APS Global Physics Summit 2026

Noise and decoherence are major obstacles to quantum computing performance. In this talk we highlight recent progress in error-suppression methodology from our group, with particular attention to the combined effect of a unified and automated error-suppression pipeline. We describe characterization and circuit-level correction of coherent gate errors, as well as circuit-aware and error-aware embedding of dynamical decoupling to fully eliminate crosstalk errors. Each of these methods is independently applied; however, we demonstrate that together they unlock significantly higher performance compared to direct hardware execution.

The thermalization dynamics of isolated quantum many-body systems lie at the core of quantum information science, revealing rich behavior between integrability and ergodicity. The tilted Fermi–Hubbard model—combining strong interactions, kinetic constraints, and tilt-induced localization—offers a clean, disorder-free platform to study non-ergodic phenomena. In this work, we use digital quantum processors to probe how a tilted potential shapes transport in one- and two-dimensional Fermi–Hubbard systems, reaching scales beyond 120 qubits. The first part of the talk will highlight the experimental findings and their implications for near-term quantum-advantage demonstrations.

In the second part of the talk, we discuss problem-tailored compilation strategies, which allow us to scale system size of the experiment to 120 qubits for the full-spin Fermi-Hubbard model, and reach circuit depths of 20 trotter steps.

Matrix models are an important class of theories in high-energy and mathematical physics. They provide simplified but powerful frameworks for studying nonperturbative aspects of string theory, gauge/gravity duality, and the dynamics of strongly interacting quantum systems. In this talk, we present the first experimental results of simulating matrix models on quantum computers. Through device-specific noisy simulations combined with error mitigation strategies, we establish concrete resource requirements and identify performance boundaries for current quantum hardware. This work provides essential benchmarking data for near-term quantum devices and lays the foundation for simulating more complex matrix models relevant to string theory and gauge/gravity duality, with future extensions targeting the BFSS and BMN models as quantum hardware capabilities advance.

Robust and reliable software tools for autonomous calibration are essential for advancing quantum computing hardware. Automated cold-start tune-up routines requiring minimal human intervention enable rapid device screening, shortening the verification cycle and accelerating hardware development speed. Simultaneously, maintaining peak device performance requires precise tuning of high-fidelity operations and real-time re-calibration to ensure stability. In this talk, we will present our approach to autonomous tune-up of superconducting qubit hardware, from cold-start bring-up to the system-wide optimization of readout and gate operations. We will share our progress toward a scalable calibration framework, highlight the challenges and opportunities we’ve encountered, and outline our vision for scaling toward the quantum utility regime.