This chapter focuses on the application of Nb Josephson junctions (JJs) in quantum annealing (QA). It begins with a brief introduction to diffident strategies for implementing quantum computation (QC) with an emphasis on quantum annealing. The chapter presents the basic physics of the simplest superconducting flux qubit: radio frequency superconducting quantum interference devices (RF-SQUID). It gives a brief description on the design and realization of couplers, and control and measurement circuitry using Nb Josephson junctions. The chapter describes a scalable architecture upon which quantum annealing processors consisting up to 1024 superconducting flux qubits made of more than 104 Nb Josephson junctions. It discusses evidence on whether D-Wave 2X, which is the latest quantum annealing processor available, works as expected. The chapter also focuses on the physics and application of RF-SQUID flux qubits because the fundamental building blocks of the most advanced quantum annealers demonstrated so far are variations of superconducting flux qubits with one Josephson junction, a.k.a. the RF-SQUID.
Commercial quantum annealing (QA) machines are now being built with hundreds of quantum bits (qubits). These are used as analog computers, to solve optimization problems by annealing to an unknown ground state (the solution), given the Hamiltonian for that problem. We propose and develop a new approach, in which we use machine learning to do the inverse problem: to find the Hamiltonian that will produce a given, desired ground state. We demonstrate successful learning to produce a desired fully entangled state for a two-qubit system, then bootstrap to do the same for three, four, five and six qubits; the amount of additional learning necessary decreases. With these new capabilities the computing possibilities for QA arrays are greatly expanded.
The performance of open-system quantum annealing is adversely affected by thermal excitations out of the ground state. While the presence of energy gaps between the ground and excited states suppresses such excitations, error correction techniques are required to ensure full scalability of quantum annealing. Quantum annealing correction (QAC) is a method that aims to improve the performance of quantum annealers when control over only the problem (final) Hamiltonian is possible, along with decoding. Building on our earlier work [S. Matsuura et al., Phys. Rev. Lett. 116, 220501 (2016)], we study QAC using analytical tools of statistical physics by considering the effects of temperature and a transverse field on the penalty qubits in the ferromagnetic
p-body infinite-range transverse-field Ising model. We analyze the effect of QAC on second (
p=2) and first ($p\ensuremath{\ge}3$) order phase transitions, and construct the phase diagram as a function of temperature and penalty strength. Our analysis reveals that for sufficiently low temperatures and in the absence of a transverse field on the penalty qubit, QAC breaks up a single, large free-energy barrier into multiple smaller ones. We find theoretical evidence for an optimal penalty strength in the case of a transverse field on the penalty qubit, a feature observed in QAC experiments. Our results provide further compelling evidence that QAC provides an advantage over unencoded quantum annealing.