The analog quantum advantage


In this module, we will explore how analog quantum computing can prove advantageous for solving complex optimization problems. From unpacking computational complexity theory to delving into the intricacies of combinatorial optimization, we explore how analog quantum computing can revolutionize problem-solving in diverse fields. We will also investigate multibody interactions, interaction graph connectivity, and the fundamentals of qudit computing, to uncover new strategies for tackling optimization challenges. Join us as we harness the power of quantum computing to redefine the boundaries of what's computationally possible.

Module Contents

  1. Complexity theory, P and NP

  2. Combinatorial optimization problems

  3. Ising models

  4. Multibody Interactions

  5. Interaction Graph Connectivity

  6. Qudit Basics

Module overview

1. Complexity theory, P and NP

Firstly, we will explore an introduction to complexity theory, which is fundamental to understanding the boundaries of efficient computation. The distinction between P and NP complexity classes, where P signifies problems solvable in polynomial time and NP encompasses those verifiable in polynomial time, is central to deciphering the theoretical landscape of algorithmic efficiency. While complexity theory provides invaluable insights, its application necessitates nuanced considerations.

2. Combinatorial optimization problems

Next, we will consider combinatorial optimization problems and the challenges they pose to traditional computational methods. Combinatorial optimization problems, exemplified by the traveling salesperson problem, entail a vast array of potential solutions that exponentially increase with the size of the problem. Herein lies the promise of hardware solvers, such as our entropy quantum computing devices, to efficiently navigate the intricate solution space, potentially outperforming conventional methods in finding practical solutions within realistic timeframes.

3. Ising models

An Ising model, originally developed for understanding magnetism, has found new applications in solving complex optimization problems. The Ising model represents systems of "spins" with values of +1 or -1, whose interactions can be mapped to NP-hard combinatorial optimization problems, providing a bridge between physics and computer science. Quadratic Unconstrained Binary Optimization (QUBO) models are similar to Ising models but use binary variables (0 or 1), and both models help understand the complexities and potential of quantum computing for optimization, despite the inherent difficulty of these problems.

3. Multibody Interactions

Next, we will delve into the significance of multibody interactions, particularly in the context of computational modeling and optimization problems. While such interactions are rare in traditional physics, they play a crucial role in computing, offering unique capabilities when directly implemented in hardware.

4. Interaction Graph Connectivity

Next, we will look into interaction graph connectivity in our quantum computing and optical devices, showcasing the flexibility of coupling light particles for diverse interactions. We will examine the challenge of mapping highly connected industrial optimization problems to hardware with limited connectivity, necessitating techniques like minor embedding. Graph theory, particularly treewidth and tree decomposition, emerges as a vital tool for understanding and addressing the limitations imposed by graph connectivity in problem mapping for some quantum systems, which can be avoided in optical systems.

5. Qudit Basics

Lastly, we will look into qudit basics, a concept that expands quantum computing beyond qubits, offering more than two possible states and leveraging superposition for versatile computation. Qudits can simulate qubit behavior mathematically and excel in handling integer and continuous problems, providing innovative solutions. In analog systems like entropy quantum computing systems, qudits undergo decoding to translate measurements into mathematical models, showcasing their potential for efficient and low-power computation across various domains.


This module has introduced how analog quantum computing can revolutionize optimization problem-solving. From unraveling the complexities of computational complexity theory to understanding the power of multibody interactions and interaction graph connectivity, we have gained insights into leveraging quantum algorithms for real-world challenges. By delving into qudit basics, we've discovered the potential for efficient and low-power computation across diverse domains. Up next, we recommend trying our hardware, with the Introduction to Dirac-3 module.