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Quantum computers provide an opportunity to efficiently sample from probability distributions that include non-trivial interference effects between amplitudes. Using a simple process wherein all possible state histories can be specified by…

Quantum Physics · Physics 2019-08-22 Davide Provasoli , Benjamin Nachman , Wibe A. de Jong , Christian W Bauer

Recently, Google announced the first demonstration of quantum computational supremacy with a programmable superconducting processor. Their demonstration is based on collecting samples from the output distribution of a noisy random quantum…

Quantum Physics · Physics 2020-02-07 Scott Aaronson , Sam Gunn

Quantum random sampling is the leading proposal for demonstrating a computational advantage of quantum computers over classical computers. Recently, first large-scale implementations of quantum random sampling have arguably surpassed the…

Quantum Physics · Physics 2023-07-21 Dominik Hangleiter , Jens Eisert

We devise a classical algorithm which efficiently computes the quantum expectation values arising in a class of continuous variable quantum circuits wherein the final quantum observable | after the Heisenberg evolution associated with the…

Quantum Physics · Physics 2021-06-22 Agung Budiyono , Hermawan K. Dipojono

Consider a fixed universe of $N=2^n$ elements and the uniform distribution over elements of some subset of size $K$. Given samples from this distribution, the task of complement sampling is to provide a sample from the complementary subset.…

Quantum Physics · Physics 2026-02-02 Marcello Benedetti , Harry Buhrman , Jordi Weggemans

Recent experiments demonstrated quantum computational advantage in random circuit sampling and Gaussian boson sampling. However, it is unclear whether these experiments can lead to practical applications even after considerable research…

Quantum Physics · Physics 2023-12-14 Chae-Yeun Park , Pablo A. M. Casares , Juan Miguel Arrazola , Joonsuk Huh

Quantum machine learning is emerging as a promising application of quantum computing due to its distinct way of encoding and processing data. It is believed that large-scale quantum machine learning demonstrates substantial advantages over…

Quantum Physics · Physics 2025-01-15 Kiwmann Hwang , Hyang-Tag Lim , Yong-Su Kim , Daniel K. Park , Yosep Kim

We study quantum algorithms working on classical probability distributions. We formulate four different models for accessing a classical probability distribution on a quantum computer, which are derived from previous work on the topic, and…

Quantum Physics · Physics 2019-04-05 Aleksandrs Belovs

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 the capabilities of known classical…

Quantum Physics · Physics 2018-01-23 Sergio Boixo , Sergei V. Isakov , Vadim N. Smelyanskiy , Hartmut Neven

Demonstrating quantum advantage requires experimental implementation of a computational task that is hard to achieve using state-of-the-art classical systems. One approach is to perform sampling from a probability distribution associated…

Quantum Physics · Physics 2024-05-03 Xun Gao , Marcin Kalinowski , Chi-Ning Chou , Mikhail D. Lukin , Boaz Barak , Soonwon Choi

This paper furthers existing evidence that quantum computers are capable of computations beyond classical computers. Specifically, we strengthen the collapse of the polynomial hierarchy to the second level if: (i) Quantum computers with…

Quantum Physics · Physics 2024-10-29 Simon C. Marshall , Scott Aaronson , Vedran Dunjko

A strong analog classical simulation of general quantum evolution is proposed, which serves as a novel scheme in quantum computation and simulation. The scheme employs the approach of geometric quantum mechanics and quantum informational…

Quantum Physics · Physics 2017-03-16 Dong-Sheng Wang

We present a scheme to efficiently simulate, with a classical computer, the dynamics of multipartite quantum systems on which the amount of entanglement (or of correlations in the case of mixed-state dynamics) is conveniently restricted.…

Quantum Physics · Physics 2016-09-08 Guifre Vidal

We develop Monte Carlo methods for sampling random states and corresponding bit strings in qubit systems. To this end, we derive exact probability density functions that yield the Porter-Thomas distribution in the limit of large systems. We…

Quantum Physics · Physics 2025-09-05 Andreas Raab

Quantum computing promises to tackle technological and industrial problems insurmountable for classical computers. However, today's quantum computers still have limited demonstrable functionality, and it is expected that scaling up to…

Simulating quantum systems using classical computing equipment has been a significant research focus. This work demonstrates that circuits as large and complex as the random circuit sampling (RCS) circuits published as a part of Google's…

Quantum Physics · Physics 2024-11-20 Venkateswaran Kasirajan , Torey Battelle , Bob Wold

Sampling from complicated probability distributions is a hard computational problem arising in many fields, including statistical physics, optimization, and machine learning. Quantum computers have recently been used to sample from…

In this work we initiate the question of whether quantum devices can provide us with an almost perfect source of classical randomness, and more generally, suffice for classical cryptographic tasks, such as encryption. Indeed, it is well…

Quantum Physics · Physics 2007-05-23 Yevgeniy Dodis , Renato Renner

The problem of efficient multiplication of large numbers has been a long-standing challenge in classical computation and has been extensively studied for centuries. It appears that the existing classical algorithms are close to their…

Today, people are looking forward to get an awesome computational power. This kind of desire can be answered by quantum computing. By adopting quantum mechanics theory, it can generate a very fast computation result. As known, quantum…

Other Computer Science · Computer Science 2015-12-18 A. B. Mutiara , R. Refianti , J. S. K. Karamoy
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