Abstract We propose the optical simulation of the Lieb–Liniger interaction using one-dimensional (1D) stationary light polaritons (SLPs) with nonlinear self-interaction. Our analysis reveals that the dark-state polariton (DSP) mode of the self-interacting SLP satisfies a Schrödinger-like equation exhibiting the Lieb–Liniger interaction, where both the effective mass and interaction strength can be tuned optically. We demonstrate the transition of the Lieb–Liniger interaction from a repulsion-dominant regime to a thermalization-dominant regime using experimentally achievable parameters. This results in the second-order correlation of the DSP mode spanning from anti-bunching to bunching statistics. By advancing the quantum simulation of the Lieb–Liniger model, our work opens new avenues for exploring complex Lieb–Liniger physics, such as interaction quenching and multi-particle bound states. Moreover, it lays the groundwork for developing novel single-photon sources operating in the anti-bunching regime, featuring narrow bandwidth, directional emission, and built-in quantum memory functionality.

Abstract A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at a large scale in optics. Here we report on experiments in which we used a frequency-multiplexed GBS with > 400 modes as a quantum reservoir. We evaluated the accuracy of our GBS-based reservoir computer on a variety of benchmark tasks. We found that when the system was given access to the correlations between measured modes of the GBS, the achieved accuracies were the same or higher than when it was only given access to the mean photon number in each mode—and in several cases the advantage in accuracy from using the correlations was greater than 20 percentage points. This provides experimental evidence in support of theoretical predictions that access to correlations enhances the power of quantum reservoir computers. We also tested our reservoir computer when operating the reservoir with various sources of classical rather than quantum light and found that using squeezed light consistently resulted in the highest accuracies. Our work experimentally establishes that a GBS can be an effective quantum reservoir and provides a practical platform for experimentally exploring the role of quantumness and correlations in quantum machine learning at very large system sizes.

Abstract Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. This work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation and the derived spectral function is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

Abstract Post-selection strategies that discard low-confidence results can significantly improve the effective fidelity of quantum computing at the cost of reduced acceptance rates, particularly useful for offline resource state generation and moderate-depth fault-tolerant circuits. Prior work has primarily relied on the “logical gap” metric, which faces fundamental limitations including computational overhead that scales exponentially with the number of logical qubits and poor generalizability beyond surface codes. We develop post-selection strategies based on computationally efficient heuristic metrics that leverage error cluster statistics from clustering-based decoders, which are applicable to arbitrary quantum low-density parity check (QLDPC) codes. We validate our method through extensive numerical simulations on surface codes, bivariate bicycle codes, and hypergraph product codes, demonstrating orders of magnitude reductions in logical error rates with moderate abort rates. For instance, applying our strategy to the [[144, 12, 12]] bivariate bicycle code achieves ~ 1000 × reduction in the logical error rate with an abort rate of only 1% at a physical error rate of 0.1%. Additionally, we integrate our approach with the sliding-window framework for real-time decoding, featuring mid-circuit abort decisions that eliminate unnecessary overheads. Notably, its performance matches or even surpasses the original strategy, while exhibiting favorable scaling in the number of rounds.

Abstract Quantum error-correcting codes with asymptotically lower overheads than the surface code require nonlocal connectivity. Leveraging multilayer routing and long-range coupling capabilities in superconducting qubit hardware, we develop Hardware-Aware Layout, HAL: a robust, runtime-efficient heuristic algorithm that automates and optimizes the placement and routing of arbitrary codes. Using HAL, we generate around 150 explicit layouts of quantum low-density parity-check (qLDPC) codes. We study codes with topological structure and find that removing the periodic boundaries significantly lowers the hardware complexity with only a moderate reduction of logical efficiency. We also lay out highly nonlocal qLDPC code families that achieve competitive tradeoffs between hardware complexity and logical efficiency. Based on our findings, we anticipate many novel qLDPC codes to be realizable on near-term superconducting qubit hardware and inform future directions for the co-design of quantum devices and fault-tolerant architectures.

Abstract We present a scalable framework for accurately modeling many-body interactions in surface-code quantum processing units. Combining a concise diagrammatic formalism with high-precision numerical methods, our approach efficiently evaluates high-order, long-range Pauli string couplings and maps complete chip layouts onto exact effective Hamiltonians. Applying this method to surface-code architectures, such as Google’s Sycamore lattice, we identify three distinct interaction regimes: computationally stable phase, error-dominated phase, and hierarchy-inverted phase. Our analysis reveals that even modest increases in residual qubit-qubit crosstalk can invert the interaction hierarchy, driving the system from a computationally favorable phase into a topologically ordered regime. This framework thus serves as a powerful guide for optimizing next-generation high-fidelity surface-code hardware and provides a pathway to investigate emergent quantum many-body phenomena.