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arXiv:2107.07231

Open-system modeling of quantum annealing: theory and applications

Ka-Wa Yip

Published Apr 15, 2021• 1 citation
arXivHTMLPDF

Abstract

In this dissertation, we explore how quantum annealing (QA) and its applications behave in an open system setting. We give derivations and numerical recipes for effective parallel simulation methods for time-dependent open dynamics of quantum annealing devices. We consider the weak-coupling limit to an environment and also the case of $1/f$ noise. The stochastic time-dependent quantum trajectories technique can be utilized in studying weak measurements and feedback error correction in quantum annealing. Then we focus on opensystem descriptions of reverse annealing (RA), which is a promising variant and application of quantum annealing. We show that, with various simulation tools, reverse annealing can benefit from the interaction between the annealer and its environment.

DOI
10.25549/usctheses-c89-443939

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In this dissertation, we explore how quantum annealing (QA) and its applications behave in an open system setting. We give derivations and numerical recipes for effective parallel simulation methods for time-dependent open dynamics of quantum annealing devices. We consider the weak-coupling limit to an environment and also the case of 1/f1/f1/f noise. The stochastic time-dependent quantum trajectories technique can be utilized in studying weak measurements and feedback error correction in quantum annealing. Then we focus on opensystem descriptions of reverse annealing (RA), which is a promising variant and application of quantum annealing. We show that, with various simulation tools, reverse annealing can benefit from the interaction between the annealer and its environment.
Abstract : Many computationally challenging problems can be reduced to Quadratic Unconstrained Binary Optimization (QUBO), which can be solved by a quantum evolution from a strong transverse field to a spin glass Hamiltonian (also known as quantum annealing or QA). We have examined open system QA, using theoretical and experimental techniques to deepen our understanding of open system QA by situating it in the context of related, well studied classical and quantum problems. We have explored and elucidated the significance of tunneling in QA with Path Integral Monte Carlo. We have also provided signatures of quantum behavior of QA against closely related simulated annealing implementations, analytically, numerically, and experimentally. We have carried out a comprehensive comparison of geometrically-local open-system QA and classical solvers for computationally hard problems such as MAX-2-SAT. We have exploited and further developed a graph-theoretical mapping between the Ising spin glass partition function and circuit model decision problems, discovered in a previous ARO Quantum Algorithms funded project. We have made significant progress on error correction techniques for QA, both theoretically and experimentally, and in demonstration of quantum speedup.
Quantum annealing is a computational paradigm in which optimisation problems are mapped onto the energy landscape of an interacting quantum system and explored through its dynamical evolution. By continuously transforming a simple initial Hamiltonian into one whose ground state encodes the solution, the system traverses a complex landscape via a combination of quantum fluctuations, tunnelling processes, and dissipative dynamics. Unlike gate-based quantum computing, quantum annealing is a specialised and near-term approach aimed primarily at discrete optimisation and sampling tasks. While it is not expected to provide polynomial-time solutions to NP-hard problems in the worst case, it offers a physically motivated heuristic for navigating rugged energy landscapes that arise across science and engineering. Modern quantum annealers realise programmable spin systems with thousands of qubits, placing them among the largest controllable quantum devices currently available. As a result, their significance extends beyond optimisation: they also function as experimental platforms for studying non-equilibrium many-body quantum dynamics in regimes that are difficult to access using classical simulation. In this review we present an accessible introduction to the principles of quantum annealing, describe the main hardware platforms and algorithmic techniques, and analyse how tunnelling, spectral gaps, and open-system effects shape computational performance. We survey applications ranging from optimisation and machine learning to quantum simulation and many-body physics, and discuss the central challenges in benchmarking, scaling, and control. These perspectives position quantum annealing as a distinctive framework at the interface of optimisation, stochastic sampling, and programmable quantum dynamics, with a role that is complementary to both classical algorithms and gate-based quantum computing.
We study quantum annealing in the quantum Ising model coupled to a thermal environment. When the speed of quantum annealing is sufficiently slow, the system evolves following the instantaneous thermal equilibrium. This quasistatic and isothermal evolution, however, fails near the end of annealing because the relaxation time grows infinitely, therefore yielding excess energy from the thermal equilibrium. We develop a phenomenological theory based on this picture and derive a scaling relation of the excess energy after annealing. The theoretical results are numerically confirmed using a novel non-Markovian method that we recently proposed based on a path-integral representation of the reduced density matrix and the infinite time evolving block decimation. In addition, we discuss crossovers from weak to strong coupling as well as from the adiabatic to quasistatic regime, and propose experiments on the D-Wave quantum annealer.
Adiabatic reverse annealing (ARA) is an improvement to conventional quantum annealing (QA) that uses an initial guess at the desired ground state to circumvent problematic phase transitions. Despite encouraging results in the closed-system setting, Ref. [1] has suggested on the basis of numerical simulations that ARA may lose its advantage in the presence of decoherence. Here, we revisit this problem from a more analytical perspective. Using the ppp-spin model as a solvable example, together with the adiabatic master equation to describe the effects of the environment (valid at weak coupling), we show that ARA can in fact succeed in open systems but that the temperature of the environment plays a key role. We first demonstrate that, in the adiabatic limit, the system will follow the instantaneous equilibrium state as long as the protocol does not pass through any (finite-temperature) phase transitions. Given this, there are two distinct mechanisms by which ARA can break down at high temperature: either there are no paths that avoid transitions, or the equilibrium state itself is disordered. When the temperature is sufficiently low that neither of these occur, then ARA succeeds. Remarkably, there are even situations in which the environment benefits ARA: we find parameter values for which no transition-avoiding paths exist at zero temperature but such paths appear at non-zero temperature.
This study explores the application of quantum annealing (QA) and reverse annealing (RA) to enhance the efficiency and accuracy of Multiple Object Tracking (MOT), a critical task in image recognition with wide-ranging real-time applications. MOT is particularly valuable in scenarios with high object density and frequent occlusions, such as traffic flow measurement, collision avoidance for autonomous systems, and industrial quality control. Traditional MOT methods, often modeled as combinatorial optimization problems, face computational bottlenecks as problem size scales. QA offers a breakthrough by leveraging quantum mechanics, including quantum superposition and tunneling, to rapidly and effectively search for optimal solutions. The proposed methods integrate QA for parallel processing of object tracking data and RA for fine-tuning solutions based on initial predictions, significantly reducing computation time while improving tracking accuracy. Experimental validation using the University at Albany DEtection and TRACking (UA-DETRAC) dataset demonstrates the effectiveness of these quantum-enhanced approaches. By combining multiple tracking solutions through ensemble techniques and leveraging RA for refinement, the methods achieved notable improvements in MOT metrics, including reduced identity switches, enhanced accuracy, and faster processing times. Applications in traffic management, autonomous systems, and industrial automation highlight the transformative potential of quantum-driven MOT systems in addressing real-time operational challenges. This work underscores the promise of quantum technologies as a foundational pillar for advancing next-generation image recognition and tracking systems.