nonzero correlation length [Smith , PRX Quantum 4, 020315 (2023)]. In this work, we broaden the scope of this approach and demonstrate that a diverse class of MPS can be exactly prepared using constant-depth adaptive quantum circuits, outperforming theoretically optimal preparation with unitary circuits. We show that this class includes short- and long-ranged entangled MPS, symmetry-protected topological (SPT) and symmetry-broken states, MPS with finite Abelian, non-Abelian, and continuous symmetries, resource states for MBQC, and families of states with tunable correlation length. Moreover, we illustrate the utility of our framework for designing constant-depth sampling protocols, such as for random MPS or for generating MPS in a particular SPT phase. We present sufficient conditions for particular MPS to be preparable in constant time, with global on-site symmetry playing a pivotal role. Altogether, this work demonstrates the immense promise of adaptive quantum circuits for efficiently preparing many-body entangled states and provides explicit algorithms that outperform known protocols to prepare an essential class of states. Published by the American Physical Society 2024
Dynamic quantum circuits (DQCs) incorporate mid-circuit measurements and gates conditioned on these measurement outcomes. DQCs can prepare certain long-range entangled states in constant depth, making them a promising route to preparing complex quantum states on devices with a limited coherence time. Almost all constructions of DQCs for state preparation have been formulated analytically, relying on special structure in the target states. In this work, we approach the problem of state preparation variationally, developing scalable tensor network algorithms which find high-fidelity DQC preparations for generic states. We apply our algorithms to critical states, random matrix product states, and subset states. We consider both DQCs with a fixed number of ancillae and those with an extensive number of ancillae. Even in the few ancillae regime, the DQCs discovered by our algorithms consistently prepare states with lower infidelity than a static quantum circuit of the same depth. Notably, we observe constant fidelity gains across system sizes and circuit depths. For DQCs with an extensive number of ancillae, we introduce scalable methods for decoding measurement outcomes, including a neural network decoder and a real-time decoding protocol. Our work demonstrates the power of an algorithmic approach to generating DQC circuits, broadening their scope of applications to new areas of quantum computing.
Mid-circuit measurements with feedforward reduce the optimal worst-case quantum depth for exact n-qubit unitary synthesis from Θ(n) to O(1), using arbitrary one- and two-qubit gates and unrestricted ancillary space. Measurements and resets without feedforward remain subject to the circuit causal-cone bound. Our deterministic construction uses single-qubit measurements, O(1) rounds of parity feedforward, and O(n4nlog(n+2)) total qubits, with all ancillas starting and ending in ∣0⟩. Classical processing is not counted in quantum depth. We teleport only the matrix error of an approximation by Nehoran and Yuen. An exactly prepared ancillary state encodes this error, whose action is added back by coherent interference. At operator-norm error 2−n, one step of amplitude amplification suffices. Coherent fanout circuits achieve the same constant-depth bound, while circuits using only one- and two-qubit gates attain depth Θ(n) with O(4nlog2(n+2)) gates. Generic targets require Ω(4n/(h+1)) qubits at depth h, even under unrestricted classical control. Our depth-width trade-offs meet this bound within a factor O(n2) for 1≤h≤2n.