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Simulating quantum circuit expectation values by Clifford perturbation theory

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arxiv 2306.04797 v2 pith:BZGMQM4R submitted 2023-06-07 quant-ph

Simulating quantum circuit expectation values by Clifford perturbation theory

classification quant-ph
keywords circuitscliffordquantumexpectationgatesclassicalexponentiallymethod
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The classical simulation of quantum circuits is of central importance for benchmarking near-term quantum devices. The fact that gates belonging to the Clifford group can be simulated efficiently on classical computers has motivated a range of methods that scale exponentially only in the number of non-Clifford gates. Here, we consider the expectation value problem for circuits composed of Clifford gates and non-Clifford Pauli rotations, and introduce a heuristic perturbative approach based on the truncation of the exponentially growing sum of Pauli terms in the Heisenberg picture. Numerical results are shown on a Quantum Approximate Optimization Algorithm (QAOA) benchmark for the E3LIN2 problem and we also demonstrate how this method can be used to quantify coherent and incoherent errors of local observables in Clifford circuits. Our results indicate that this systematically improvable perturbative method offers a viable alternative to exact methods for approximating expectation values of large near-Clifford circuits.

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Cited by 2 Pith papers

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  1. Classical simulability of Clifford+T circuits with Clifford-augmented matrix product states

    quant-ph 2024-12 unverdicted novelty 7.0

    Develops an optimization-free disentangling algorithm and algebraic criterion for efficient CAMPS representations of Clifford circuits doped with αI+βP gates, enabling polynomial classical simulation for more circuits...

  2. How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

    quant-ph 2024-11 accept novelty 4.0

    A comprehensive review of scaling paths for superconducting quantum computers, with resource and sensitivity analyses for utility-scale applications under realistic error distributions.