Chao-Yang Lu

Gaussian boson sampling (GBS) is one of the leading approaches for demonstrating quantum computational advantage, but its application to practical real-world problems remains a central challenge. Here, we propose a GBS-based image recognition scheme inspired by extreme learning machine to enhance the performance of perceptron and implement it using our latest GBS device, ji\ifmmode \check{u}\else \v{u}\fi{}zh\ifmmode \bar{a}\else \={a}\fi{}ng 4.0. By avoiding in situ programmability of the photonic circuit, the scheme substantially reduces experimental overhead while retaining a large, fixed random feature map. Our approach utilizes an 8176-mode temporal-spatial hybrid encoding photonic processor, achieving approximately 2200 average photon clicks in the quantum computational advantage regime. We apply this scheme to classify images from the MNIST and Fashion-MNIST datasets, achieving a testing accuracy of 95.86% on MNIST and 85.95% on Fashion-MNIST, respectively. These results surpass both classical linear-kernel support vector machines and the three previous physical extreme-learning-machine experiments. In addition, we systematically explore the influence of three key hyperparameters and the efficiency of GBS in our experiments. Our results not only demonstrate the potential of GBS in real-world machine learning applications but will also inspire further advancements in powerful machine learning schemes utilizing GBS technology.

Neutral-atom arrays have rapidly advanced to support thousands of qubits and execute high-fidelity logical operations. However, these processors remain severely throttled by their slowest fundamental operation: nondestructive qubit measurement, which requires milliseconds and fundamentally limits the system's clock rate. This bottleneck arises from both an inherent photon-budget dilemma--sufficient fluorescence for reliable state discrimination must be collected without excessive heating or loss--and frame-based imaging, which imposes one common exposure and decision latency on intrinsically independent, site-local measurements. Here, we overcome these limitations with a fast, nondestructive readout architecture based on real-time, site-resolved adaptive protection. By integrating continuous photon counting with a dynamic feedforward framework, we decode qubit states with sub-microsecond latency and instantly shield atoms from redundant scattering. Demonstrated in parallel across a 100-qubit reconfigurable atom array, with adaptive protection on a 25-site subarray, this dynamic decision protocol reduces the average probe time to just 15 μs. Model-free benchmarking yields a discrimination infidelity of 4.1×10 ^-5 and an atom loss of 2.1×10 ^-4 , simultaneously setting new performance records for atom arrays. Exploiting this capability, we operate repeated quantum circuits at an unprecedented 1.7 kHz clock rate with atoms reused over 120 consecutive rounds—nearly sevenfold higher than the previous record—and enter the sub-millisecond cycle regime for the first time. By removing nondestructive readout as the dominant cycle-time bottleneck, this work unlocks high-clock-rate mid-circuit syndrome extraction, paving the way for high-throughput, fault-tolerant quantum computation.

Nonequilibrium dynamics of quantum many-body systems is challenging for classical computing, providing opportunities for demonstrating practical quantum computational advantage with analog quantum simulators. Owing to the intimate connection with a random matrix ensemble, it is proposed to be classically intractable to sample the driven thermalized many-body states of a Bose-Hubbard system and further extract multipoint correlations from the output strings for characterizing quantum systems. Here, leveraging dedicated precise manipulations and atom-number-resolved detection through a quantum gas microscope with bichromatic superlattices, we perform sampling of the driven Hubbard chains and two-leg ladders in the thermalized phase involving up to 64 sites with 20 atoms, yielding a Hilbert space dimension of 10 19 and outpacing the most powerful supercomputer in terms of sampling rate by 3 orders of magnitude. The volume law scaling of the Rényi entanglement entropy in the thermalized phase is observed, which hinders efficient classical simulation for large systems. We employ the Bayesian tests to verify that our prepared systems operate in the driven thermalized phase. Multipoint correlations of up to 14th-order extracted from the experimental samples offer clear distinctions between the thermalized and many-body-localized phases, where classical computations such as tensor network fail to give accurate and faithful predictions within a reasonable time cost. Our work demonstrates the sampling of an interacting chaotic system performed on a quantum processor of ultracold atoms and opens the door of utilizable quantum computational advantage in simulating Floquet dynamics of many-body systems.

The development of large-scale, high-fidelity quantum processors is a fundamental scientific challenge, essential for exploring the boundaries of classical computation and advancing towards fault-tolerant systems. Gaussian boson sampling not only serves as a prominent model for demonstrating quantum computational advantage^1,2,3 but can also generate bosonic error-correcting codes for fault-tolerant quantum computing^4,5,6. However, its scalability has been hindered by significant photon loss in increasingly large and complex encoding circuits. Here we show a programmable photonic quantum processor, Jiuzhang 4.0, which incorporates 1,024 high-efficiency squeezed states into a hybrid spatial–temporal encoded 8,176-mode circuit. By achieving 92% source efficiency and 51% overall system efficiency, the processor produces samples with detection events up to 3,050 photons, representing an order-of-magnitude increase in scale over previous demonstrations^7,8,9,10. This architecture realizes a cubic scaling of connectivity (16³ = 4, 096), enabling sampling within a Hilbert space of dimension approximately 10^2,461. The experimental results are rigorously validated against all current classical simulation methods, especially the matrix product state algorithms recently designed to exploit photon loss¹¹. The ability to control thousands of photons in programmable low-loss quantum processors pushes the experimental frontier into a regime far beyond classical tractability and opens a pathway to trillion-qumode three-dimensional cluster states and fault-tolerant photonic quantum hardware.

The realization of fault-tolerant quantum computation hinges on the ability to execute deep quantum circuits while maintaining gate fidelities consistently above error-correction thresholds. Although neutral-atom arrays have recently demonstrated high-fidelity two-qubit gates and early-stage logical quantum processors, sustaining such high performance across deep, repetitive circuits remains a formidable challenge due to cumulative motional heating and atom loss. Here we demonstrate a sustainable neutral-atom framework that overcomes these limitations by integrating a suite of hardware-efficient mid-circuit operations. We report a two-qubit controlled logic gate with a raw fidelity of 99.60(1)%, which is further increased to a fidelity of 99.81(1)% via non-destructive erasure detection. Crucially, by implementing in-circuit Raman sideband cooling and qubit re-initialization, we demonstrate that gate fidelities can be maintained at the ~99.8% level across multiple operational rounds without observable degradation. By actively managing the internal and motional entropy of the system mid-stream, our in-situ refreshable architecture provides a critical pathway for executing the repeated syndrome-extraction cycles required for large-scale, continuous quantum error correction.

Programmable neutral atom arrays show great promise for fault-tolerant quantum computing. A dominant physical error on this platform is qubit leakage and loss, notably decay errors from the Rydberg state during two-qubit gates. Such leakage events are particularly detrimental as they propagate, generating correlated errors that severely degrade the effective error distance of quantum error correction codes. Here, we present a novel approach to address Rydberg decay errors leveraging measurement-based quantum computation. Our scheme strategically exploits the inherent geometric structure of topological cluster states and only uses final leakage detection information to locate propagated errors originating from Rydberg decay. This eliminates the need for complex and atom-species-specific midcircuit leakage detection, offering broader applicability, e.g., to the well-established Rb atom platform. We demonstrate a high error threshold of 3.65% per cz gate for pure Rydberg decay and achieve a favorable error distance d_{e}≈d. Our method compares favorably with state-of-the-art erasure conversion protocols in the subthreshold performance, offering comparable or marginally larger logical error rates while significantly reducing experimental overhead.