Quantum Elements and Planckian Partner to Develop Architecture-Specific Digital Twins for Superconducting Quantum Processors

Quantum Elements, a Los Angeles-based provider of AI-powered digital twins for quantum computing developers, announced a development agreement on July 14, 2026, with Planckian, an Italian quantum computing company pioneering a novel superconducting quantum processor architecture. The collaboration will produce architecture-specific noise models and digital twin capabilities that characterize the physical noise environment of Planckian’s processors—including coherence, leakage, and operation-level error sources—to support evaluation of quantum error correction (QEC) schemes across its unique designs.

Digital Twin Noise Characterization

Quantum processors remain susceptible to environmental noise, crosstalk, and control imperfections that impede progress toward fault-tolerant systems. Conventional direct density-matrix simulation of open quantum systems becomes computationally prohibitive as qubit counts increase. Quantum Elements’ Digital Twins technology models noisy quantum-circuit behavior with substantially lower resources while retaining the dynamics required for studying QEC, correlated noise, and decoder performance.

Under the agreement, Quantum Elements will develop models that accurately reflect Planckian’s architectures, which employ shared/global control lines to decouple control complexity from qubit count and address the well-known wiring bottleneck in conventional superconducting systems as detailed in analyses of global control approaches. This architecture-specific characterization is essential because Planckian’s approach reshapes the error landscape relative to standard designs.

The practical foundation for this work was demonstrated in a collaboration involving Quantum Elements, the University of Southern California, Harvard University, and Amazon Web Services, where a Quantum Monte Carlo-accelerated digital twin simulated a 97-physical-qubit, distance-7 surface-code syndrome-extraction round. A brute-force open-system simulation of the same system would require tracking 4⁹⁷ density-matrix entries; the QMC method completed in approximately one hour on a single compute node while capturing coherent and correlated noise effects.

Supporting Scalable Error Correction Strategies

“Our Digital Twins platform can accurately mirror quantum systems on classical computers, leading to a clear development path from system co-design to quantum error correction and all the way to fault-tolerant quantum computing for Planckian and other quantum hardware companies,” said Izhar Medalsy, co-founder and CEO of Quantum Elements.

Michele Dallari, co-founder and CEO of Planckian, noted that the company’s architecture removes control complexity and infrastructure overhead that typically limit scaling of conventional superconducting processors. “A new approach also reshapes the errors the system has to contend with. That makes architecture-specific characterization essential: we need a faithful picture of our own noise environment before we decide how to correct it. Quantum Elements’ digital twins enable us to evaluate error-correction schemes against a realistic model of our processors, on classical hardware and well ahead of scaling, the kind of groundwork a credible path to fault tolerance actually depends on.”

By enabling early, hardware-faithful evaluation of QEC performance on classical resources, the partnership supports Planckian’s strategy of combining superconducting circuit reliability with reduced wiring overhead. This aligns with broader industry efforts to close the loop between hardware design, noise characterization, and error-correction decoding prior to large-scale physical deployment, as further contextualized in scalable superconducting processor initiatives and digital twin QEC simulations.

Bottom Line

The partnership equips Planckian with tailored digital twins to evaluate quantum error correction on its unique superconducting architectures before physical scaling.

Find out more here.

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