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Rydberg atom hardware (Aquila) produced ground-state energy estimates for the Fermi-Hubbard model at up to 56 qubits using sample-based quantum diagonalization, with sampling that outperformed random sampling even at 10x the sample count. The results are preliminary: the advantage is shown on a 1D model, the convergence is only "near" the ground state, and the gate-based IBM benchmark is a comparison point rather than a head-to-head win. Readers should watch whether the sampling advantage holds for higher-dimensional systems and for tasks beyond this specific benchmark.
Rydberg atom processors beat random sampling for quantum diagonalization
The researchers present a method for calculating the ground state energy of the Fermi-Hubbard model leveraging Rydberg atom processors and sample-based quantum diagonalization (SQD). Rydberg atom sampling shows a clear advantage over random sampling, even with 10× more samples for diagonalization, and converges near to the ground state for up to 56 qubits.
Kübra Yeter-Aydeniz and Nora Bauer of The MITRE Corporation have demonstrated a new method for calculating ground state energies of the Fermi-Hubbard model, a system central to understanding high-temperature superconductivity. Their work uses Rydberg atom processors and a technique called sample-based quantum diagonalization, achieving convergence near the ground state for up to 56 qubits. The researchers observed a clear advantage with Rydberg atom sampling, outperforming random sampling even when using ten times more data for diagonalization. This approach also includes a gap analysis for studying emergent superconductivity within the 1-dimensional Fermi-Hubbard model. The Aquila Rydberg atom processor successfully converged on approximate ground states for the Fermi-Hubbard model with up to 56 qubits using a novel sample-based…
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