D-Wave Systems has designed processors based on a scalable architecture that aim to implement quantum annealing, an algorithm that can be used to solve a wide variety of optimization problems. A minimal requirement for a device to perform quantum annealing is that it maintains coherence throughout an appreciable fraction of the annealing protocol. In reality, any quantum annealer will be subject to noise, which leads to decoherence in some form.

D-Wave’s quantum computer is good at solving a specific type of problems known as Ising spin problems. However, in order to solve one of these spin problems, you must first solve another hard problem—embedding the spin problem on D-Wave’s quantum processor.
From the land of discrete mathematics, this embedding problem falls into a well studied branch of graph theory known as graph minors. Being that this problem is difficult in and of itself, D-Wave has developed a heuristic solution. This project’s main aim is to help improve this embedding process.

D-Wave systems purports to have designed a quantum processor based on scalable architecture that physically implements quantum annealing, an algorithm that can be used to solve a wide variety of optimization problems. In order for D-Wave devices to exhibit a performance advantage over classical processors, it is necessary that the devices utilize a resource that is inaccessible to any classical algorithm. This resource is generally associated with quantum entanglement.

Optimization problems, such as finding the shortest or fastest path to a destination are ubiquitous in industry. Hower, for some industrial applications it may be desirable to have a set of few diverse, yet nearly optimal solutions. The goal of this project is to create new optimization problem solvers that focus on both quality and diversity of the solutions proposed. These solvers will subsequently be used to assess the performance of the D-Wave quantum annealer processor.

D-Wave Systems develops and manufactures quantum annealing processors. These processors implement a model of quantum computation that seeks to solve hard problems by exploiting quantum effects such as tunneling and superposition. The aim of this project is to study and improve the performance of quantum annealing processors by mitigating inherent and implementation-dependent failure mechanisms for near-term quantum annealing devices.

Quantum computers are believed to offer significant advantages over classical computers, specifically in solving non-deterministic polynomially hard problems. One of the known schemes of quantum computation is quantum annealing, which is suitable for solving many types of hard optimization problems with a wide range of applications including machine learning, finance, security, and healthcare. D-Wave Systems Inc. develops the only commercially available quantum annealers, which are being successfully applied to solve certain types of problems.

Quantum computers are believed to offer significant advantages over classical computers, specifically in solving non-deterministic polynomially hard problems. One of the known schemes of quantum computation is quantum annealing, which is suitable for solving many types of hard optimization problems with a wide range of applications including machine learning, finance, security, and healthcare. D-Wave Systems Inc. develops the only commercially available quantum annealers, which are being successfully applied to solve certain types of problems.

As computer chips approach the nanometer-scale size, it is becoming increasingly clear that the next revolution in computing technologies will be enabled by quantum computing. A pioneer in quantum technologies aimed at quantum computing, the Canadian company D-wave Systems has developed quantum annealing processors consisting of superconducting circuit that can be used as efficient devices for solving high-dimensional optimization or sampling problems. The same problems can be solved using conventional computers and it is not yet clear if quantum annealers offer quantum speedup.

Current and near-future D-Wave processors implement optimization using quantum effects on a fixed processor layout. This layout can be modeled as a graph with certain constraints, and the optimization is applied to a specific problem: fixed-topology Ising spin optimization. It is therefore important to study how best to use this hardware to solve combinatorial optimization problems in various forms, and to study performance versus conventional optimizers. The intern will study problems in this area, as well as the problem of predicting performance of proposed processor topologies.

D-Wave Systems is currently involved in the research and development of quantum computing technology. Quantum computers allow for not just a fast computer, but potentially, a change in the computational complexity of problems. Graph theory plays a crucial role in the development and understanding of the capabilities and behaviour of the quantum computing hardware being developed, both from an operational and an applications perspective.