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Cambridge University Press eBooks

Adiabatic Quantum Computing

Juan José García‐Ripoll

Published Jul 31, 2022• 2 citations
Publisher

Abstract

In this chapter, we discuss the notion of a quantum simulator as a device that emulates a complex quantum many-body Hamiltonian, and a quantum annealer as an extension of such a paradigm that focuses on the preparation of the ground state in those Hamiltonians. Starting from the Landau–Zener processes and the adiabatic theorem, we illustrate how such ground states can be prepared by slow (adiabatic) deformations of a Hamiltonian. We discuss how this results in an adiabatic quantum computing algorithm and how, depending on the Hamiltonian we apply it to, we can solve problems of different classical and quantum complexity. The chapter closes with a thorough discussion of the D-Wave quantum annealer as a real-world superconducting quantum simulator of Ising-type models. This discussion centers both on the design of the superconducting annealers as well as on the conclusion from the literature on how this device works in practice, including how quantum operations are still possible even in the pressence of decoherence. We close the chapter with an outlook on the challenges that need to be overcome for making coherent quantum annealers and universal adiabatic quantum computers.

DOI
10.1017/9781316779460.009

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In this chapter, we discuss the notion of a quantum simulator as a device that emulates a complex quantum many-body Hamiltonian, and a quantum annealer as an extension of such a paradigm that focuses on the preparation of the ground state in those Hamiltonians. Starting from the Landau–Zener processes and the adiabatic theorem, we illustrate how such ground states can be prepared by slow (adiabatic) deformations of a Hamiltonian. We discuss how this results in an adiabatic quantum computing algorithm and how, depending on the Hamiltonian we apply it to, we can solve problems of different classical and quantum complexity. The chapter closes with a thorough discussion of the D-Wave quantum annealer as a real-world superconducting quantum simulator of Ising-type models. This discussion centers both on the design of the superconducting annealers as well as on the conclusion from the literature on how this device works in practice, including how quantum operations are still possible even in the pressence of decoherence. We close the chapter with an outlook on the challenges that need to be overcome for making coherent quantum annealers and universal adiabatic quantum computers.
We review here some recent work in the field of quantum annealing, alias adiabatic quantum computation. The idea of quantum annealing is to perform optimization by a quantum adiabatic evolution which tracks the ground state of a suitable time-dependent Hamiltonian, where 'ℏ' is slowly switched off. We illustrate several applications of quantum annealing strategies, starting from textbook toy-models—double-well potentials and other one-dimensional examples, with and without disorder. These examples display in a clear way the crucial differences between classical and quantum annealing. We then discuss applications of quantum annealing to challenging hard optimization problems, such as the random Ising model, the travelling salesman problem and Boolean satisfiability problems. The techniques used to implement quantum annealing are either deterministic Schrödinger's evolutions, for the toy models, or path-integral Monte Carlo and Green's function Monte Carlo approaches, for the hard optimization problems. The crucial role played by disorder and the associated non-trivial Landau–Zener tunnelling phenomena is discussed and emphasized.
Quantum physics has the potential to revolutionize computer science by building what is called a quantum computer. Quantum computers can perform tasks that are impossible with Turing-machine based classical computers (e.g., generating random sequences of bits) and can outperform classical computers (to different degrees) in a variety of classical computation problems. Several different models of quantum computing exist. Based on the Adiabatic Theorem in physics, Adiabatic Quantum Computing is a novel computing framework that is drastically different from the more traditional quantum logic gate model. Traditionally, quantum computing has only been tested on devices with a very small number of qubits, severely limiting their uses in practice. In recent years however, advances in engineering and physics have allowed the construction for (relatively) large machines called quantum annealers that do what is known as a specific type of adiabatic quantum computation called quantum annealing. Solving classical computation problems with the quantum annealing framework is not an easy task and there are many obstacles to overcome if we want to achieve any kind of speedups over classical algorithms. In this thesis, we present quantum annealing solutions to several classical problems including the Hamiltonian Cycle Problem, Mixed Dominating Set Problem, Graph Isomorphism Problem, Densest k-Subgraph Problem and the Maximum Weighted Independent Set Problem. Experimental results of these quantum solutions on DWave quantum annealers are also reported. We also look at some of the bottlenecks in the quantum annealing framework and propose several novel approach to mitigate their effects on the efficiency of the quantum solutions.
