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arXiv:1501.06914

Entanglement as a resource in adiabatic quantum optimization

Bela Bauer, Lei Wang, Iztok Pižorn, Matthias Troyer

Published Jan 27, 2015• 7 citations
arXivHTMLPDF

Abstract

We explore the role of entanglement in adiabatic quantum optimization by performing approximate simulations of the real-time evolution of a quantum system while limiting the amount of entanglement. To classically simulate the time evolution of the system with a limited amount of entanglement, we represent the quantum state using matrix-product states and projected entangled-pair states. We show that the probability of finding the ground state of an Ising spin glass on either a planar or non-planar two-dimensional graph increases rapidly as the amount of entanglement in the state is increased. Furthermore, we propose evolution in complex time as a way to improve simulated adiabatic evolution and mimic the effects of thermal cooling of the quantum annealer.

DOI
10.48550/arxiv.1501.06914

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We explore the role of entanglement in adiabatic quantum optimization by performing approximate simulations of the real-time evolution of a quantum system while limiting the amount of entanglement. To classically simulate the time evolution of the system with a limited amount of entanglement, we represent the quantum state using matrix-product states and projected entangled-pair states. We show that the probability of finding the ground state of an Ising spin glass on either a planar or non-planar two-dimensional graph increases rapidly as the amount of entanglement in the state is increased. Furthermore, we propose evolution in complex time as a way to improve simulated adiabatic evolution and mimic the effects of thermal cooling of the quantum annealer.
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Energy minimization of Ising spin-glasses has played a central role in statistical and solid-state physics, facilitating studies of phase transitions and magnetism. Recent proposals suggest using Ising spin-glasses for non-traditional computing as a way to harness the nature's ability to find min-energy configurations, and to take advantage of quantum tunneling to boost combinatorial optimization. Laboratory demonstrations have been unconvincing so far and lack a non-quantum baseline for definitive comparisons. In this work we (i) design and evaluate new computational techniques to simulate natural energy minimization in spin glasses and (ii) explore their application to study design alternatives in quantum adiabatic computers. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds ground states on 100 spins, while our local search approximates ground states on 1, 000, 000 spins. We use this computational tool as a simulator to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.
Quantum annealing with the D-Wave Advantage system in the random Ising model on a cubic lattice is simulated using a three-dimensional (3D) tensor network. The Hamiltonian is driven across a quantum phase transition from a paramagnetic phase to a spin-glass phase. The network is represented as a tensor product state, also known-particularly in two dimensions-as a projected entangled-pair state (PEPS). The annealing procedure is repeated for a range of annealing times in order to test the Kibble-Zurek (KZ) power law governing the residual energy at the end of the annealing ramp. For an infinite lattice with periodic nearest-neighbor random Ising couplings, the final energy is evaluated using a deterministic method. For a finite lattice with open boundaries, we introduce a more efficient Monte Carlo sampling approach. In both cases, the residual energy as a function of annealing time approaches the KZ power law as the annealing time increases.
Quantum many-body systems are challenging targets for computational physics due to their large number of degrees of freedom. The tensor networks, particularly Tensor Product States (TPS) and Projected Entangled Pair States (PEPS), effectively represent these systems on two-dimensional lattices. However, the technical complexity of TPS/PEPS-based coding can be challenging for many researchers to manage effectively. To reduce this problem, we developed TeNeS (Tensor Network Solver). This paper introduces TeNeS -v2, which extends TeNeS with real-time and finite temperature simulations, providing deeper insights into quantum many-body systems. We detail the new algorithms, input/output design, and application examples, demonstrating TeNeS -v2's applicability to various quantum spin and Bose models on two-dimensional lattices. New version program summary Program Title: TeNeS CPC Library link to program files: https://doi.org/10.17632/psm26xxbvd.2 Developer's repository link:: https://github.com/issp-center-dev/TeNeS Licensing provisions: GPLv3 Programming language: C++11 Journal reference of previous version: Comput. Phys. Commun. (2022) 279, 108437, doi:10.1016/j.cpc.2022.108437 Reasons for the new version: To extend the capabilities of TeNeS to include real-time and finite temperature simulations, enabling deeper insights into quantum many-body systems beyond ground states. Summary of revisions: TeNeS -v2 introduces real-time evolution and finite temperature simulation capabilities, expanding the range of quantum many-body phenomena that can be studied. Nature of problem: Quantum many-body systems are extremely difficult to simulate because of the huge dimension of the Hilbert space. Conventional methods have difficulty in accurately representing these systems, especially for properties beyond small lattices and ground states. Advanced computational techniques are required to efficiently capture the dynamics and thermal properties of these systems. Solution method: TeNeS employs tensor networks, specifically Tensor Product States (TPS) and Projected Entangled Pair States (PEPS), to efficiently represent quantum many-body states on two-dimensional lattices. The real-time evolution is handled through a straightforward extension of imaginary time evolution methods, allowing the study of dynamical properties. Finite temperature simulations are conducted using an imaginary time evolution approach starting from the infinite-temperature mixed state. These methods enable the accurate calculation of various physical properties in different quantum states. Additional comments including restrictions and unusual features: TeNeS -v2 maintains ease of use by providing flexible input file configurations and supporting a variety of two-dimensional lattice models. Increasing the bond dimension to improve accuracy requires more computational resources. Finite temperature calculations may exhibit unphysical behavior due to limitations of the tensor network approximation method. Users should be aware of these potential problems and interpret the results following the suggestions made in the text.
Quantum entanglement, a bizarre phenomenon in which two or more particles become linked in such a way that they share the same fate, holds immense promise for quantum computing and quantum information processing. However, harnessing entanglement effectively requires precise control over its quantum states. This paper explores the application of physics simulation techniques to optimize quantum entanglement states, specifically focusing on a novel geometric optimization method leveraging the unique properties of entanglement. Traditional optimization methods struggle to fully capture the intricacies of entangled states, necessitating a shift towards a more sophisticated approach. We propose a new method that utilizes entanglement to guide the search for optimal quantum states, resulting in enhanced efficiency and control over quantum computation. The core claim is that this method provides a significant improvement in the performance of quantum algorithms by effectively manipulating entangled states. The mechanism involves the exploitation of entanglement's non-classical correlation to generate a more complex and structured quantum state, facilitating faster and more accurate computations. This research presents a theoretical framework and preliminary results demonstrating the effectiveness of this approach.