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


Grover’s algorithm speeds up the search of an unordered list. We represent a list of size N with n qubits, where N = 2n. Eventually, when we measure the qubits, we see a combination of n bits. Each possible combination stands for an element in the list. Each step of Grover’s algorithm increases the probability that the measurement outcome represents the target of our search.

The optimal number of steps depends on the number of elements in our unordered list. Each step of Grover’s algorithm makes approximately the same number increase in the target combination’s amplitude. Since an amplitude is the square root of a probability, the optimal number of steps grows with {"mathml":"<math style=\"font-family:stix;font-size:16px;\" xmlns=\"\"><mstyle mathsize=\"16px\"><msqrt><mi>N</mi></msqrt></mstyle></math>"}. That’s better than a classical search, where the optimal number of steps grows with N.

Grover’s search can be useful when it’s easy to verify that a particular element is the search target but difficult to find the search target among all the choices. Problems...