Skip to content

Mad Scientist Laboratory

… Exploring the Operational Environment

  • Home
  • About
  • Mad Scientist APAN
  • The Convergence podcast
  • TRADOC G-2 Multimedia
  • Guest Bloggers
  • Contact
  • Disclaimer
  • Privacy
  • Terms Of Use

Tag: Quantum Many Worlds

Posted on January 14, 2019January 11, 2019

112. A Strategy for Everything: Quantum Artificial Intelligence, Quantum Multiverse, and Feedback Loop of Finite Information

[Editor’s Note: Mad Scientist Laboratory is pleased to publish the following post by repeat guest blogger Mr. Victor R. Morris, addressing the relationship of Artificial Intelligence (AI), Robotic and Autonomous systems (RAS), and Quantum Information Science (QIS) to Quantum Artificial Intelligence (QAI), and why we should pursue a parallel QAI strategy in order to predict alternative possibilities in a quantum multiverse.  Prepare to have your consciousness expanded — Read on!  (Note:  Some of the embedded links in this post are best accessed using non-DoD networks.)]

Introduction
The U.S. defense industry routinely analyzes emerging and potentially disruptive technological trends influencing long-term strategic competition. This post describes the greater defense community as public and private sectors responsible for national security and associated interests abroad. Interstate competition has implications for global order and disorder, according to the 2018 National Defense Strategy summary.

The three defense industry trends identified in this post are:

  • Artificial Intelligence (AI),
    • Robotic and Autonomous systems (RAS), and
      • Quantum Information Science (QIS).

According to Paul Scharre‘s preface to Elsa Kania‘s paper on Battlefield Singularity, published by the Center for a New American Security (CNAS), “Artificial intelligence (AI) is fast heating up as a key area of strategic competition.” (N.B., both Mr. Scharre and Ms. Kania are proclaimed Mad Scientists whose works have previously graced this blog site). Furthermore, structured analysis identified interrelated aspects of these trends and the requirement for a multi-disciplinary strategy focused on Quantum Artificial Intelligence (QAI), anticipating the potential impact on global systems.

A number of countries have published AI Strategies during the past 21 months. China is routinely assessed as a leader in AI development and investment. Russia is finalizing its draft National AI Roadmap; the final version will be released mid-year 2019.  The U.S. is scheduled to release its national strategy for artificial intelligence in early 2019. This intentionally or unintentionally follows the National Strategic Overview for Quantum Information Science released in September 2018. Additionally, around the same time period, both the Massachusetts Institute of Technology (MIT) and U.S. Defense Advanced Research Projects Agency (DARPA) announced billion dollar investment plans for AI research and technologies. The U.S. Army’s Robotic and Autonomous Systems Strategy publication coincided with Canada’s AI strategy in March 2017.  Canada was the first country to announce an AI strategy and future investment in the AI ecosystem. The European Union followed up on recent strategic developments by announcing a plan fostering AI development and employment last month.

First, this post argues that AI, QIS, and RAS are components of a greater QAI ecosystem underpinned by the scientific notion of information discussed in detail later.  Information does not measure what is known, rather it measures the number of possible alternatives for something. Combining AI and quantum computing applications potentially results in QAI, according to a variety of scientists and theorists in the field. Additionally, information is the nucleus or “quanta” of the entire QAI ecosystem. Understanding information is critical to understanding the natural world. Secondly, the post argues “keeping up with the Joneses” in AI is counterproductive and perpetuates misunderstanding of advancements and implications for the future.

The first section of this post briefly describes AI, Machine Learning (ML), RAS, QIS, and QAI, and their relationships with information. The second section describes theoretical interpretations of reality based on quantum mechanical properties.

Section 1 Overview

AI, sometimes called machine intelligence, includes the machine learning field enabling autonomous or independent functions and activity. QIS and computing are the next evolution of classical computing with implications for machine learning, reasoning, and autonomous systems behavior. As mentioned above, information is a fundamental consideration for all of these fields and the ability to perform parallel probabilistic tasks. “Probabilistic” refers to probabilities indirectly associated with randomness.

