Simulating Noisy Variational Quantum Algorithms: A Polynomial Approach

Large-scale variational quantum algorithms are widely recognized as a potential pathway to achieve practical quantum advantages. However, the presence of quantum noise might suppress and undermine these advantages, which blurs the boundaries of classical simulability. To gain further clarity on this matter, we present a novel polynomial-scale method based on the path integral of observable’s backpropagation on Pauli paths (OBPPP). This method efficiently approximates expectation values of operators in variational quantum algorithms with bounded truncation error in the presence of single-qubit Pauli noise. Theoretically, we rigorously prove: (i) For a constant minimal nonzero noise rate $\gamma$, OBPPP’s time and space complexity exhibit a polynomial relationship with the number of qubits $n$, the circuit depth $L$. (ii) For variable $\gamma$, in scenarios where more than two nonzero noise factors exist, the complexity remains $\text{Poly}(n,L)$ if $\gamma$ exceeds $1/\log L$, but grows exponential with $L$ when $\gamma$ falls below $1/L$. Numerically, we conduct classical simulations of IBM’s zero-noise extrapolated experimental results on the 127-qubit Eagle processor [Y. Kim et al., Evidence for the utility of quantum computing before fault tolerance, Nature (London) 618, 500 (2023).]. Our method attains higher accuracy and faster runtime compared to the quantum device. Furthermore, our approach allows us to simulate noisy outcomes, enabling accurate reproduction of IBM’s unmitigated results that directly correspond to raw experimental observations. Our research reveals the vital role of noise in classical simulations and the derived method is general in computing the expected value for a broad class of quantum circuits and can be applied in the verification of quantum computers.