Imaginary Hamiltonian variational ansatz for combinatorial optimization problems
Peking University（北京大学）；Collaborative Innovation Center of Quantum Matter（量子物质协同创新中心）；Center for High Energy Physics, Peking University（北京大学高能物理中心）；Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN（理化学研究所跨学科理论与数学科学项目（iTHEMS））；Deutsches Elektronen-Synchrotron DESY（德国电子同步加速器）；Computation-Based Science and Technology Research Center, The Cyprus Institute（塞浦路斯研究所计算科学与技术研究中心）；Institut für Physik, Humboldt-Universität zu Berlin（柏林洪堡大学物理研究所）
Obtaining exact solutions to combinatorial optimization problems using classical computing is computationally expensive. The current tenet in the field is that quantum computers can address these problems more efficiently. While promising algorithms require fault-tolerant quantum hardware, variational algorithms have emerged as viable candidates for near-term devices. The success of these algorithms hinges on multiple factors, with the design of the ansatz having the utmost importance. It is known that popular approaches such as quantum approximate optimization algorithm (QAOA) and quantum annealing suffer from adiabatic bottlenecks, that lead to either larger circuit depth or evolution time. On the other hand, the evolution time of imaginary time evolution is bounded by the inverse energy gap of the Hamiltonian, which is constant for most non-critical physical systems. In this work, we propose imaginary Hamiltonian variational ansatz ($i$HVA) inspired by quantum imaginary time evolution to solve the MaxCut problem. We introduce a tree arrangement of the parametrized quantum gates, enabling the exact solution of arbitrary tree graphs using the one-round $i$HVA. For randomly generated $D$-regular graphs, we numerically demonstrate that the $i$HVA solves the MaxCut problem with a small constant number of rounds and sublinear depth, outperforming QAOA, which requires rounds increasing with the graph size. Furthermore, our ansatz solves MaxCut exactly for graphs with up to 24 nodes and $D \leq 5$, whereas only approximate solutions can be derived by the classical near-optimal Goemans-Williamson algorithm. We validate our simulated results with hardware demonstrations on a graph with 67 nodes.
Imaginary time evolution is a powerful tool for studying quantum systems. While it is possible to simulate with a classical computer, the time and memory requirements generally scale exponentially with the system size. Conversely, quantum computers can efficiently simulate quantum systems, but not non-unitary imaginary time evolution. We propose a variational algorithm for simulating imaginary time evolution on a hybrid quantum computer. We use this algorithm to find the ground-state energy of many-particle systems; specifically molecular hydrogen and lithium hydride, finding the ground state …
We introduce a variational wavefunction for many-body ground states that involves imaginary time evolution with two different Hamiltonians in an alternating fashion with variable time intervals. We successfully apply the ansatz on the one- and two-dimensional transverse-field Ising model and systematically study its scaling for the one-dimensional model at criticality. We find the total imaginary time required scales logarithmically with system size, in contrast to the linear scaling in conventional Quantum Monte Carlo. We suggest this is due to unique dynamics permitted by alternating imagina…
Detrimental Agnostic Entanglement: The Case Against Hardware-Efficient Ansätze for Combinatorial Optimization
Institute for Computer Science, LMU Munich（慕尼黑大学计算机科学研究所）；relAI – Konrad Zuse School of Excellence in Reliable AI, LMU Munich（LMU慕尼黑 relAI – 康拉德·楚泽可靠人工智能卓越学院）
Constant-Depth Quantum Imaginary Time Evolution Using Dynamic Fan-out Circuits
Jeppesen ForeFlight（杰普森福飞）；Department of Microtechnology and Nanoscience (MC2), Chalmers University of Technology（查尔姆斯理工大学微技术和纳米科学系（MC2））；Department of Chemistry and Chemical Engineering, Chalmers University of Technology（查尔姆斯理工大学化学与化学工程系）；Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯多夫亥姆霍兹中心高级系统理解中心）；Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, TU Dresden（德累斯顿/莱比锡可扩展数据分析与人工智能中心，德累斯顿工业大学）