Artificial Intelligence (AI) and Machine Learning (ML)

AI involves computer systems performing tasks normally requiring human intelligence. In computer science, AI is the study of intelligent agents or autonomous entities perceiving and acting upon their environment. AI is intelligence exhibited by machines, enabled by machine learning algorithms in simpler terms. Algorithms are rule sets defining sequences of operations. ML is a field of AI and set of statistical techniques associated with machines performing intellectual, human tasks. ML includes deep learning and is critical to AI because it involves Artificial Neural Networks (ANN) like the human brain, enabling learning from large quantities of data to improve predictions and data driven decisions. ANNs are a framework for ML algorithms working together to process complex data sets.

Robotic and Autonomous Systems (RAS)                                                                Robots are one type of AI entity, while others include cyber agents, decision aids, and virtual assistants. Amazon’s Alexa and Apple’s Siri are good examples of AI-enabled virtual assistants using ML to perform tasks.  RAS are technologies granted autonomy or level of independence to execute tasks in a prescribed environment in a military context. RAS examples include both land and air systems like explosive ordnance disposal robots and unmanned aerial vehicles commonly referred to as “drones.” Autonomous behavior is designed by humans through a combination of sensors and advanced computing processes. Advanced computing involves both environmental navigation and software enabled decision-making. RAS independence is a progressive spectrum, ranging from remote control to full autonomy.

Quantum Information Science (QIS)
According to the September 2018 United States Government’s National Strategic Overview for Quantum Information Science report, “Quantum information science (QIS) applies the best understanding of the sub-atomic world—quantum theory—to generate new knowledge and technologies.” Quantum theory, also called quantum mechanics, describes the smallest finite quantities, or “quanta,” making up the quantum fields composing the universe. QIS includes the quantum computing field using quantum mechanical properties to advance information processing, transmission, and measurement.For example, quantum computation uses the quantum analog of a bit, called a quantum bit, existing in multiple states due to quantum superposition. Superposition allows quantum systems the ability to simultaneously occupy different quantum states. This fundamental principle means qubits are described as a linear combination of 0 and 1 (composition of basis states), and not solely 0 or 1 as in classical computing before measurement.

Quantum Artificial Intelligence (QAI)
This section does not attempt to explain AI and QIS intersections in detail. Both areas are so extensive that unifying concepts are difficult to understand. This post sees QAI as a different element of the taxonomy and not a subset of classical AI. “Quantum physics is based on information theory and probability theory” according to Andreas Wichert, author of Principles of Quantum Artificial Intelligence. He presents both theories in his book, highlighting quantum physics’ relationship to AI through associative memory and Bayesian networks. Associate memory and Bayesian networks are applied later to QAI based on their access to information.

Section 2 Overview
This section outlines interpretations of information and quantum theories and AI intersections. Information is a finite measurement of possible alternatives existing in the multiverse. Quantum computing has the potential for reversible or time-invertible deep learning and associative memory based on quantum entanglement and superposition. Quantum AI has the potential to test the multiverse theory, because QAI networks process, transmit, and measure information across space-time.

Information Theory
Information takes many forms that differ from one another, like natural language, symbols, acoustic speech, and pictures. The scientific notion of information is more precise. Information theory, proposed by Claude E. Shannon, studies the quantification, storage, and communication of information. Once again, Information does not measure what is known, it measures the number of possible alternatives for something.

Carlo Rovelli uses a dice example in his book Reality Is Not What It Seems: The Journey to Quantum Gravity to illustrate this point. If a dice is thrown, it can land on one of six sides. When we observe it fall on a number, we have an amount of information where N=6 because the possible alternatives are six. Instead of “N” (number of alternatives), scientists measure information in terms of quantity deemed “S” after Shannon. Rovelli also states information is finite in nature based on quantum mechanical properties. New or “relevant” information cancels out “irrelevant” information in a physical system, therefore systems can always obtain new information from other systems. *This point is important for later.

Measuring Possible Alternatives
The fundamental unit of classical information is a “bit.” The natural unit of information, or “nat,” is a unit of information or entropy. Information entropy is the average rate information is produced by a random source of data. Information entropy can be measured in bits, nats, or decimal digits, depending on the base logarithm defining it. Once again, a binary digit, characterized as 0 and 1, represents information in classical computing. A quantum bit, or “qubit,” is the basic unit of quantum information in the quantum world. A qubit can be a coherent superposition of both 0 and 1 eigenstates according to quantum mechanical properties. A qubit can also hold more information than a classical bit. Lastly, probability amplitudes are complex numbers. They are the probability of a qubit to appear in its basis states.

