Reproduce “A Hardware-Trained Generative Quantum Eigensolver for Sample-Based Quantum Diagonalization of Organotin Hydroxides up to 70 Qubits” on OpenResearch
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About this paper
Kunal Kumar, Kimberlee Keithley, Toshiya Iwamori, Shu Kanno, Ikko Hamamura, Takao Kobayashi, Akira Koseki, Qi Gao and 7 more
The authors train a transformer with quantum-hardware feedback to propose circuits for sample-based quantum diagonalization (SQD). Quantum devices sample configurations for classical energy recovery. Tested without retraining on 11 held-out tin active spaces up to 70 qubits, the policy places 27 of 28 large-space runs within 15 mHa of DMRG, with a 4.5 mHa median. This selected-circuit result does not establish chemical accuracy.
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