Quantum computers open new scientific avenues, from exploring complex quantum mechanical systems to new computational paradigms, but face the fundamental challenge of decoherence. Remarkably, decoherence can be prevented by creating highly entangled states of physical qubits that encode an error-corrected “logical” qubit. This thesis will describe the development of quantum computing with reconfigurable arrays of neutral atoms and their use for quantum processing with logical qubits. Quantum processing in this approach is based on the coherent transport of atoms shuttled by optical tweezers, enabling any-to-any connectivity, high-fidelity programmable logic, and mid-circuit processing within a zoned architecture. Logical qubit processing is greatly facilitated by parallel control and transversal operations, and is used for experiments ranging from entangling logical qubits to their use for precise simulation of quantum scrambling. Core physical mechanisms for achieving deep-circuit, universal algorithms with logical qubits are identified, and these are leveraged into new techniques that greatly reduce overheads for large-scale computation. Finally, applications of quantum processors will be explored via analog and gate-based quantum simulations. These results, alongside other recent advances, herald a transition to error-corrected quantum processing, establishing foundations that can enable future large-scale quantum computers and their useful applications.
Quantum computers open new scientific avenues, from exploring complex quantum mechanical systems to new computational paradigms, but face the fundamental challenge of decoherence. Remarkably, decoherence can be prevented by creating highly entangled states of physical qubits that encode an error-corrected “logical” qubit. This thesis will describe the development of quantum computing with reconfigurable arrays of neutral atoms and their use for quantum processing with logical qubits. Quantum processing in this approach is based on the coherent transport of atoms shuttled by optical tweezers, enabling any-to-any connectivity, high-fidelity programmable logic, and mid-circuit processing within a zoned architecture. Logical qubit processing is greatly facilitated by parallel control and transversal operations, and is used for experiments ranging from entangling logical qubits to their use for precise simulation of quantum scrambling. Core physical mechanisms for achieving deep-circuit, universal algorithms with logical qubits are identified, and these are leveraged into new techniques that greatly reduce overheads for large-scale computation. Finally, applications of quantum processors will be explored via analog and gate-based quantum simulations. These results, alongside other recent advances, herald a transition to error-corrected quantum processing, establishing foundations that can enable future large-scale quantum computers and their useful applications.
Abstract Suppressing errors is the central challenge for useful quantum computing 1 , requiring quantum error correction (QEC) 2–6 for large-scale processing. However, the overhead in the realization of error-corrected ‘logical’ qubits, in which information is encoded across many physical qubits for redundancy 2–4 , poses substantial challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Using logical-level control and a zoned architecture in reconfigurable neutral-atom arrays 7 , our system combines high two-qubit gate fidelities 8 , arbitrary connectivity 7,9 , as well as fully programmable single-qubit rotations and mid-circuit readout 10–15 . Operating this logical processor with various types of encoding, we demonstrate improvement of a two-qubit logic gate by scaling surface-code 6 distance from d = 3 to d = 7, preparation of colour-code qubits with break-even fidelities 5 , fault-tolerant creation of logical Greenberger–Horne–Zeilinger (GHZ) states and feedforward entanglement teleportation, as well as operation of 40 colour-code qubits. Finally, using 3D [[8,3,2]] code blocks 16,17 , we realize computationally complex sampling circuits 18 with up to 48 logical qubits entangled with hypercube connectivity 19 with 228 logical two-qubit gates and 48 logical CCZ gates 20 . We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical-qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling 21,22 . These results herald the advent of early error-corrected quantum computation and chart a path towards large-scale logical processors.
Quantum processors have the potential to significantly advance our understanding of quantum systems. In particular, the programmability of digital quantum devices enables access to highly tunable quantum dynamics and observables. The central challenge, however, is suppressing errors, making quantum error correction essential for large-scale algorithms. In this thesis, I present work on quantum processing with reconfigurable atom arrays, with experiments featuring up to 448 neutral atom qubits, high two-qubit gate fidelities, arbitrary connectivity, and mid-circuit qubit readout and reuse. Using this system, I describe how we realize gate-based quantum simulations. In particular, we experimentally study Kitaev’s honeycomb model and explore hardware-efficient fermion-to-qubit encodings. We then leverage unique opportunities with atoms to study the key building blocks of scalable quantum error correction. These include below-threshold performance, deep computation at constant entropy, and universal logical gates. Together, these experiments highlight unique near-term opportunities with atom arrays and chart a path toward future large-scale atomic processors.