Quantum Machine Learning through Quantum Information
Quantum ANNs potentially enable deep learning from large quantities of qubits. Qubits are information, so they measure possible alternatives. Quantum ANNs are like Bayesian networks graphically modelling probabilistic relationships in this specific interpretation. The quantum nature of these networks expand access to reciprocal or correlated information.

An interpretation of reciprocal information is discussed through quantum mechanical properties and quantum many-worlds, also called “multiverse” theory in the last part of this post. This specific interpretation is multiverses are finite because information is. This is loosely based on Steven Hawking and Thomas Hertog’s April 2018 article, A smooth exit from internal inflation? where they state, “eternal inflation does not produce an infinite fractal-like multiverse, but is finite and reasonably smooth.”

Quantum Many Worlds
Quantum computing has the potential to allow reversible or time-invertible deep learning and associative memory, based on quantum entanglement and superposition. Qubits contain entangled relevant and irrelevant (anti-correlated probabilities) information across space-time. This concept ensures a retro-causality loop of finite information exchange. Quantum associative memory is the ability to learn and remember correlations between seemingly unrelated items. This is possible because all “items” are correlated through quantum phenomena. Relevant information in one world or universe (macro possible alternative) is simultaneously irrelevant information in the adjacent world because of quantum states and finite quantity of information in nature. Quantum information cannot be copied according to the no-cloning theorem. Conversely, it cannot be deleted based on a time reversed dual called the “no-deleting theorem.”

Information is the quanta of consciousness. It is a measurement of awareness following all possible trajectories through the quantum multiverse ensuring the feedback loop of finite information that is reality.

This specific interpretation is based on Hugh Everett’s relative state or many-worlds interpretation (MWI) and information reality code concept. MWI states “all possible alternate histories and futures are real, each representing an actual world” or universe. The reality code behaves similarly to classical coding. Coding theory is the application of information theory manifesting efficient and reliable data transmission in a non-deterministic manner (where meaning is relative). Information in a data set is characterized by its Shannon entropy.

Summary of Key Points (You made it!)
• The QAI ecosystem is underpinned by the scientific notion of information
• Information does not measure what is known, it measures the number of possible alternatives for something
• Relevant information cancels out irrelevant information in a physical system, therefore systems can always obtain new information from other systems
• A qubit can be a coherent superposition of both 0 and 1 eigenstates, according to quantum mechanical properties
• Qubits contain entangled relevant and irrelevant information across the multiverse
• MWI states all possible alternate histories and futures are real, each representing an “actual world” or universe
• Multiverses are finite because information is
• Information is the quanta of consciousness and measurement of awareness

What’s Next?
One interpretation of AI is whoever becomes the leader in this sphere will become the ruler of the world. This is one possible alternative for QAI. Another possible alternative is the validation the many-worlds theory, providing insight into observable world alternate histories and optimized futures because information is available to QAI agent networks. The predictive nature of classical AI to support global superpower decision-making may not happen as planned either. Predictions in the observable world exist in other worlds, so AI predicting the observable future is relative. For example, when a dice lands on the number 1 in the observable world, it lands on the other five alternatives in alternate worlds.  Additionally, unknown events in the observable world are known elsewhere in the quantum multiverse and vice versa (alternate histories and futures). Physicist David Deutsch, a proponent of the MWI, believes MWI will be testable through quantum computing. Based on this blog’s conjecture, developing a parallel QAI strategy is the first step in preparing for our changing understanding of the world.

If you enjoyed this mind-bending post, please see Mr. Morris’ previous guest blog posts:

  • Engaging Human-Machine Networks for Cross-domain Effects
  • Quanta of Competition

And read Ms. Kania’s Quantum Surprise on the Battlefield?

Victor R. Morris is a civilian irregular warfare and threat mitigation instructor at the Joint Multinational Readiness Center (JMRC) in Germany.

HOT!!!

Explore the latest TRADOC G-2 OE content on the Operational Environment Enterprise

Download and check out our new OE Assessment — TRADOC Pam 525-92, The Operational Environment 2024-2034: Large-Scale Combat Operations

Learn more about how our Pacing Threat fights in ATP 7-100.3, Chinese Tactics

Click here to read the Army Future Command’s new AFC Pamphlet 525-2, Future Operational Environment: Forging the Future in an Uncertain World 2035-2050 addressing the mid- to far term OE and watch the associated video!