Abstract Having spent almost an entire book on working out that Hopfield nets and adiabatic quantum computing are closely related concepts, this chapter departs from our main story line of problem solving as energy minimization and concludes our study with an outlook to the possibilities of quantum gate computing. Indeed, the central ideas behind quantum gate computing differ from most of what we studied so far but our study still has us prepared for getting to grips with the basics of this paradigm. As this paradigm posits that quantum algorithms are sequences of unitary operators which each act on the joint state of a qubit system, we will look at the circuit model of quantum computing and see that quantum circuits consist of quantum gates which are interconnected by quantum wires where the latter represent the states of qubits on which the former perform unitary operations. As a practical example, we discuss how such circuits could compute univariate Boolean functions. This then seamlessly allows us to finally study Deutsch’s problem and algorithm and therefore to see an instance of a computational problem for which quantum computing algorithms can be exponentially faster than their classical counterparts.
Quantum annealing represents an essential milestone towards the goal of adiabatic quantum computing. In quantum annealing, the computation involves finding the ground state of a classical Ising-like Hamiltonian realized as interactions between qubits. Quantum fluctuations are introduced to allow the wavefunction of the qubits to explore the energy landscape, the hope being that the wavefunction finds a minimum energy configuration and possibly giving the result of the computation. While quantum annealing likely may not be as powerful as adiabatic quantum computing, it is possible that it may be better at optimization compared to analogous classical algorithms. In physical realizations of quantum annealing, there are still questions as to the role of quantum fluctuations in the operation of a device given the short coherence times of the individual qubits. These questions have consistently posed a serious theoretical challenge making it difficult to verify experimental results. Here we simplify the problem by considering a system of qubits with ferromagnetic interactions, modeling the decoherence effects as classical noise in the transverse-field of each qubit. We compare the calculations to data collected from a system of manufactured qubits produced by D-wave Systems by performing a finite-size scaling analysis that captures the competition between quantum fluctuations of the transverse-field and bit-flip errors from the noise. We argue that on time-scales larger than the single-qubit decoherence time, the device produces the expected quantum fluctuations for the many-body system. Using this finite-size scaling, one can diagnose sources of noise in the system. Hopefully, in the near future, these devices will not only be realizing coherent quantum annealing but will likely be useful as another example of synthetic quantum matter.
Adiabatic quantum computing enables the preparation of many-body ground states. Realization poses major experimental challenges: Direct analog implementation requires complex Hamiltonian engineering, while the digitized version needs deep quantum gate circuits. To bypass these obstacles, we suggest an adiabatic variational hybrid algorithm, which employs short quantum circuits and provides a systematic quantum adiabatic optimization of the circuit parameters. The quantum adiabatic theorem promises not only the ground state but also that the excited eigenstates can be found. We report the first experimental demonstration that many-body eigenstates can be efficiently prepared by an adiabatic variational algorithm assisted with a multiqubit superconducting coprocessor. We track the real-time evolution of the ground and excited states of transverse-field Ising spins with a fidelity that can reach about 99%.
Controlled quantum mechanical devices provide a means of simulating more complex quantum systems exponentially faster than classical computers. Such "quantum simulators" rely heavily upon being able to prepare the ground state of Hamiltonians, whose properties can be used to calculate correlation functions or even the solution to certain classical computations. While adiabatic preparation remains the primary means of producing such ground states, here we provide a different avenue of preparation: cooling to the ground state via simulated dissipation. This is in direct analogy to contemporary efforts to realize generalized forms of simulated annealing in quantum systems.