An adaptive variational quantum imaginary time evolution (AVQITE) approach is introduced that yields efficient representations of ground states for interacting Hamiltonians on near-term quantum computers. It is based on McLachlan's variational principle applied to imaginary time evolution of variational wave functions. The variational parameters evolve deterministically according to equations of motions that minimize the difference to the exact imaginary time evolution, which is quantified by the McLachlan distance. Rather than working with a fixed variational ansatz, where the McLachlan dista…
The performance of variational quantum algorithms relies on the success of using quantum and classical computing resources in tandem. Here, we study how these quantum and classical components interrelate. In particular, we focus on algorithms for solving the combinatorial optimization problem MaxCut, and study how the structure of the classical optimization landscape relates to the quantum circuit used to evaluate the MaxCut objective function. In order to analytically characterize the impact of quantum features on the critical points of the landscape, we consider a family of quantum circuit a…
The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems. While there is evidence suggesting that the fixed form of the standard QAOA ansatz is not optimal, there is no systematic approach for finding better ansätze. We address this problem by developing an iterative version of QAOA that is problem-tailored, and which can also be adapted to specific hardware constraints. We simulate the algorithm on a class of Max-Cut graph problems and show that it converges much faster than the standard QAOA, whi…
Variational quantum algorithms for permutation-based combinatorial problems: Optimal ansatz generation with applications to quadratic assignment problems and beyond
Institut Polytechnique de Paris（巴黎理工学院）；ENSTA（国立高等先进技术学院）；UMA（应用数学实验室）；Conservatoire National des Arts et Métiers（国立工艺博物馆）；CEDRIC（计算机科学与数字系统研究中心）；École Nationale Supérieure d’Informatique pour l’Industrie et l’Entreprise（工业与企业高级信息学院）
Quantum Imaginary Time Evolution on an Infinite 1D Chain
National Taiwan University（台湾大学）；Center for Theoretical Physics, National Taiwan University（台湾大学理论物理中心）；Department of Physics, National Taiwan University（台湾大学物理系）
The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the QAOA ansatz is not optimal, however. To address this problem, we propose an alternative ansatz, which we call QAOA+, that augments the traditional $p = 1$ QAOA ansatz with an additional multiparameter problem-independent layer. The QAOA+ ansatz allows obtaining higher approximation ratios than $p = 1$ QAOA while keeping the circuit depth below that of $p = …
We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state involving no entanglement, as well as an imaginary-time-dependent Hamiltonian interpolating between a given graph and a subgraph with two edges excised. We apply the method to thousands of randomly selected graphs with up to fifty vertices. We show that our algorithm exhibits a 93% and above performance converging to the maximum solution of the MaxCut problem for all considered graphs. Our results compare favorably wi…
Iterative warm-start optimization with quantum imaginary time evolution
Oak Ridge National Laboratory（橡树岭国家实验室）；Quantum Artificial Intelligence Laboratory, USRA RIACS, NASA Ames Research Center（量子人工智能实验室，USRA RIACS，NASA艾姆斯研究中心）
We present a new hybrid, local search algorithm for quantum approximate optimization of constrained combinatorial optimization problems. We focus on the Maximum Independent Set problem and demonstrate the ability of quantum local search to solve large problem instances on quantum devices with few qubits. This hybrid algorithm iteratively finds independent sets over carefully constructed neighborhoods and combines these solutions to obtain a global solution. We study the performance of this algorithm on 3-regular, Community, and Erdős-Rényi graphs with up to 100 nodes.
