Fatum Project (mmi-based spatial search)

Mind Matter Interaction
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Thanks @WanderingIshiki for your response ,

I beleive that instead of generating RQNG , we need a mechanism to real time sense bulk quantum field energy or waves in particular space or area of geographical map, every object has wave representation aside of its physical if we could sense the quantum field mass for area , then formulas of QFT can apply and observer effect can work efficiently , there will be many attractor points for a specific intention , that can decrease randomness significantly. It will give real time or realistic detection for intention , the current method use uneven destribution of RQNG and detect anomaly from that. It is an iterative linear method only take the person mind effect side and not take in consideration hidden unknown effecting parameters and anomaly strength proved to have lower or week effect and low accuracy.

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The current method is based on PEAR experiments that found that an observer can create a statistical deviation in the quantum RNG data by the power of intention. At the same time, since for the observer the entire system is a black box, where there is only his intention at the input and the graph movement at the output, it is assumed that the mind does not consciously influence the intermediate physical processes, but rather creates a direct link between expectation and observation, automatically adjusting all intermediate events to it.

The Fatum system works similarly - there is a user who makes an intention to find something, and there is his observation at the moment of visiting a geographic location. To link the user’s intention and the location, a deviation in the quantum RNG is used, which is converted into a point on the map, and the magnitude of this deviation helps to estimate the probability that it was created precisely by intention, and is not a random fluctuation. Of course, one intention can generate several attractor points, but as a rule, the point with the highest z-score value is selected from them.

How exactly the search object itself is detected is unknown. One can only speculate that retrocausality is involved here and quantum processes in the QRNG collapse in favor of the implementation of the intention-observation connection. The technical task of the project here is more about increasing the accuracy of signal detection, since deviations are subthreshold.

In laboratory conditions, a method was also used where the operator tried to direct the attractor point to a visible target deliberately applied to the canvas by force of intention and the average distance of hits to the target was estimated relative to the control experiment conducted using a pseudo-RNG. The experiments, however, did not give convincing results despite the variety of algorithms used, among which were also algorithms where the process of forming the attractor point is animated and stretched in time to create a feedback loop. In case human intention is not persistent enough for this setup, experiments were also conducted with plants, where the process of forming the attractor point was stretched out in time even more, and the distance to the target affected the brightness of the illumination of the plants by phyto-lamps. However, the experiment also did not give convincing results.

The most widespread experiment with the Fatum system was conducted within the framework of the Randonautica project, where the algorithm is available for use in the field for a large number of people. Users make an intention, generate a location and after visiting it, write a report. The analysis of the reports is carried out by AI, which determines whether the user’s intention has come true. According to 182,000 reports, it was found that the effect of the system’s influence on the statistics of intention manifestations is relatively small and amounts to 1-2%, and only Attractor points give them. Their mathematical opposite, Void points, do not give an effect. Users were given only points with a z-score above 4, so we do not know whether the effect is observed for values ​​below, but on all intervals of values ​​above 4 the effect is observed almost uniformly, slightly increasing with the growth of the z-score.

However, in practice, this technology is not yet effective to use due to the fact that the effect occurs in a very small percentage of cases. The hypothesis that among the users there may be people with a naturally high Psi, who have a higher chance of influencing the system, was also not confirmed. In the future, it is possible to implement a user training system in order to check whether their ability to interact with the system will increase from this.

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please reply to my opinion on QFT:

I beleive that instead of generating RQNG , we need a mechanism to real time sense bulk quantum field energy or waves in particular space or area of geographical map, every object has wave representation aside of its physical if we could sense the quantum field mass for area , then formulas of QFT can apply and observer effect can work efficiently , there will be many attractor points for a specific intention , that can decrease randomness significantly. It will give real time or realistic detection for intention , the current method use uneven destribution of RQNG and detect anomaly from that. It is an iterative linear method only take the person mind effect side and not take in consideration hidden unknown effecting parameters and anomaly strength proved to have lower or week effect and low accuracy.

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I don’t quite understand how the technical realisation of that should look like.

