paper-with-me

홈 › Papers

The Complexity of NISQ

2022-10-13 · Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li

The recent proliferation of NISQ devices has made it imperative to understand their computational power. In this work, we define and study the complexity class $\textsf{NISQ} $, which is intended to encapsulate problems that can be efficiently solved by a classical computer with access to a NISQ device. To model existing devices, we assume the device can (1) noisily initialize all qubits, (2) apply many noisy quantum gates, and (3) perform a noisy measurement on all qubits. We first give evidence that $\textsf{BPP}\subsetneq \textsf{NISQ}\subsetneq \textsf{BQP}$, by demonstrating super-polynomial oracle separations among the three classes, based on modifications of Simon's problem. We then consider the power of $\textsf{NISQ}$ for three well-studied problems. For unstructured search, we prove that $\textsf{NISQ}$ cannot achieve a Grover-like quadratic speedup over $\textsf{BPP}$. For the Bernstein-Vazirani problem, we show that $\textsf{NISQ}$ only needs a number of queries logarithmic in what is required for $\textsf{BPP}$. Finally, for a quantum state learning problem, we prove that $\textsf{NISQ}$ is exponentially weaker than classical computation with access to noiseless constant-depth quantum circuits.

📄 PDF Abstract BibTeX arXiv:2210.07234

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Variational Quantum Approximate Support Vector Machine with Inference Transfer

2022-06-29 · Siheon Park, Daniel K. Park, June-Koo Kevin Rhee

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector …

ClassificationQuantum Machine Learning

A Modified Depolarization Approach for Efficient Quantum Machine Learning

2024-04-10 · Bikram Khanal, Pablo Rivas

Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite the progress, challenges persist due to system noise, err…

Quantum Machine Learning

Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices

2019-04-01 · International Conference on Architectural Support for Programming Languages and Operating Systems 2019 4 · Gushu Li, Yufei Ding, Yuan Xie

Due to little consideration in the hardware constraints, e.g., limited connections between physical qubits to enable two-qubit gates, most quantum algorithms cannot be directly executed on the Noisy Intermediate-Scale Qu…

Heuristic Search

FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm

2025-11-13 · Muhammad Kashif, Shaf Khalid, Alberto Marchisio, Nouhaila Innan 외 arxiv

Hybrid Quantum Neural Networks (HQNNs), which combine parameterized quantum circuits with classical neural layers, are emerging as promising models in the noisy intermediate-scale quantum (NISQ) era. While quantum circui…

Neural Architecture Search

A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor

2021-05-25 · Nathan Eli Miller, Saibal Mukhopadhyay

In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience. The QHAM is based on a quantum neuron design which can be ut…

BIG-bench Machine Learning