Watch the TRADOC G-2’s Threats to 2030 video addressing the challenges facing the U.S. Army in the current OE (i.e., now to 2030)

PRIMERS ON THE OPERATIONAL ENVIRONMENT

The Changing Character of Future Warfare video

Potential Game Changers handout

TP 525-92-1, The Changing Character of Warfare: The Urban Operational Environment

The Arctic Through 2035: An Overview of the Operational Environment and Competitor Strategies for U.S. Army Training, Doctrine, and Capabilities Development

The Information Environment: Competition & Conflict anthology

MORE OPERATIONAL ENVIRONMENT RESOURCES…

Foreign Military Studies Office OE Watch

Watch The Future of Unmanned Maritime Systems Webinar [via a non-DoD network]

“THE CONVERGENCE” — Army Mad Scientist Podcasts

Former Undersecretary of the Navy James F. “Hondo” Geurts and Dr. Zachary S. Davis, Senior Fellow, Center for Global Security Research, Lawrence Livermore National Laboratory, discuss Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Force and how to think radically about the future, capitalize on talent, and unleash technological convergences to out-compete our adversaries, and when necessary, defeat them decisively in conflict.

COL John Antal (USA-Ret.), author and innovator in the interactive gaming and learning industry, discusses the implications of the Second Nagorno-Karabakh Conflict, the psychological effects of drone warfare, and the future of maneuver.

COL Scott Shaw, Commander, U.S. Army Asymmetric Warfare Group, discusses the future of ground warfare, including lessons learned from the Nagorno-Karabakh Conflict in 2020 and the realities of combat for tomorrow’s Soldiers.

Dr. David Kilcullen, bestselling author and expert on unconventional warfare, discusses how the U.S. Army must prepare to engage “Dragons” and “Snakes” as they employ Liminal Warfare and blended cyber-kinetic operations to avoid our conventional warfare dominance.

Mr. Doowan Lee, CEO and co-founder of VAST-OSINT, discusses disinformation, changes over time in approaches to information warfare, and collaboration between Russia and the Chinese Communist Party on information operations.

Click here to listen to other episodes of “The Convergence”

Mad Scientist Partner Sites

Modern War Institute Podcasts

Former Deputy Defense Secretary Robert Work Assesses the Future Battlefield

Dr. Moriba Jah on What Does the Future Hold for the US Military in Space?

Elsa Kania on China and its Pursuit of Enhanced Military Technology

Small Wars Journal Mad Scientist Page

U.S. Army War College War Room

U.S. Army Intelligence Center of Excellence Military Intelligence Professional Bulletin

Australian Army The Cove

Australian Defence College The Forge

Search

ARCHIVES

  • July 2025
  • June 2025
  • May 2025
  • April 2025
  • March 2025
  • February 2025
  • January 2025
  • December 2024
  • November 2024
  • October 2024
  • September 2024
  • August 2024
  • July 2024
  • June 2024
  • May 2024
  • April 2024
  • March 2024
  • February 2024
  • January 2024
  • December 2023
  • November 2023
  • October 2023
  • September 2023
  • August 2023
  • July 2023
  • June 2023
  • May 2023
  • April 2023
  • March 2023
  • February 2023
  • January 2023
  • December 2022
  • November 2022
  • October 2022
  • September 2022
  • August 2022
  • July 2022
  • June 2022
  • May 2022
  • April 2022
  • March 2022
  • February 2022
  • January 2022
  • December 2021
  • November 2021
  • October 2021
  • September 2021
  • August 2021
  • July 2021
  • June 2021
  • May 2021
  • April 2021
  • March 2021
  • February 2021
  • January 2021
  • December 2020
  • November 2020
  • October 2020
  • September 2020
  • August 2020
  • July 2020
  • June 2020
  • May 2020
  • April 2020
  • March 2020
  • February 2020
  • January 2020
  • December 2019
  • November 2019
  • October 2019
  • September 2019
  • August 2019
  • July 2019
  • June 2019
  • May 2019
  • April 2019
  • March 2019
  • February 2019
  • January 2019
  • December 2018
  • November 2018
  • October 2018
  • September 2018
  • August 2018
  • July 2018
  • June 2018
  • May 2018
  • April 2018
  • March 2018
  • February 2018
  • January 2018
  • December 2017
  • November 2017

Content has been removed to align with the President’s executive orders and DoD priorities in accordance with DoD Instruction 5400.17, “Official Use of Social Media for Public Affairs.”

  • Twitter
Proudly powered by WordPress