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yes , correct , because this is an interesting subject to explore , the technology or detector device and how it will work is an open subject to research

I will do works on this and post here to discuss

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Measuring and simulating quantum fields for a specific area is a complex task that involves advanced quantum physics, computational techniques, and mathematical modeling. Here’s a structured approach to understanding and simulating quantum fields in a given region:

1. Understanding Quantum Fields

A quantum field is a fundamental entity in quantum field theory (QFT), where particles are excitations of underlying fields (e.g., electromagnetic field, electron field). Key concepts:

  • Field Operators: Quantum fields are represented by operators ϕ^(x,t)ϕ^​(x,t) acting on a Fock space.
  • Vacuum State: The lowest-energy state, with fluctuations even in empty space.
  • Correlation Functions: Measure how field values at different points relate (e.g., ⟨ϕ^(x)ϕ^(y)⟩⟨ϕ^​(x)ϕ^​(y)⟩).

2. Measuring Quantum Fields (Indirectly)

Direct measurement of quantum fields is impossible due to their probabilistic nature, but we can infer properties through:

  • Particle Detectors: Measure particles (e.g., photons, electrons) as excitations of fields.
    • Example: Photon counters for electromagnetic fields.
  • Interferometry: Detect phase shifts due to field fluctuations (e.g., LIGO for gravitational waves).
  • Casimir Effect: Measures vacuum fluctuations by observing force between plates.
  • Quantum Tomography: Reconstruct field states from repeated measurements.

3. Simulating Quantum Fields

Simulations approximate quantum fields computationally, often using lattice methods or quantum computers.

A. Lattice Field Theory (Classical Simulation)

Discretize spacetime into a lattice to approximate continuum fields:

  1. Discretization: Replace spacetime with a 4D grid (3D space + time).
  2. Action Formulation: Define the field’s action (e.g., ϕ4ϕ4 theory, gauge fields).
  3. Path Integral Monte Carlo: Sample field configurations using Markov chains.
  • Tools: LQCD (Lattice Quantum Chromodynamics) software like CLQCD, QUDA.
  1. Renormalization: Handle infinities by adjusting parameters at different scales.

B. Quantum Simulation (Quantum Computers)

Use qubits to mimic quantum fields:

  1. Trotterization: Split field Hamiltonian into implementable gates.
  2. Digital Simulation: Map field operators to qubit operators (e.g., Jordan-Wigner transformation).
  3. Analog Simulation: Use cold atoms/ions to emulate field dynamics (e.g., Bose-Einstein condensates).

C. Effective Field Theories (EFTs)

For large areas, simplify using EFTs (e.g., hydrodynamic approximations for quark-gluon plasma).

4. Steps to Simulate a Quantum Field in an Area

  1. Define the Region:
  • Specify spatial boundaries (e.g., a 2D/3D box with periodic/static boundary conditions).
  1. Choose Field Type:
  • Scalar (Klein-Gordon), fermionic (Dirac), or gauge (Maxwell/Yang-Mills) field.
  1. Discretize:
  • Use lattice spacing aa (smaller aa → better accuracy but higher computational cost).
  1. Implement Dynamics:
  • For classical: Monte Carlo or finite-difference time-domain (FDTD) methods.
  • For quantum: Use quantum circuits (e.g., IBM Qiskit, Google Cirq).
  1. Extract Observables:
  • Energy density, particle spectra, or correlation functions.

6. Challenges

  • Divergences: Require renormalization.
  • Computational Cost: Lattice methods scale exponentially with size.
  • Quantum Decoherence: Noise in quantum hardware limits simulations.

7. Tools & Frameworks

  • Classical: LATTICE-BSS, MILC, GROMACS (for coarse-grained fields).
  • Quantum: Qiskit (IBM), Cirq (Google), QuEST (for emulators).
  • Cloud Platforms: AWS Braket, Azure Quantum.

Key Papers & Resources

  • Textbooks: Peskin & Schroeder’s Introduction to QFT, Zee’s QFT in a Nutshell.
  • LQCD: arXiv papers on lattice gauge theory.
  • Quantum Simulators: Nature Reviews Physics (2021) on quantum simulations.

