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Quantum-chemistry calculations on noisy hardware are bottlenecked by qubit count and measurement overhead. We introduce Lossy-QSCI –a compact form of Quantum Selected CI that (i) uses a chemistry-aware lossy Random Linear Encoder (Chemical-RLE) to compress an \(M\) -orbital, \(N\) -electron Hamiltonian to \( O(N \log M) \) qubits, and (ii) restores observables via a lightweight neural–network Fermionic Expectation Decoder (NN-FED). Applied to \( \textrm{C}_2 \) and LiH, Lossy-QSCI attains chemical accuracy with roughly half the qubits and determinants required by standard QSCI, pointing to a practical route for accurate quantum-chemistry on NISQ and early fault-tolerant devices.

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Neural Network Assisted Fermionic Compression Encoding: A Lossy-QSCI Framework for Scalable Quantum Chemistry Simulations

  • 会议论文

  • 发表日期：

    2026年

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  • Yu Cheng Chen,
  • Ronin Wu,
  • M. H. Cheng,
  • Min Hsiu Hsieh

摘要

Quantum-chemistry calculations on noisy hardware are bottlenecked by qubit count and measurement overhead. We introduce Lossy-QSCI –a compact form of Quantum Selected CI that (i) uses a chemistry-aware lossy Random Linear Encoder (Chemical-RLE) to compress an \(M\) -orbital, \(N\) -electron Hamiltonian to \( O(N \log M) \) qubits, and (ii) restores observables via a lightweight neural–network Fermionic Expectation Decoder (NN-FED). Applied to \( \textrm{C}_2 \) and LiH, Lossy-QSCI attains chemical accuracy with roughly half the qubits and determinants required by standard QSCI, pointing to a practical route for accurate quantum-chemistry on NISQ and early fault-tolerant devices.