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 neural nets

Your brain is made up of approximately 200 billion cells, of which about half are neurons. Figure 10.3 illustrates an interaction between two neurons.

Figure 10.3 – Communicating neurons

Figure 10.3 – Communicating neurons

A neuron communicates with a neighboring neuron by sending a chemical substance (a neurotransmitter) out of its axon. The neurotransmitter leaps across a synapse – a little gap between the sending and receiving neuron. On the other side of the synapse, a dendrite receives the neurotransmitter and then forwards a signal to the receiving neuron’s soma. Receipt of this signal may cause an electrical spike inside the receiving neuron. If the receiving neuron spikes, it sends a signal along its own axon, and the process continues.

In 1943, researchers named McCulloch and Pitts [1] described an electrical device whose behavior modeled that of a neuron. An artificial neuron has variable weights, as shown in Figure 10.4.

Figure 10.4 – An artificial neuron