Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization

Many artificial intelligence (AI) problems naturally map to NP-hard optimization problems. This has the interesting consequence that enabling human-level capability in machines often requires systems that can handle formally intractable problems. This issue can sometimes (but possibly not always) be resolved by building special-purpose heuristic algorithms, tailored to the problem in question. Because of the continued difficulties in automating certain tasks that are natural for humans, there remains a strong motivation for AI researchers to investigate and apply new algorithms and techniques to hard AI problems. Recently a novel class of relevant algorithms that require quantum mechanical hardware have been proposed. These algorithms, referred to as quantum adiabatic algorithms, represent a new approach to designing both complete and heuristic solvers for NP-hard optimization problems. In this work we describe how to formulate image recognition, which is a canonical NP-hard AI problem, as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The QUBO format corresponds to the input format required for D-Wave superconducting adiabatic quantum computing (AQC) processors.Comment: 7 pages, 3 figure

Finding Low-Energy Conformations of Lattice Protein Models by Quantum Annealing

Lattice protein folding models are a cornerstone of computational biophysics. Although these models are a coarse grained representation, they provide useful insight into the energy landscape of natural proteins. Finding low-energy threedimensional structures is an intractable problem even in the simplest model, the Hydrophobic-Polar (HP) model. Description of protein-like properties are more accurately described by generalized models, such as the one proposed by Miyazawa and Jernigan (MJ), which explicitly take into account the unique interactions among all 20 amino acids. There is theoretical and experimental evidence of the advantage of solving classical optimization problems using quantum annealing over its classical analogue (simulated annealing). In this report, we present a benchmark implementation of quantum annealing for lattice protein folding problems (six different experiments up to 81 superconducting quantum bits). This first implementation of a biophysical problem paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.Chemistry and Chemical Biolog

On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models

In this report, we explore the use of a quantum optimization algorithm for obtaining low energy conformations of protein models. We discuss mappings between protein models and optimization variables, which are in turn mapped to a system of coupled quantum bits. General strategies are given for constructing Hamiltonians to be used to solve optimization problems of physical/chemical/biological interest via quantum computation by adiabatic evolution. As an example, we implement the Hamiltonian corresponding to the Hydrophobic-Polar (HP) model for protein folding. Furthermore, we present an approach to reduce the resulting Hamiltonian to two-body terms gearing towards an experimental realization.Comment: 35 pages, 8 figure

Building an organization that can build a quantum computer

D-Wave — Quantum computation is based on a very compelling idea: that physics, and physics alone, ultimately determines what can be computed, and how efficiently. Changing the laws of physics relevant for a computing device can open up new possibilities for manipulating information, allowing better algorithms that could transform the way we live. Quantum computation has, up until very recently, been the province of basic research. It is clear that the extreme difficulty and complexity of converting this basic science into useful technology cannot occur within a basic research environment. Here I will describe the conceptual framework behind D-Wave’s organization and technology development model, and compare and contrast this approach to other possible models.Non UBCUnreviewedOthe

AC relaxation in the Fe8 molecular magnet

We investigate the low energy magnetic relaxation characteristics of the "iron eight" (Fe8) molecular magnet. Each molecule in this material contains a cluster of eight Fe³+ ions surrounded by organic ligands. The molecules arrange themselves into a regular lattice with triclinic symmetry. At sufficiently low energies, the electronic spins of the Fe³+ ions lock together into a "quantum rotator" with spin S = 10. We derive a low energy effective Hamiltonian for this system, valid for temperatures less than Tc ~ 360 mK, where Tc is the temperature at which the Fe8 system crosses over into a "quantum regime" where relaxation characteristics become temperature independent. We show that in this regime the dominant environmental coupling is to the environmental spin bath in the molecule. We show how to explicitly calculate these couplings, given crystallographic information about the molecule, and do this for Fe8- We use this information to calculate the linewidth, topological decoherence and orthogonality blocking parameters. All of these quantities are shown to exhibit an isotope effect. We demonstrate that orthogonality blocking in Fe8 is significant and suppresses coherent tunneling. We then use our low energy effective Hamiltonian to calculate the single-molecule relaxation rate in the presence of an external magnetic field with both AC and DC components by solving the Landau-Zener problem in the presence of a nuclear spin bath. Both sawtooth and sinusoidal AC fields are analyzed. This single-molecule relaxation rate is then used as input into a master equation in order to take into account the many-molecule nature of the full system. Our results are then compared to quantum regime relaxation experiments performed on the Fe8 system.Science, Faculty ofPhysics and Astronomy, Department ofGraduat