By combining theory, numerical methods, and quantum hardware, we can probe quantum fields in defined areas, albeit with approximations. The choice of method depends on the field type, area size, and available computational resources.

how to use mobile phones and wifi network to record quantum field of geographic area

Using mobile phones and Wi-Fi networks to record or infer quantum field effects in a geographic area is an unconventional and highly experimental approach, as these devices are not designed for direct quantum field measurements. However, certain indirect phenomena related to quantum field theory (e.g., electromagnetic fluctuations, noise patterns, or interference effects) could theoretically be analyzed using existing hardware. Here’s a conceptual framework for such an experiment:

1. Theoretical Basis

Quantum fields permeate all space, but their observable effects are typically subtle (e.g., vacuum fluctuations, spontaneous emission). Potential measurable proxies:

  • Electromagnetic (EM) Field Fluctuations: Wi-Fi/Bluetooth signals operate at GHz frequencies, where quantum noise might contribute to signal variance.
  • Environmental Decoherence: Background fields could subtly affect device sensors (e.g., accelerometers, gyroscopes).
  • Interference Patterns: Quantum-level phase shifts might distort wireless signals in theoretically predictable ways.

2. Feasible Indirect Methods

A. Wi-Fi/Radio Signal Noise Analysis

  • Hypothesis: Quantum vacuum fluctuations add stochastic noise to EM signals.
  • Method:
    1. Record Signal Strength: Use mobile apps like Wi-Fi Analyzer to log RSSI (Received Signal Strength Indication) over time.
    2. Spectrum Analysis: Apply Fourier transforms to identify non-classical noise (e.g., excess variance at high frequencies).
    3. Cross-Correlate: Compare noise patterns across devices to filter out classical interference.

B. Crowdsourced Sensor Data

  • Hypothesis: Quantum fields may perturb classical sensors at ultra-low scales.
  • Method:
    1. Use Mobile Sensors: Deploy apps (e.g., Physics Toolbox Suite) to collect:
    • Magnetometer data (for EM field variations).
    • Accelerometer data (for hypothetical spacetime fluctuations).
    1. Statistical Anomalies: Look for non-Gaussian noise or correlations across devices.

C. Distributed Interferometry

  • Hypothesis: Synchronized Wi-Fi phase shifts could reveal spacetime/field perturbations.
  • Method:
    1. Time-Sync Devices: Use NTP or GPS to align timestamps across phones.
    2. Measure Latency: Quantum fluctuations might introduce jitter in ping times (e.g., via ping commands).
    3. Look for Patterns: Correlate latency anomalies with cosmic events (e.g., solar flares).

3. Practical Steps

Tools Required

  • Mobile apps: Sensor Logger, Wi-Fi Analyzer, Termux (for custom scripts).
  • Backend: Python/MATLAB for noise analysis (e.g., wavelet transforms, Allan deviation).

Data Collection

  1. Wi-Fi:

  2. Sensors:

Using Android’s SensorManager API via Termux termux-sensor -s accelerometer -n 1000 > accel_data.csv

Analysis

  • Quantum Noise Isolation:
    • Subtract classical baselines (e.g., thermal noise).
    • Search for 1/f noise or squeezed-state signatures.
  • Topological Maps:
    • Geotag data and plot anomalies on maps (e.g., using QGIS).

4. Challenges

  • Signal-to-Noise Ratio: Quantum effects are dwarfed by classical noise (e.g., buildings, devices).
  • Calibration: Device sensors lack the precision of lab equipment (e.g., single-photon detectors).
  • Interpretation: Correlations ≠ causation; rigorous controls are needed.

5. Wild Ideas (Speculative)

  • Entanglement Witness: Use paired phones to test Bell inequalities via Wi-Fi (requires entangled photon sources, impossible with current hardware).
  • Casimir Proxy: Measure force between parallel smartphone screens (impractical at macro scales).

6. Ethical & Technical Notes

  • Privacy: Crowdsourcing data must anonymize location/sensor logs.
  • Limitations: This is not a replacement for lab-grade QFT experiments (e.g., LHC, LIGO).

