Bifurcation-based quantum annealing with nested spins

Counterdiabatic Reverse Annealing

We present Counterdiabatic Reverse Annealing, a novel quantum annealing protocol that extends the range of application of reverse annealing to the previously inaccessible short-time domain. This is achieved by exploiting approximate counterdiabatic driving expanded in low-order nested commutators. In this work, we offer a comparative study of the performance of this new technique to that of unassisted reverse annealing in terms of metrics such as the ground-state fidelity and the time to solution. We provide a quantitative measure of the energetic cost of counterdiabatic reverse annealing and …

用于高能对撞机径迹重建的量子退火启发式算法

Quantum-Annealing-Inspired Algorithms for Track Reconstruction at High-Energy Colliders

针对高能对撞机径迹重建的计算资源消耗问题，本研究提出使用量子退火启发的模拟分岔算法求解QUBO模型，在保持或提升重建效率与纯度的同时，将运行时间最多减少四个数量级。

Counterdiabatic driving in the quantum annealing of the $p$-spin model: a variational approach

Finding the exact counterdiabatic potential is, in principle, particularly demanding. Following recent progresses about variational strategies to approximate the counterdiabatic operator, in this paper we apply this technique to the quantum annealing of the $p$-spin model. In particular, for $ p = 3 $ we find a new form of the counterdiabatic potential originating from a cyclic ansatz, that allows us to have optimal fidelity even for extremely short dynamics, independently of the size of the system. We compare our results with a nested commutator ansatz, recently proposed in P. W. Claeys, M. P…

Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network: Toward quantum soft computing

The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased s…

量子退火的最优退火路径：通过高效绝热相变实现

Best Annealing Path of Quantum Annealing via Efficient Adiabatic Phase Transition

本文提出嵌套模拟退火（NSA）方法，推导相关能量计算公式，明确局部极大值与收敛速度的关系，证明合理选择NSA参数可实现高效绝热相变，为经典计算机模拟QMC提供参数选择方法。

Improved Boltzmann machines with error corrected quantum annealing

Boltzmann machines are the basis of several deep learning methods that have been successfully applied to both supervised and unsupervised machine learning tasks. These models assume that a dataset is generated according to a Boltzmann distribution, and the goal of the training procedure is to learn the set of parameters that most closely match the input data distribution. Training such models is difficult due to the intractability of traditional sampling techniques, and proposals using quantum annealers for sampling hope to mitigate the sampling cost. However, real physical devices will inevit…

具有四体相互作用的Kerr非线性谐振器光谱测量的理论研究

Theoretical study of the Spectroscopic measurements of Kerr non-linear resonators with four-body interaction

该研究理论分析具有四体相互作用的Kerr非线性谐振器光谱，提出通过共振与弱驱动识别六个特征频率，为表征四体耦合和实现Kerr参量振荡器量子退火提供依据。

受量子退火启发的多喷注聚类算法

Quantum-annealing-inspired algorithms for multijet clustering

本研究将高能对撞机中的喷注重建表述为QUBO问题，提出受量子退火启发的算法（如弹道模拟分岔）解决多喷注聚类难题，实现了一次性全局重建超越双喷注的多喷注，突破了传统迭代方法的局限。

结合机器学习、量子退火与量子启发式优化的蛋白质设计

Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization

本研究提出一种整合机器学习、量子启发式优化的通用蛋白质设计框架，通过迭代学习物理打分函数，在晶格蛋白模型验证中性能优于传统优化，可扩展至更多场景，为蛋白设计提供新范式。

Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization

Recently, inspired by quantum annealing, many solvers specialized for unconstrained binary quadratic programming problems have been developed. For further improvement and application of these solvers, it is important to clarify the differences in their performance for various types of problems. In this study, the performance of four quadratic unconstrained binary optimization problem solvers, namely D-Wave Hybrid Solver Service (HSS), Toshiba Simulated Bifurcation Machine (SBM), Fujitsu DigitalAnnealer (DA), and simulated annealing on a personal computer, was benchmarked. The problems used for…

迈向近似优化中的量子缩放优势

Toward quantum scaling advantage in approximate optimization

研究在近似优化中量子缩放优势，用模拟分岔机（SBM）重新审视相关发现，其源于量子动力学，与量子退火有共同特征，获得可比或更优缩放，明确当前量子退火器优势不明显及未来量子设备可实现优势的问题类。

张量网络方法在优化与采样方面的局限性：与量子和经典 Ising 机器的比较

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines

本文提出结合分支定界与张量网络收缩的确定性低能谱算法，并在 Pegasus、Zephyr 自旋玻璃问题上对比量子退火器和 Simulated Bifurcation，揭示其在大规模优化与多样采样中的局限。