Book Image

Quantum Computing Algorithms

By : Barry Burd
5 (1)
Book Image

Quantum Computing Algorithms

5 (1)
By: Barry Burd

Overview of this book

Navigate the quantum computing spectrum with this book, bridging the gap between abstract, math-heavy texts and math-avoidant beginner guides. Unlike intermediate-level books that often leave gaps in comprehension, this all-encompassing guide offers the missing links you need to truly understand the subject. Balancing intuition and rigor, this book empowers you to become a master of quantum algorithms. No longer confined to canned examples, you'll acquire the skills necessary to craft your own quantum code. Quantum Computing Algorithms is organized into four sections to build your expertise progressively. The first section lays the foundation with essential quantum concepts, ensuring that you grasp qubits, their representation, and their transformations. Moving to quantum algorithms, the second section focuses on pivotal algorithms — specifically, quantum key distribution and teleportation. The third section demonstrates the transformative power of algorithms that outpace classical computation and makes way for the fourth section, helping you to expand your horizons by exploring alternative quantum computing models. By the end of this book, quantum algorithms will cease to be mystifying as you make this knowledge your asset and enter a new era of computation, where you have the power to shape the code of reality.
Table of Contents (19 chapters)
Free Chapter
Part 1 Nuts and Bolts
Part 2 Making Qubits Work for You
Part 3 Quantum Computing Algorithms
Part 4 Beyond Gate-Based Quantum Computing

Quantum annealing

With rising concerns about climate change, it’s useful to think about the way temperature varies by geographic location. Figure 10.1 has a fictional map of temperatures in a particular region.

Figure 10.1 – A temperature map

Figure 10.1 – A temperature map

Our goal is to find the coldest spot on this map. One strategy would be to start at an arbitrary point and work our way to points of ever-decreasing temperature. This is called a greedy method because the algorithm always moves in a direction that yields a better value.

The arrow in Figure 10.1 shows a possible path using the greedy approach. The arrow starts at the top of the map, moves downward until it hits the center of the northernmost region, and then stops. The arrow stops because, having reached this point, movement in any direction would raise the temperature. The algorithm has found a local extreme point without ever getting near the coldest spot on the map.

Annealing is a way of moving slowly toward and...