Hardware-Faithful Digital Twins for Quantum Computing with Izhar Medalsy

Izhar Medalsy is not a career qubit theorist. His path runs from a physical chemistry PhD and an ETH Zurich postdoc in atomic force microscopy and ternary nanoscale logic, through productizing scientific instruments at Bruker, through building one of the fastest resin 3D printers on the market, into co-founding Quantum Elements in 2023 with Daniel Lidar (USC) and Amir Yacoby (Harvard). That arc — nanoscale measurement scientist turned deep-tech operator — shapes how he thinks about the simulation gap in quantum computing.

The conversation lands at a specific moment. In April 2026, Quantum Elements published a joint result with AWS, USC, and Harvard simulating a distance-7 rotated surface code with 97 physical qubits using full quantum master equations on AWS HPC7a, and announced a deeper collaboration with Rigetti Computing on next-generation superconducting processors. If you care about how error correction strategies, decoders, and pulse-level controls actually get developed before they ever touch hardware, this episode is for you.

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What We Get Into

• Why generic noise models fall short and what "hardware-faithful" actually means when two nominally identical QPUs have different noise fingerprints
• How Quantum Elements scaled open-system master-equation simulation from a brute-force ceiling around 16 qubits to 97 qubits using stochastic compression on top of Quantum Monte Carlo
• The compute reality of the distance-7 surface code run on AWS HPC7a — only 96 vCPUs and a few hundred gigabytes of memory, not the thousands of vCPUs they initially feared
• Why decoders are the invisible bottleneck in fault tolerance, and where AI-trained decoders fed by digital twin data could plausibly run inside the real-time quantum-classical loop
• Extending error suppression from physical qubits up to logical qubits — the IBM Eagle work where digital-twin-guided strategies reportedly took entangled logical qubit fidelity from 43% to 95%
• How the same digital twin approach extends to neutral atoms (live today) and ion traps (on the roadmap)
• What Rigetti gets out of the partnership, what it means to have Chad Rigetti on the board, and how Constellation fits alongside real hardware time
• Izhar's "wooden models in the air tunnel" critique of how the quantum industry currently iterates — and what a parallel virtual development track buys you

Resources & Links

Guest & Company

• Izhar Medalsy — Quantum Elements team page — Background and role at Quantum Elements.
• Izhar Medalsy on LinkedIn — Full career arc from ETH biophysics through 3D printing to quantum.
• Quantum Elements — Constellation platform, where listeners can build their own virtual QPU and run circuits, error suppression, and QEC experiments.

Papers & Articles

• AWS Quantum Computing Blog: Decoding realistic QEC syndrome with Quantum Elements digital twins — Primary technical reference for the 97-qubit distance-7 result discussed in the episode.
• The Next Platform: How HPC and AI Digital Twins Accelerate Quantum Error Correction (Apr 17, 2026) — Independent reporting on the AWS/USC/Harvard simulation.
• The Quantum Insider: Quantum Elements & Rigetti collaboration (Apr 21, 2026) — Details on the partnership Izhar describes.
• Guest post: Quantum Digital Twins — The Missing Acceleration Layer — Izhar's own framing of the thesis.
• The Next Platform: Startup Profile of Quantum Elements (Jan 2026) — Background on the company.
• arXiv 2603.14607 — Calibration-Based Digital Twins for IBM Quantum Hardware — Useful independent context on the limits and promise of calibration-based twins.

Key Quotes & Insights

• "Sometimes when I look at the quantum industry, there are instances where you think, well, it's almost like building the next fighter jet with wooden models in the air tunnel." — Izhar's framing for why the field needs a real simulation layer.
• On hardware awareness: each modality, each QPU, sometimes each calibration cycle has its own pulses, its own noise processes, and its own failure modes. You cannot build the control stack without modeling where you are starting from and where you are trying to get to.
• Insight: The brute-force ceiling for open-system master-equation simulation is roughly 16 qubits. Stochastic compression layered on Quantum Monte Carlo is what let Quantum Elements reach distance-7 surface code at 97 qubits — exploiting sparsity rather than enumerating the full state space.
• On logical qubits: "We cannot assume that logical qubits will be noise-free." Error suppression strategies developed at the physical level need to be re-derived at the logical level, and digital twins are how you train and test those strategies before hardware.
• Insight: The most interesting downstream story may not be simulation itself but AI decoders trained on digital-twin-generated data — small enough to run at the edge, fast enough to live inside the real-time quantum-classical loop.

Related Episodes

• Episode 52 — Quantum noise with Daniel Lidar — Quantum Elements' co-founder and CSO on the noise suppression and error correction foundat...