Using variational quantum imaginary time evolution as a training rule can fit toy functions with a KAN-style quantum circuit, but classification performance remains worse than standard approaches.
Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry
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abstract
Variational quantum eigensolvers (VQEs) are leading candidates to demonstrate near-term quantum advantage. Here, we conduct density-matrix simulations of leading gate-based VQEs for a range of molecules. We numerically quantify their level of tolerable depolarizing gate-errors. We find that: (i) The best-performing VQEs require gate-error probabilities between $10^{-6}$ and $10^{-4}$ ( $10^{-4}$ and $10^{-2}$ with error mitigation) to predict, within chemical accuracy, ground-state energies of small molecules with $4-14$ orbitals. (ii) ADAPT-VQEs that construct ansatz circuits iteratively outperform fixed-circuit VQEs. (iii) ADAPT-VQEs perform better with circuits constructed from gate-efficient rather than physically-motivated elements. (iv) The maximally-allowed gate-error probability, $p_c$, for any VQE to achieve chemical accuracy decreases with the number $\ncx$ of noisy two-qubit gates as $p_c\approxprop\ncx^{-1}$. Additionally, $p_c$ decreases with system size, even with error mitigation, implying that larger molecules require even lower gate-errors. Thus, quantum advantage via gate-based VQEs is unlikely unless gate-error probabilities are decreased by orders of magnitude.
fields
quant-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Using variational quantum imaginary time evolution as a training rule can fit toy functions with a KAN-style quantum circuit, but classification performance remains worse than standard approaches.