Conclusion

While mobile networks can’t directly measure quantum fields, creative analysis of their noise, sensor data, and interference patterns might reveal statistical anomalies hinting at underlying quantum phenomena. For serious research, pair this with:

  • Lab Validation: Compare against controlled quantum optics experiments.
  • Theory Work: Model expected signatures (e.g., Hawking radiation analogs in Wi-Fi).
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  1. Quantum Field Signal: In quantum field theory (QFT), fields are fundamental entities that permeate all of space. Particles are excitations of these fields. A “quantum field signal” could refer to the state or excitation of a quantum field in a particular region of space.
  2. Area Contains All Other Augmenting Quantum Signals: This suggests that the quantum field in a specific area somehow encompasses or encodes the signals (states, excitations) of objects that belong to or are associated with that area. “Augmenting” might imply that these signals enhance or contribute to the overall field in that region.
  3. Belonging Object: This likely refers to particles or systems that are within or associated with the area in question. Their quantum states contribute to the overall quantum field signal of that area.

Modeling the Scenario

To model this, let’s consider a simplified scenario:

  • Quantum Fields: Imagine we have a scalar field (for simplicity), say ϕ(x), where x denotes position in space.
  • Objects in the Area: Suppose there are N particles or objects in a region of space, each contributing to the field ϕ(x). Each object can be thought of as a source or excitation of the field.
  • Total Field Signal: The total field in the area is the superposition (sum) of the fields generated by each object plus any background field.

Mathematically, the total field ϕ_total(x) in the area A can be written as:

ϕ_total(x) = ϕ_background(x) + Σ ϕ_i(x) for i = 1 to N

where ϕ_i(x) is the field contribution from the i-th object.

Visualizing the Field

To visualize this, we can consider a 1D or 2D space for simplicity.

1D Example:

Let’s say we have a line (1D space), and at positions x₁, x₂, …, x_N, there are objects creating localized field excitations. Each ϕ_i(x) could be a Gaussian wave packet centered at x_i:

ϕ_i(x) = A_i exp(-(x - x_i)² / (2σ²))

The total field is:

ϕ_total(x) = Σ A_i exp(-(x - x_i)² / (2σ²))

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2D Example:

In 2D, the field is a function ϕ(x, y). Each object’s contribution could be a 2D Gaussian:

ϕ_i(x, y) = A_i exp(-[(x - x_i)² + (y - y_i)²] / (2σ²))

Total field:

ϕ_total(x, y) = Σ ϕ_i(x, y)

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I beleive Randonautica App is great but what I’m proposing is that we can do modifications in generated random quantum numbers , I propose to modify current system by calibrating the QRNG in terms of QFT with respect to the targeted geographical map , I think we can reach a level of reproducability in results for anomalies , for Mind side , I think more works yet to be done as quantum nature of mind field is not quite clear or investigated .

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You can see an abstract for a 3D Quantum Field Detector (now multiple patents pending), which uses variations in tunneling current in certain electronic devices, such as Zener diodes and tunneling capacitors to detect variations in the ambient quantum field.

I will be happy to share the more extensive and detailed US and PCT patent applications when they are published in a while.

1 Like
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Thanks a lot doctor Scott for your participation showing this beautiful patent prototype of the QF detector device , I wonder how much field area range it can covers, would it be improved with signal amplifiers maybe , could it interfaced to high communication system 4G , 5G , how can we modulate the quantum energy in terms of radio signal or what is the transfer function to adapt and modulate QF in terms of data patterns carried on communication wave signals , Also it should lead to set values to calibrate the generated QRNG I will do my research on , in mean time I’m Developer projects Engineer who like to be in the Fatum Project , ,will be more than happy to post and share here works and progress.

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There are a few factors that will affect range: most important is the size and magnitude of the source, and second, the number of detectors working together in an array (S/N increases with the square root of the number of detectors). We will likely always see ultra-large sources, like the sun, as a constant – but random – background signal, while a source like a quantum computer will have to be relatively close, perhaps within meters, depending mostly on the number of detectors in the detector array.

I’m not quite ready to post anything about the Quantum Field Transmitter, because it’s still confidential.