Conference

In distributed quantum information processing, the parties are often limited to performing local operations and classical communication (LOCC). The class of LOCC is notoriously difficult to analyze, and typically one considers the larger class of so-called separable (SEP) operations, which has a nicer mathematical structure. Finding separable but non-LOCC maps is a challenging endeavor, with most known examples involving the task of state discrimination. In this talk, I will describe an approach for constructing SEP but non-LOCC channels based on the resource theory of quantum coherence. This further highlights the value of studying general resource theories within quantum information theory. This is joint work with Ludovico Lami.

In this talk, I will discuss quantum diagonalization methods, based on subspaces obtained from quantum computers, which overcome the scaling limitations of variational algorithms and enabled realistic chemistry computations of up to 77 qubits on a quantum centric supercomputing architecture, using a Heron quantum processor and the RIKEN supercomputer Fugaku. Merging these ideas with Krylov quantum subspaces, I will present a ground state algorithm with convergence similar to phase estimation and robustness to noise, which led to an experimental demonstration for lattice ground state problems obtained with Heron processors and the supercomputer Frontier.

In order to achieve fault-tolerant quantum computing, we make use of quantum error correction schemes designed to protect the logical information of the system from decoherence. A promising way to preserve such information is using the multimode Gottesman-Kitaev-Preskill (GKP) encoding, which encodes a single logical qubit into harmonic oscillators. This type of encoding adds redundancy in the physical system by leveraging the infinitely large Hilbert space of multiple oscillators. Such redundancy can be used to increase the distance between the logical code words, protecting the logical qubit against larger errors. Usual protocols to correct multimode GKP states are based on Steane-type quantum error correction circuits. Steane-type circuits consist of auxiliary state preparation, two-mode squeezing operations, measurements and decoding. In this work, we focus on the decoding part of these protocols. More precisely, we propose a decoder that considers the noise present on the auxiliary states. Specifically, we do so by tracking the correlations between errors on different modes spreading throughout the circuit which represents the Steane-type protocol. We show that leveraging the correlations between the measurement result and the actual error affecting the multimode GKP state enables less decoding errors. Overall, for each different multimode GKP code studied, we can increase the lifetime by at least an order of magnitude, yielding more robust quantum computation.

Quantum error correction (QEC) is believed to be essential for the realization of large-scale quantum computers. However, due to the complexity of operating on the encoded `logical' qubits, understanding the physical principles for building fault-tolerant quantum devices and combining them into efficient architectures is an outstanding scientific challenge. Here we utilize reconfigurable arrays of up to 448 neutral atoms to implement all key elements of a universal, fault-tolerant quantum processing architecture and experimentally explore their underlying working mechanisms. We first employ surface codes to study how repeated QEC suppresses errors, demonstrating 2.14(13)x below-threshold performance in a four-round characterization circuit by leveraging atom loss detection and machine learning decoding. We then investigate logical entanglement using transversal gates and lattice surgery, and extend it to universal logic through transversal teleportation with 3D [[15,1,3]] codes, enabling arbitrary-angle synthesis with logarithmic overhead. Finally, we develop mid-circuit qubit re-use, increasing experimental cycle rates by two orders of magnitude and enabling deep-circuit protocols with dozens of logical qubits and hundreds of logical teleportations with [[7,1,3]] and high-rate [[16,6,4]] codes while maintaining constant internal entropy. Our experiments reveal key principles for efficient architecture design, involving the interplay between quantum logic and entropy removal, judiciously using physical entanglement in logic gates and magic state generation, and leveraging teleportations for universality and physical qubit reset. These results establish foundations for scalable, universal error-corrected processing and its practical implementation with neutral atom systems.

We present a family of simple three-dimensional stabilizer codes, called the chiral color codes, that realize fermionic and chiral topological orders. In the qubit case, the code realizes the topological phase of a single copy of the fermionic toric code. For qudit systems with local dimension d, the model features a chiral parameter α and realizes 3D topological phases characterized by $\mathbb{Z}^\alpha_d$ anyon theories with anomalous chiral surface topological order. On closed manifolds, the code has a unique ground state after removing bulk transparent fermions or bosons. Furthermore, we prove that the bulk is short-range entangled (for odd d, coprime α) by constructing an explicit local quantum channel that prepares the ground state. The chiral color codes are constructed within the gauge color code, and hence inherit its fault-tolerant features: they admit single-shot error-correction and allow code switching to other stabilizer color codes. These properties position the chiral color codes as particularly useful platforms for realizing and manipulating fermions and chiral anyons.

Particle physics underpins our understanding of the world at a fundamental level by describing the interplay of matter and forces through gauge theories. Yet, despite their unmatched success, the intrinsic quantum mechanical nature of gauge theories makes important problem classes notoriously difficult to address with classical computational techniques. A promising way to overcome these roadblocks is offered by quantum computers, which are based on the same laws that make the classical computations so difficult. We present a quantum computation of the properties of the basic building block of two-dimensional lattice quantum electrodynamics, involving both gauge fields and matter. This computation is made possible by the use of a trapped-ion qudit quantum processor, where quantum information is encoded in  d  different states per ion, rather than in two states as in qubits. Qudits are ideally suited for describing gauge fields, which are naturally high-dimensional, leading to a dramatic reduction in the quantum register size and circuit complexity. Our results open the door for hardware-efficient quantum simulations with qudits in near-term quantum devices.

Ordered phases of matter have close connections to computation. Two prominent examples are spin glass order, with wide-ranging applications in machine learning and optimization, and topological order, closely related to quantum error correction. Here, we introduce the concept of topological quantum spin glass (TQSG) order which marries these two notions, exhibiting both the complex energy landscapes of spin glasses, and the quantum memory and long-range entanglement characteristic of topologically ordered systems. Using techniques from coding theory and a quantum generalization of Gibbs state decompositions, we show that TQSG order is the low-temperature phase of various quantum LDPC codes on expander graphs, including hypergraph and balanced product codes. Our work introduces a topological analog of spin glasses that preserves quantum information, opening new avenues for both statistical mechanics and quantum computer science.

Quantum computing and sensing represent two distinct frontiers of quantum information science. Here, we harness quantum computing to solve a fundamental and practically important sensing problem: the detection of weak oscillating fields with unknown strength and frequency. We present a quantum computing enhanced sensing protocol, that we dub quantum search sensing, outperforming all existing approaches. Furthermore, we prove our approach is optimal by establishing the Grover-Heisenberg limit -- a fundamental lower bound on the minimum sensing time. The key idea is to robustly digitize the continuous, analog signal into a discrete operation, which is then integrated into a quantumalgorithm. Our metrological gain originates from quantum computation, distinguishing our protocol from conventional sensing approaches. Indeed, we prove that broad classes of protocols based on quantum Fisher information, finite-lifetime quantum memory, or classical signal processing are strictly less powerful. We propose and analyze a proof-of-principle experiment using nitrogen-vacancy centers, where meaningful improvements are achievable using current technology. This work establishes quantum computation as a powerful new resource for advancing sensing capabilities.