Quantum compression with classically simulatable circuits
- URL: http://arxiv.org/abs/2207.02961v1
- Date: Wed, 6 Jul 2022 20:36:10 GMT
- Title: Quantum compression with classically simulatable circuits
- Authors: Abhinav Anand, Jakob S. Kottmann and Al\'an Aspuru-Guzik
- Abstract summary: We present a strategy to design quantum autoencoders using evolutionary algorithms for transforming quantum information into lower-dimensional representations.
We successfully demonstrate the initial applications of the algorithm for compressing different families of quantum states.
This approach opens the possibility of using classical logic to find low representations of quantum data, using fewer computational resources.
- Score: 0.5735035463793007
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As we continue to find applications where the currently available noisy
devices exhibit an advantage over their classical counterparts, the efficient
use of quantum resources is highly desirable. The notion of quantum
autoencoders was proposed as a way for the compression of quantum information
to reduce resource requirements. Here, we present a strategy to design quantum
autoencoders using evolutionary algorithms for transforming quantum information
into lower-dimensional representations. We successfully demonstrate the initial
applications of the algorithm for compressing different families of quantum
states. In particular, we point out that using a restricted gate set in the
algorithm allows for efficient simulation of the generated circuits. This
approach opens the possibility of using classical logic to find low
representations of quantum data, using fewer computational resources.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - Realization of quantum algorithms with qudits [0.7892577704654171]
We review several ideas indicating how multilevel quantum systems, also known as qudits, can be used for efficient realization of quantum algorithms.
We focus on techniques of leveraging qudits for simplifying decomposition of multiqubit gates, and for compressing quantum information by encoding multiple qubits in a single qudit.
These theoretical schemes can be implemented with quantum computing platforms of various nature, such as trapped ions, neutral atoms, superconducting junctions, and quantum light.
arXiv Detail & Related papers (2023-11-20T18:34:19Z) - Hybrid quantum transfer learning for crack image classification on NISQ
hardware [62.997667081978825]
We present an application of quantum transfer learning for detecting cracks in gray value images.
We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
arXiv Detail & Related papers (2023-07-31T14:45:29Z) - Resource-efficient utilization of quantum computers [0.0]
We suggest a general optimization procedure for hybrid quantum-classical algorithms.
We demonstrate this procedure on a specific example of variational quantum algorithm used to find the ground state energy of a hydrogen molecule.
arXiv Detail & Related papers (2023-05-15T18:01:49Z) - Expressive Quantum Supervised Machine Learning using Kerr-nonlinear
Parametric Oscillators [0.0]
Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era.
Recent researches reveal that the data reuploading, which repeatedly encode classical data into quantum circuit, is necessary for obtaining the expressive quantum machine learning model.
We propose quantum machine learning with Kerrnon Parametric Hilberts (KPOs) as another promising quantum computing device.
arXiv Detail & Related papers (2023-05-01T07:01:45Z) - Simple Quantum State Encodings for Hybrid Programming of Quantum
Simulators [10.953231643211229]
We show the admissibility of using a classical database to encode quantum states for a few practical examples.
We argue in favor of further optimizations for quantum simulation targeting simpler, only'semi-quantum' circuits.
arXiv Detail & Related papers (2022-04-23T10:22:21Z) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z) - Resource-efficient encoding algorithm for variational bosonic quantum
simulations [0.0]
In the Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, quantum resources are limited.
We present a resource-efficient quantum algorithm for bosonic ground and excited state computations.
arXiv Detail & Related papers (2021-02-23T19:00:05Z) - Space-efficient binary optimization for variational computing [68.8204255655161]
We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem.
We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
arXiv Detail & Related papers (2020-09-15T18:17:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.