A broad effort is currently underway to develop quantum computers that can outperform classical counterparts for certain computational or simulation tasks. Suppressing errors is one of the central challenges for useful quantum computing, requiring quantum error correction for large-scale processing. However, the overhead in the realization of error-corrected “logical” qubits, where information is encoded across many physical qubits for redundancy, poses significant challenges to large-scale logical quantum computing. In this talk, we will discuss the recent advances involving programmable, coherent manipulation of quantum systems based on neutral atom arrays excited into Rydberg states, allowing the control over several hundred qubits in two dimensions. Optical control techniques are central to this approach. In particular, we use this platform to explore quantum algorithms with encoded logical qubits and quantum error correction techniques. Using this logical processor with various types of error-correcting codes, we demonstrate that we can improve logical two-qubit gates by increasing code size, outperform physical qubit fidelities, create logical GHZ states, and perform computationally complex scrambling circuits using 48 logical qubits and hundreds of logical gates. These results herald the advent of early error-corrected quantum computation, enabling new applications and inspiring a shift in addressing both the challenges and opportunities that lay ahead.
Neutral atom quantum computing utilizes laser-trapped neutral atoms as qubits and realizes quantum logic gate operations through Rydberg-state interactions. In recent years, it has become one of the most vibrant directions in quantum computing hardware. This paper systematically reviews the working principles of neutral-atom quantum computers, including qubit encoding, atom trapping and manipulation, Rydberg states and interactions, the Rydberg blockade quantum gate mechanism, and atom rearrangement with reconfigurable architectures. The mainstream technical routes are surveyed, represented by optical tweezer arrays combined with Rydberg interactions, optical lattice schemes, and dipole trap arrays. A panoramic review is provided of domestic and international research progress from theoretical foundations in 2000 to the latest achievements in 2026, including thousand-qubit-scale systems, logical qubits, and quantum error correction experiments. Key breakthroughs are highlighted, such as the 6100-atom qubit array, continuous operation of a 3000-qubit system, quantum simulation of the Kitaev honeycomb model, toric code error correction demonstrations, encoding rates exceeding 1/2, and fault-tolerant architectures. The core bottlenecks are analyzed in depth, including the scalability--fidelity trade-off, engineering implementation of quantum error correction, atom loss and mid-circuit replenishment, laser system industrialization, control electronics scalability, and long-distance quantum interconnection. This paper aims to provide a systematic reference for academic research and technological development in this field.
Quantum error correction (QEC) [1,2] is essential for the realization of large-scale quantum computers [3,4]. However, due to the complexity of operating on the encoded ‘logical’ qubits [5,6], 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 the 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 [6,7], demonstrating 2.14(13)x below-threshold performance in a four-round characterization circuit by leveraging atom loss detection and machine learning decoding [8,9]. We then investigate logical entanglement using transversal gates and lattice surgery [10–12], and extend it to universal logic through transversal teleportation with 3D [[15,1,3]] codes [13,14], enabling arbitrary-angle synthesis with polylogarithmic overhead [5,15]. Finally, we develop mid-circuit qubit re-use [16], increasing experimental cycle rates by two orders of magnitude and enabling deep-circuit protocols with dozens of logical qubits and hundreds of logical teleportations [17–20] 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 & entropy removal, judiciously using physical entanglement in logic gates & magic state generation, and leveraging teleportations for universality & physical qubit reset. These results establish foundations for scalable, universal error-corrected processing and its practical implementation with neutral atom systems.
Quantum computers have the potential to solve certain problems exponentially faster than classical computers. However, realizing this potential in practice is a major challenge, as it requires precise control over large-scale quantum systems operating at extremely low error rates. Quantum error correction (QEC) provides a path to achieving such error rates, but its substantial resource overhead poses a significant practical barrier. In addition, identifying which computational problems offer a provable quantum advantage—and which remain fundamentally intractable—remains an open question. This thesis presents progress towards addressing both of these challenges. The first part of this thesis describes advances that substantially reduce the resource overhead of QEC. We begin by presenting realizations of logical circuits in dynamically reconfigurable arrays of neutral atoms. By jointly decoding the logical qubits, we reduce the cost of implementing such transversal Clifford circuits by a factor proportional to the code distance. We then develop new theories of fault tolerance to extend these savings to universal quantum computation with magic state inputs. Finally, we introduce techniques for fast correlated decoding, enabling practical implementations of these improvements in experimental hardware. Collectively, these advances accelerate progress toward large-scale computation and reduce the cost of QEC by over an order of magnitude. The second part of the thesis explores the relative power of quantum and classical computation in combinatorial optimization, a class of problems that is ubiquitous in science and engineering and foundational to the theory of computational complexity. We experimentally implement the optimized quantum adiabatic algorithm (QAA) in neutral atom arrays and observe evidence of a superlinear speedup over classical simulated annealing on certain hard problem instances. To interpret these results, we develop a theoretical framework to compare the performance of QAA with a broad class of classical Markov chain Monte Carlo algorithms. We identify conditions under which a quantum quadratic speedup is achievable and propose modifications to the QAA to reliably realize this advantage. Together, these contributions advance both the practical implementation and theoretical understanding of quantum algorithms.