Solving the Sampling Problem of the Sycamore Quantum Circuits.

@article{Pan2022SolvingTS,
  title={Solving the Sampling Problem of the Sycamore Quantum Circuits.},
  author={Feng Pan and Keyang Chen and P. Zhang},
  journal={Physical review letters},
  year={2022},
  volume={129 9},
  pages={
          090502
        }
}
We study the problem of generating independent samples from the output distribution of Google's Sycamore quantum circuits with a target fidelity, which is believed to be beyond the reach of classical supercomputers and has been used to demonstrate quantum supremacy. We propose a method to classically solve this problem by contracting the corresponding tensor network just once, and is massively more efficient than existing methods in generating a large number of uncorrelated samples with a… 

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References

SHOWING 1-10 OF 16 REFERENCES

Characterizing quantum supremacy in near-term devices

A critical question for quantum computing in the near future is whether quantum devices without error correction can perform a well-defined computational task beyond the capabilities of

Boundaries of quantum supremacy via random circuit sampling

The constraints of the observed quantum runtime advantage in an analytical extrapolation to circuits with a larger number of qubits and gates are examined, suggesting the boundaries of quantum supremacy via random circuit sampling may fortuitously coincide with the advent of scalable, error corrected quantum computing in the near term.

On the complexity and verification of quantum random circuit sampling

Evidence is provided that quantum random circuit sampling, a near-term quantum computational task, is classically hard but verifiable, making it a leading proposal for achieving quantum supremacy.

Simulation of low-depth quantum circuits as complex undirected graphical models

Near term quantum computers with a high quantity (around 50) and quality (around 0.995 fidelity for two-qubit gates) of qubits will approximately sample from certain probability distributions beyond

Complexity-Theoretic Foundations of Quantum Supremacy Experiments

General theoretical foundations are laid for how to use special-purpose quantum computers with 40--50 high-quality qubits to demonstrate "quantum supremacy": that is, a clear quantum speedup for some task, motivated by the goal of overturning the Extended Church-Turing Thesis as confidently as possible.

Strong Quantum Computational Advantage Using a Superconducting Quantum Processor.

This work develops a two-dimensional programmable superconducting quantum processor, Zuchongzhi, which is composed of 66 functional qubits in a tunable coupling architecture and establishes an unambiguous quantum computational advantage that is infeasible for classical computation in a reasonable amount of time.

A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware

Here we present qFlex, a flexible tensor network-based quantum circuit simulator. qFlex can compute both the exact amplitudes, essential for the verification of the quantum hardware, as well as

Efficient parallelization of tensor network contraction for simulating quantum computation

We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach. Our main

Classical Simulation of Intermediate-Size Quantum Circuits

By successfully simulating quantum supremacy circuits of size, this work gives evidence that noisy random circuits with realistic physical parameters may be simulated classically, and suggests that either harder circuits or error-correction may be vital for achieving quantum supremacy from random circuit sampling.

Hyper-optimized tensor network contraction

This work implements new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks, and introduces a hyper-optimization approach, where both the method applied and its algorithmic parameters are tuned during the path finding.