Quantum Receiver Enhanced by Adaptive Learning
- URL: http://arxiv.org/abs/2205.07755v1
- Date: Mon, 16 May 2022 15:30:16 GMT
- Title: Quantum Receiver Enhanced by Adaptive Learning
- Authors: Chaohan Cui, William Horrocks, Shuhong Hao, Saikat Guha, N.
Peyghambarian, Quntao Zhuang, Zheshen Zhang
- Abstract summary: We present a general architecture dubbed the quantum receiver enhanced by adaptive learning (QREAL)
QREAL is experimentally implemented in a hardware platform with record-high efficiency.
The error rate is reduced up to 40% over the standard quantum limit in two coherent-state encoding schemes.
- Score: 1.468535802698246
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum receivers aim to effectively navigate the vast quantum-state space to
endow quantum information processing capabilities unmatched by classical
receivers. To date, only a handful of quantum receivers have been constructed
to tackle the problem of discriminating coherent states. Quantum receivers
designed by analytical approaches, however, are incapable of effectively
adapting to diverse environment conditions, resulting in their quickly
diminishing performance as the operational complexities increase. Here, we
present a general architecture, dubbed the quantum receiver enhanced by
adaptive learning (QREAL), to adapt quantum receiver structures to diverse
operational conditions. QREAL is experimentally implemented in a hardware
platform with record-high efficiency. Combining the QREAL architecture and the
experimental advances, the error rate is reduced up to 40% over the standard
quantum limit in two coherent-state encoding schemes.
Related papers
- 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) - Quantum control of Rydberg atoms for mesoscopic-scale quantum state and
circuit preparation [0.0]
Individually trapped Rydberg atoms show significant promise as a platform for scalable quantum simulation.
We show that quantum control can be used to reliably generate fully connected cluster states and to simulate the error-correction encoding circuit.
arXiv Detail & Related papers (2023-02-15T19:00:01Z) - SAT-Based Quantum Circuit Adaptation [0.9784637657097822]
Adapting a quantum circuit from a universal quantum gate set to the quantum gate set of a target hardware modality has a crucial impact on the fidelity and duration of the intended quantum computation.
We develop a satisfiability modulo theories model that determines an optimized quantum circuit adaptation given a set of allowed substitutions and decompositions.
arXiv Detail & Related papers (2023-01-27T14:09:29Z) - Deep Quantum Error Correction [73.54643419792453]
Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing.
In this work, we efficiently train novel emphend-to-end deep quantum error decoders.
The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
arXiv Detail & Related papers (2023-01-27T08:16:26Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum Architecture Search via Deep Reinforcement Learning [0.0]
It is non-trivial to design a quantum gate sequence for generating a particular quantum state with as fewer gates as possible.
We propose a quantum architecture search framework with the power of deep reinforcement learning (DRL) to address this challenge.
We demonstrate a successful generation of quantum gate sequences for multi-qubit GHZ states without encoding any knowledge of quantum physics in the agent.
arXiv Detail & Related papers (2021-04-15T18:53:26Z) - Direct Quantum Communications in the Presence of Realistic Noisy
Entanglement [69.25543534545538]
We propose a novel quantum communication scheme relying on realistic noisy pre-shared entanglement.
Our performance analysis shows that the proposed scheme offers competitive QBER, yield, and goodput.
arXiv Detail & Related papers (2020-12-22T13:06:12Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z)
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.