Event Details

Quantum Field Lens Coding: A New Way to Predict Particle States and System Events (part 1)

Presenter: Philip Baback Alipour
Supervisor:

Date: Tue, March 31, 2026
Time: 09:15:00 - 00:00:00
Place: Zoom - see below.

ABSTRACT

Zoom Link: https://uvic.zoom.us/j/89140535483?pwd=6PJdbataQzENWRIadlua3RUskMzWfB.1

Meeting ID: 891 4053 5483

Passcode: 040612

 

Summary:
Imagine a specialized computer program stored on a specialized hardware acts like a powerful quantum lens. Just as a physical lens focuses light to see an object clearly, Quantum Field Lens Coding (QF-LC) is a new framework designed to focus and analyze the energy states of particles. The lens is a set of QF-LC algorithm steps that transform a Single Field of a particle state into a Quantum Double-Filed, projecting future state transitions (STs) as events with a success probability of ≥ 2/3, from a lower probability of 1/3 between 3 to N-entangled particles.  This technology helps scientists predict how systems—ranging from microscopic energy states to macroscopic states, bodies, biological cells, societies, environments, the globe, and the universe—will change, evolve, and reduce entropy to near 0, or near 100% certainty. 

Research Synthesis: From QF-LC Theory to Global Impact

This seminar focuses on the Quantum Double-Field (QDF) model developed within the speaker's PhD dissertation.

The Four Pillars of the QF-LC Project

1.      The QDF Model: A mathematical blueprint of quantum double fields.

2.      The What, the How, and the Key Effect/Output of the Project:

o   The What?  - The Algorithm (QF-LCA): Instructions (QF-LC algorithm steps) that allow quantum/hybrid computers to predict system state transitions (STs) as events.

o   The How? - QDF Circuit: A QDF circuit transforms a single field of a particle state into a QDF as a strong prediction, which is doubling the probability value of the single field (SF) within an ST or a phase transition (PT).

o   The Key Part of Technology:  Transform an SF into a more detailed QDF as a heat engine. 

3.      The Simulator (QF-LCS): Software that mimics a QDF system.

4.      The Dataset: A set of recorded data points used for algorithm training and state analysis of a system.

The Quantum Power-Up: Scalar k (Kappa) and Doubled Probability

Scalar k (Kappa): This acts as a multiplier, scaling the particle's quantum field during particle interactions.

·       Doubling the Probability: This field transformation significantly improves accuracy and gains greater certainty to the point of 100%. While a standard prediction can have a probability of about  ≥ 1/3 (lower threshold of an expected measurement outcome)  for a particle via an entangled particle (three in total, at least).

·       Entanglement from the QDF circuit:  This is developed by focusing the energy through the quantum lenses and qubit pairs which double the probability to 2/3 or higher.

·       Energy Efficiency: The system uses these high-probability predictions to suggest the most efficient energy paths for a user to choose, as an intelligent (or quantum AI) decision support system. This minimizes system entropy  near absolute zero temperatures. 

The Secret Sauce: The Three-Way Connection

To gather data, the system creates a unique information network using three particles:

1.      The Sample: A particle taken from the system being observed.

2.      The Partner: A "trapped" particle that pairs up (entangles) with the sample.

3.      The Decoder: A third particle that acts as a bridge, unlocking hidden information between the first two to maximize their connection (entanglement). 

This process effectively turns the quantum circuit into a "heat engine" that processes information to achieve a desired system  (Hamiltonian) outcome, known as the target state (TS) as real world applications listed below.

Real-World Applications

This research project has practical goals that extend beyond the lab (notable examples):

  • ​Medicine: Reconstructing damaged DNA to predict virus spread or cancer growth (a TS).
  • Security: Identifying forged documents via microscopic particle signatures (a TS).
  • ​Global Sustainability: Providing affordable clean energy by optimizing system efficiencies (a TS).

QF-LCA project origins as peer-reviewed by Software Impacts and Data in Brief Elsevier journals, can be accessed at https://github.com/SoftwareImpacts/SIMPAC-2024-159