arXiv subjects

Xinjian Yan

Publications and source records attributed to Xinjian Yan.

4 recordsLinked to original sources

arXiv2025

Light Cone Cancellation for Variational Quantum Eigensolver in Solving Noisy Max-Cut

Variational Quantum Eigensolver (VQE) is a quantum-classical hybrid algorithm used to estimate the ground energy of a given Hamiltonian. It consists of a parameterized quantum circuit, which the parameters are optimized using a classical optimizer. With the increasing need in solving large-scale problems in real-world applications, solving those large problems with fewer qubits and fewer gates becomes essential, so that we reduce the simulation difficulty and mitigate the effect of noise in real quantum hardware. In this study, we applied the Light Cone Cancellation (LCC) method to reduce the number of qubits and gates required in a two-local ansatz. LCC removes redundant gates that are not required in the calculation of the expectation value for a local observable. This leads to two consequences: 1) the quantum circuit used to create the trial wavefunction of VQE can be broken down into multiple quantum subcircuits with fewer qubits, enabling large-scale problems to be solved without actually simulating the entire circuit; and 2) reduced number of quantum gates in the circuit leads to the noise mitigation in quantum hardware. The main purpose of this work is to demonstrate the effectiveness of this method (called the LCC-VQE) in mitigating the device noise when solving the Max-Cut problem up to 100 qubits, using simulations on small (7-qubit and 27-qubit) fake noisy backends. Employing a single-layer two-local ansatz circuit architecture, the reuslts show that LCC-VQE yields higher approximation ratios than those cases without LCC, implying that the effect of noise is mitigated when LCC is applied. An analysis of more than one layer of two-local ansatz is also performed, but empirical results show that the single-layer ansatz still performs the best among them. We also compare LCC-VQE under noiseless conditions with the Goemans-Williamson algorithm.

quant-ph↗

arXiv2024

Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results.

quant-ph↗

arXiv2023

Iterative Layerwise Training for Quantum Approximate Optimization Algorithm

The capability of the quantum approximate optimization algorithm (QAOA) in solving the combinatorial optimization problems has been intensively studied in recent years due to its application in the quantum-classical hybrid regime. Despite having difficulties that are innate in the variational quantum algorithms (VQA), such as barren plateaus and the local minima problem, QAOA remains one of the applications that is suitable for the recent noisy intermediate scale quantum (NISQ) devices. Recent works have shown that the performance of QAOA largely depends on the initial parameters, which motivate parameter initialization strategies to obtain good initial points for the optimization of QAOA. On the other hand, optimization strategies focus on the optimization part of QAOA instead of the parameter initialization. Instead of having absolute advantages, these strategies usually impose trade-offs to the performance of the optimization problems. One of such examples is the layerwise optimization strategy, in which the QAOA parameters are optimized layer-by-layer instead of the full optimization. The layerwise strategy costs less in total compared to the full optimization, in exchange of lower approximation ratio. In this work, we propose the iterative layerwise optimization strategy and explore the possibility for the reduction of optimization cost in solving problems with QAOA. Using numerical simulations, we found out that by combining the iterative layerwise with proper initialization strategies, the optimization cost can be significantly reduced in exchange for a minor reduction in the approximation ratio. We also show that in some cases, the approximation ratio given by the iterative layerwise strategy is even higher than that given by the full optimization.

quant-ph↗

arXiv2022

Less is More: Surgical Phase Recognition from Timestamp Supervision

Surgical phase recognition is a fundamental task in computer-assisted surgery systems. Most existing works are under the supervision of expensive and time-consuming full annotations, which require the surgeons to repeat watching videos to find the precise start and end time for a surgical phase. In this paper, we introduce timestamp supervision for surgical phase recognition to train the models with timestamp annotations, where the surgeons are asked to identify only a single timestamp within the temporal boundary of a phase. This annotation can significantly reduce the manual annotation cost compared to the full annotations. To make full use of such timestamp supervisions, we propose a novel method called uncertainty-aware temporal diffusion (UATD) to generate trustworthy pseudo labels for training. Our proposed UATD is motivated by the property of surgical videos, i.e., the phases are long events consisting of consecutive frames. To be specific, UATD diffuses the single labelled timestamp to its corresponding high confident ( i.e., low uncertainty) neighbour frames in an iterative way. Our study uncovers unique insights of surgical phase recognition with timestamp supervisions: 1) timestamp annotation can reduce 74% annotation time compared with the full annotation, and surgeons tend to annotate those timestamps near the middle of phases; 2) extensive experiments demonstrate that our method can achieve competitive results compared with full supervision methods, while reducing manual annotation cost; 3) less is more in surgical phase recognition, i.e., less but discriminative pseudo labels outperform full but containing ambiguous frames; 4) the proposed UATD can be used as a plug and play method to clean ambiguous labels near boundaries between phases, and improve the performance of the current surgical phase recognition methods.

cs.CV↗