# 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…

## 5 Citations

### Decomposition of Matrix Product States into Shallow Quantum Circuits

- Computer Science
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This work compares a range of novel and previously-developed algorithmic protocols for decomposing matrix product states of arbitrary bond dimension into low-depth quantum circuits consisting of stacked linear layers of two-qubit unitaries and proposes a proposed decomposition protocol to form a useful ingredient within any joint application of TNs and PQCs.

### A density-matrix renormalization group algorithm for simulating quantum circuits with a finite fidelity

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We develop a density-matrix renormalization group (DMRG) algorithm for the simulation of quantum circuits. This algorithm can be seen as the extension of time-dependent DMRG from the usual situation…

### A polynomial-time classical algorithm for noisy random circuit sampling

- Computer Science
- 2022

This work gives strong evidence that, in the presence of a constant rate of noise per gate, random circuit sampling (RCS) cannot be the basis of a scalable experimental violation of the extended Church-Turing thesis.

### Machine Learning based Discrimination for Excited State Promoted Readout

- Physics, Computer ScienceArXiv
- 2022

Readout data from IBM’s qubit-state-assignment quantum systems are used to measure the effectiveness of using deep neural networks, like feedforward neural networks and various classiﬁcation algorithms, like k- nearest neighbors, decision trees, and Gaussian naive Bayes, for single-qubit and multi-qu bit discrimination.

### Is quantum computing green? An estimate for an energy-efficiency quantum advantage

- Computer Science, Physics
- 2022

It is shown that the green quantum advantage threshold crucially depends on (i) the quality of the experimental quantum gates and (ii) the entanglement generated in the QPU, and algorithms with a power-law decay of singular values of bipartitions – with power-laws exponent α (cid:46) 1 – are identified as the green Quantum Advantage threshold in the near future.

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