Quantum Dropout: On and Over the Hardness of Quantum Approximate
Optimization Algorithm
- URL: http://arxiv.org/abs/2203.10101v2
- Date: Wed, 21 Jun 2023 02:16:20 GMT
- Title: Quantum Dropout: On and Over the Hardness of Quantum Approximate
Optimization Algorithm
- Authors: Zhen-Duo Wang, Pei-Lin Zheng, Biao Wu, and Yi Zhang
- Abstract summary: We find that difficulty mainly originates from the QAOA quantum circuit instead of the cost function.
Our numerical results confirm improvements in QAOA's performance with various types of quantum-dropout implementation.
- Score: 4.546053983380784
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A combinatorial optimization problem becomes very difficult in situations
where the energy landscape is rugged, and the global minimum locates in a
narrow region of the configuration space. When using the quantum approximate
optimization algorithm (QAOA) to tackle these harder cases, we find that
difficulty mainly originates from the QAOA quantum circuit instead of the cost
function. To alleviate the issue, we selectively dropout the clauses defining
the quantum circuit while keeping the cost function intact. Due to the
combinatorial nature of the optimization problems, the dropout of clauses in
the circuit does not affect the solution. Our numerical results confirm
improvements in QAOA's performance with various types of quantum-dropout
implementation.
Related papers
- Symmetry-preserved cost functions for variational quantum eigensolver [0.0]
Hybrid quantum-classical variational algorithms are considered ideal for noisy quantum computers.
We propose encoding symmetry preservation directly into the cost function, enabling more efficient use of Hardware-Efficient Ans"atze.
arXiv Detail & Related papers (2024-11-25T20:33:47Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Fermionic Quantum Approximate Optimization Algorithm [11.00442581946026]
We propose fermionic quantum approximate optimization algorithm (FQAOA) for solving optimization problems with constraints.
FQAOA tackle the constrains issue by using fermion particle number preservation to intrinsically impose them throughout QAOA.
We provide a systematic guideline for designing the driver Hamiltonian for a given problem Hamiltonian with constraints.
arXiv Detail & Related papers (2023-01-25T18:36:58Z) - Constrained Quantum Optimization for Extractive Summarization on a
Trapped-ion Quantum Computer [13.528362112761805]
We show the largest-to-date execution of a quantum optimization algorithm that preserves constraints on quantum hardware.
We execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159.
We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.
arXiv Detail & Related papers (2022-06-13T16:21:04Z) - How Much Entanglement Do Quantum Optimization Algorithms Require? [0.0]
We study the entanglement generated during the execution of ADAPT-QAOA.
By incrementally restricting this flexibility, we find that a larger amount of entanglement entropy at earlier stages coincides with faster convergence at later stages.
arXiv Detail & Related papers (2022-05-24T18:00:02Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity.
We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree.
These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
arXiv Detail & Related papers (2022-01-06T21:02:30Z) - 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) - 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) - Cross Entropy Hyperparameter Optimization for Constrained Problem
Hamiltonians Applied to QAOA [68.11912614360878]
Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications.
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
arXiv Detail & Related papers (2020-03-11T13:52:41Z)
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.