Quantifying the flatness of the objective-function landscape associated with unstructured parameterized quantum circuits is important for understanding the performance of variational algorithms utilizing a "hardware-efficient ansatz", particularly for ensuring that a prohibitively flat landscape -- a so-called "barren plateau" -- is avoided. For a model of such ansätze, we relate the typical landscape flatness to a certain family of random walks, enabling us to derive a Monte Carlo algorithm for efficiently, classically estimating the landscape flatness for any architecture. The statistical pi…
Hardware-Efficient Ground-State Preparation using Variational Imaginary-Time Majorana Evolution
IQM Quantum Computers GmbH（IQM量子计算机有限公司）；RPTU Kaiserslautern-Landau（莱布尼茨技术大学凯泽斯劳滕-兰道分校）；Universidad Autónoma de Madrid（马德里自治大学）；Technical University of Munich（慕尼黑工业大学）
Developing scalable quantum algorithms to study finite-temperature physics of quantum many-body systems has attracted considerable interest due to recent advancements in quantum hardware. However, such algorithms in their present form require resources that exceed the capabilities of current quantum computers except for a limited range of system sizes and observables. Here, we report calculations of finite-temperature properties including energies, static and dynamical correlation functions, and excitation spectra of spin Hamiltonians with up to four sites on five-qubit IBM Quantum devices. Th…
Digitized Counter-Diabatic Quantum Optimization for Bin Packing Problem
Instituto de Ciencia de Materiales de Madrid (CSIC)（马德里材料科学研究所）；Department of Physical Chemistry, University of the Basque Country UPV/EHU（巴斯克大学物理化学系）；Kipu Quantum GmbH（基普量子有限公司）
Complex quantum trajectory approach, which arose from a modified de Broglie-Bohm interpretation of quantum mechanics, has attracted much attention in recent years. The exact complex trajectories for the Eckart potential barrier and the soft potential step, plotted in a previous work, show that more trajectories link the left and right regions of the barrier, when the energy is increased. In this paper, we evaluate the reflection probability using a new ansatz based on these observations, as the ratio between the total probabilities of reflected and incident trajectories. While doing this, we a…
Physics-inspired transformer quantum states via latent imaginary-time evolution
Graduate School of Information Science and Technology, The University of Osaka（信息科学与技术研究生学校，大阪大学）；Fujitsu Laboratories, Fujitsu Limited（富士通实验室，富士通有限公司）；Center for Advanced Intelligence Project, RIKEN（先进智能项目中心，理化学研究所）
Proposed hybrid algorithms encode a combinatorial cost function into a problem Hamiltonian and optimize its energy by varying over a set of states with low circuit complexity. Classical processing is typically only used for the choice of variational parameters following gradient descent. As a consequence, these approaches are limited by the descriptive power of the associated states. We argue that for certain combinatorial optimization problems, such algorithms can be hybridized further, thus harnessing the power of efficient non-local classical processing. Specifically, we consider combining …
The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansatz for the quantum circuit architecture, together with a prescription for choosing the variational parameters of the circuit. We propose modifications to both. First, we define the Gibbs objective function and show that it is superior to the energy expectation value for use as an objective function in tuning the variational parameters. Second, we describe an Ansatz Architecture Search (AAS) algorithm for searching th…
Variational Quantum Algorithm for Constrained Combinatorial Optimization Problems
College of Science, North China Institute of Science and Technology（华北科技学院理学院）；School of Mathematical Sciences, Capital Normal University（首都师范大学数学科学学院）
The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a need to design ansatz for better optimization. To this end, we propose a digitized version of QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better ansatz, along with the Hamiltonian and mixing terms, enhancing th…
We construct an exact map between a tight-binding model on any bipartite lattice in presence of dephasing noise and a Hubbard model with imaginary interaction strength. In one dimension, the exact many-body Liouvillian spectrum can be obtained by application of the Bethe ansatz method. We find that both the non-equilibrium steady state and the leading decay modes describing the relaxation at late times are related to the eta-pairing symmetry of the Hubbard model. We show that there is a remarkable relation between the time-evolution of an arbitrary k-point correlation function in the dissipati…
The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we introduce a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good pa…
