Compression of quantum shallow-circuit states
- URL: http://arxiv.org/abs/2404.11177v2
- Date: Mon, 30 Sep 2024 09:53:27 GMT
- Title: Compression of quantum shallow-circuit states
- Authors: Yuxiang Yang,
- Abstract summary: Storing quantum information generated by shallow circuits is a fundamental question of both theoretical and practical importance.
We show that $N$ copies of an unknown $n$-qubit state can be compressed into a hybrid memory of $O(nlog N)$ (qu)bits.
- Score: 11.305910458469098
- License:
- Abstract: Shallow quantum circuits feature not only computational advantages over their classical counterparts but also cutting-edge applications. Storing quantum information generated by shallow circuits is a fundamental question of both theoretical and practical importance that remained largely unexplored. In this work, we show that $N$ copies of an unknown $n$-qubit state generated by a fixed-depth circuit can be compressed into a hybrid memory of $O(n\log_2 N)$ (qu)bits, which achieves the optimal scaling of memory cost. Our work shows that the computational complexity of resources can significantly impact the rate of quantum information processing, offering a unique and unified view of quantum Shannon theory and quantum computing.
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) - The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - 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) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - 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) - Limitations of Noisy Quantum Devices in Computational and Entangling
Power [5.178527492542246]
We show that noisy quantum devices with a circuit depth of more than $O(log n)$ provide no advantages in any quantum algorithms.
We also study the maximal entanglement that noisy quantum devices can produce under one- and two-dimensional qubit connections.
arXiv Detail & Related papers (2023-06-05T12:29:55Z) - Quantum compression with classically simulatable circuits [0.5735035463793007]
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.
arXiv Detail & Related papers (2022-07-06T20:36:10Z) - Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most.
We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
arXiv Detail & Related papers (2022-04-12T19:39:31Z) - Quantum State Preparation with Optimal Circuit Depth: Implementations
and Applications [10.436969366019015]
We show that any $Theta(n)$-depth circuit can be prepared with a $Theta(log(nd)) with $O(ndlog d)$ ancillary qubits.
We discuss applications of the results in different quantum computing tasks, such as Hamiltonian simulation, solving linear systems of equations, and realizing quantum random access memories.
arXiv Detail & Related papers (2022-01-27T13:16:30Z) - Configurable sublinear circuits for quantum state preparation [1.9279780052245203]
We show a configuration that encodes an $N$-dimensional state by a quantum circuit with $O(sqrtN)$ width and depth and entangled information in ancillary qubits.
We show a proof-of-principle on five quantum computers and compare the results.
arXiv Detail & Related papers (2021-08-23T13:52:43Z)
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.