Avoiding Barren Plateaus with Entanglement
- URL: http://arxiv.org/abs/2406.03748v1
- Date: Thu, 6 Jun 2024 05:06:05 GMT
- Title: Avoiding Barren Plateaus with Entanglement
- Authors: Yuhan Yao, Yoshihiko Hasegawa,
- Abstract summary: We propose incorporating auxiliary control qubits to shift the circuit from a unitary $2$-design to a unitary $1$-design.
We then remove these auxiliary qubits to return to the original circuit structure while preserving the unitary $1$-design properties.
- Score: 1.6574413179773757
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the search for quantum advantage with near-term quantum devices, navigating the optimization landscape is significantly hampered by the barren plateaus phenomenon. This study presents a strategy to overcome this obstacle without changing the quantum circuit architecture. We propose incorporating auxiliary control qubits to shift the circuit from a unitary $2$-design to a unitary $1$-design, mitigating the prevalence of barren plateaus. We then remove these auxiliary qubits to return to the original circuit structure while preserving the unitary $1$-design properties. Our experiment suggests that the proposed structure effectively mitigates the barren plateaus phenomenon. A significant experimental finding is that the gradient of $\theta_{1,1}$, the first parameter in the quantum circuit, displays a broader distribution as the number of qubits and layers increases. This suggests a higher probability of obtaining effective gradients. This stability is critical for the efficient training of quantum circuits, especially for larger and more complex systems. The results of this study represent a significant advance in the optimization of quantum circuits and offer a promising avenue for the scalable and practical implementation of quantum computing technologies. This approach opens up new opportunities in quantum learning and other applications that require robust quantum computing power.
Related papers
- Efficient Quantum Circuit Compilation for Near-Term Quantum Advantage [17.38734393793605]
We propose an approximate method for compiling target quantum circuits into brick-wall layouts.
This new circuit design consists of two-qubit CNOT gates that can be directly implemented on real quantum computers.
arXiv Detail & Related papers (2025-01-13T15:04:39Z) - The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits [15.344606386620136]
variational quantum circuits could be the leading paradigm in the near-term quantum devices and the early fault-tolerant quantum computers.
We use stabilizer bootstrap to optimize quantum neural networks before their quantum execution.
We find that, in a general setup of variational ansatze, the possibility of improvements from the stabilizer bootstrap depends on the structure of the observables and the size of the datasets.
arXiv Detail & Related papers (2024-12-16T01:12:00Z) - QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits [2.3332157823623403]
emphQAdaPrune is an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters.
We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits.
arXiv Detail & Related papers (2024-08-23T19:57:40Z) - 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) - Adaptive Circuit Learning of Born Machine: Towards Realization of
Amplitude Embedding and Data Loading [7.88657961743755]
We present a novel algorithm "Adaptive Circuit Learning of Born Machine" (ACLBM)
Our algorithm is tailored to selectively integrate two-qubit entangled gates that best capture the complex entanglement present within the target state.
Empirical results underscore the proficiency of our approach in encoding real-world data through amplitude embedding.
arXiv Detail & Related papers (2023-11-29T16:47:31Z) - Near-Term Distributed Quantum Computation using Mean-Field Corrections
and Auxiliary Qubits [77.04894470683776]
We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production.
We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
arXiv Detail & Related papers (2023-09-11T18:00:00Z) - 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) - Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits [63.83649593474856]
Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years.
However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number.
This result leads to a general standpoint that deep quantum circuits would not be feasible for practical tasks.
arXiv Detail & Related papers (2022-03-17T15:06:40Z) - Surviving The Barren Plateau in Variational Quantum Circuits with
Bayesian Learning Initialization [0.0]
Variational quantum-classical hybrid algorithms are seen as a promising strategy for solving practical problems on quantum computers in the near term.
Here, we introduce the fast-and-slow algorithm, which uses gradients to identify a promising region in Bayesian space.
Our results move variational quantum algorithms closer to their envisioned applications in quantum chemistry, optimization, and quantum simulation problems.
arXiv Detail & Related papers (2022-03-04T17:48:57Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - 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)
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.