AWS Quantum Technologies Blog Decoding Realistic Quantum Error Syndrome with Quantum Elements Digital Twins Fault-tolerant quantum computing requires quantum error correction (QEC): Encoding one logical qubit into many physical qubits so that, below a threshold error rate, the logical error rate falls rapidly as the code grows. The practical engineering question is: How large must the code be and how good must the hardware be to reach a useful logical qubit? Credible answers require models that capture a device’s real error mechanisms, including coherent and correlated effects, yet run fast enough to support iterative design. These requirements motivate the design of hardware‑calibrated digital twins for QEC. As a step toward this goal, we report on results from a collaboration involving researchers from Quantum Elements Inc., the University of Southern California (USC), Harvard University and Amazon Web Services (AWS), to speed up hardware-faithful QEC simulations with classical compute resources. Building on a real-time quantum Monte Carlo (QMC) algorithm developed at USC [1], we used Amazon Elastic Compute Cloud (Amazon EC2) Hpc7a instances orchestrated by AWS ParallelCluster to run quantum master‑equation simulations of a distance‑7 rotated surface code with 97 physical qubits (49 data qubits + 48 measurement qubits) on par with the state-of-the-art surface-code memory demonstrations [2]. A full open-system simulation of a 97-qubit distance-7 surface code round without approximations would require tracking a density matrix with 4⁹⁷ entries, far beyond the capabilities of classical computers. Our approach runs this simulation in about an hour on a single compute node, while faithfully capturing coherent and correlated noise that simpler models miss. In this post, we present a foundational demonstration of scalable, hardware-faithful QEC simulations at experiment-relevant scale. In future posts, we will incorporate richer error models into the digital twin, use the resulting syndrome data to develop and evaluate more expressive