QT-Computers

TIMELINE

This article takes place in the 24 & 26 centuries of Distant Worlds.

Article contributors: tesinormed

Quantatransistor computers, also known as quantum-transistorial hybrid computation (QTHC) systems, are the cornerstone of modern human technology. Their distinctive feature is the seamless integration of quantum qubit processors and transistor-based hardware, blending the computational strengths of both paradigms.

The dawn of mass quantum computing began in the early 22nd century, with pioneering collaborations between IBM and Google. At its core, quantum computation exploits powerful principles of quantum mechanics, where particles can simultaneously exist as waves and vice versa—a phenomenon radically distinct from the classical flow of electrons through traditional NPN/PNP silicon transistors.

Before the breakthrough of this era, the creation of a "perfect" qubit presented an almost insurmountable challenge. Quantum decoherence—the tendency of qubits to lose their quantum state when exposed to environmental noise—severely limited their utility. Scientists struggled for decades to isolate qubits sufficiently to maintain their integrity during calculations.

The turning point came with the creation of QSC-1 (Quantum Simulation Computer One)—a monumental achievement resulting from the combined efforts of IBM, Google, and Nvidia. The QSC-1 housed 1,000 isolated leptonic qubits capable of executing exponentially complex simulations. It represented humanity’s first significant foray into practical quantum computing.

In advanced computational environments QT-Computers (Quantum-Transistorial Computers) are designed to complement one another—harnessing both quantum probability manipulation and deterministic classical verification to solve complex problems. Let’s walk through how such synergy is realized using Grover’s Algorithm on a 20-qubit quantum computer.

This chapter is adapted version of 3Blue1Brown's video: "But what is quantum computing? (Grover's Algorithm)"

We are tasked with finding a secret key $\large \ket{K}$ required to complete a logic procedure. The quantum system estimates the solution lies within a search space of:

$N = 2^{20} = 1,048,576 \text{ possible keys}$

This search space is vast for classical brute-force methods, but Grover’s Algorithm allows a quantum speedup.

To conceptualize the quantum state space, consider a simplified model:

The secret key state is denoted $\large \ket{K}$

The initial superposition is aligned along vector $\large \ket{B}$

The state space is abstractly hyperdimensional, but we can visualize it as a 2D plane for clarity

The angle between $\large \ket{B}$ and $\large \ket{K}$ is approximately: $\large \theta \approx \frac{1}{\sqrt{N}}$

For ease of explanation, let’s now reduce the size of N to 100, purely for conceptual illustration.

Grover’s Process: Two-Step Rotation:

Oracle Step – Phase Inversion:

1. The quantum oracle checks the candidate state and flips its phase: $\large \ket{0101} \rightarrow -\ket{0101}$ , This flip doesn’t alter the probability yet—it simply reflects the amplitude across the origin in our plane.

Diffusion Operator – Inversion About the Mean:

2. The second transformation reflects the state vector around the average direction (mean vector), effectively rotating it by an angle $\large 2θ$ toward $\large \ket{K}$ .

This "flip-then-rotate" step is repeated multiple times. The number of optimal repetitions is approximately:

$R = \left\lfloor \frac{\pi}{4} \sqrt{N} \right\rfloor$

In our example with $\large N = 2^{20}$ , this yields:

$R = \left\lfloor \frac{\pi}{4} \cdot \sqrt{1,048,576} \right\rfloor = \left\lfloor \frac{\pi}{4} \cdot 1024 \right\rfloor \approx 804$

Imagine a circle representing all quantum states. Flipping the phase of the correct key inverts its axis on the circle. Then, rotating around the mean vector increases the component pointing in the direction of $\large \ket{K}$ . Each iteration "nudges" the state closer to $\large \ket{K}$ .

By the 804th iteration, the probability of measuring the key state reaches:

$P \approx \sin^2\left((2R+1)\theta\right) \approx 99.9999757\%$

At this stage, the quantum processor measures the state. Suppose it returns: $\large \ket{K} = \ket{885440}$

Due to the non-deterministic nature of quantum measurement, there's still a slight chance the result may be incorrect. This is where the classical component of the QT-Computer intervenes.

Using standard transistorial logic gates, the classical processor now verifies $\large \ket{K}$ by deterministically evaluating the function:

$f(\ket{K}) \overset{?}{=} 1$

If the output is True, the key is validated. If not, the quantum process restarts with updated probabilities. This hybrid approach combines quantum speedup with classical certainty, creating a system far more efficient than either mode operating alone.

History

By the late 22nd century, engineers and developers began exploring ways to merge quantum computation with classical transistor-based architectures. While quantum systems excelled in high-complexity calculations, such as simulating molecular dynamics or solving optimization problems, they were inefficient for tasks requiring logical, step-by-step processing or graphical rendering.

The proposed hybrid architecture skyrocketed in popularity, allowing quantum and transistor systems to work in tandem. Quantum cores would handle resource-intensive calculations, while transistor systems managed more straightforward, sequential operations. This hybrid design brought quantum computation to the masses, driving a wave of commercialization and accessibility.

Accompanying this revolution was the introduction of the Volex Open-Source Operating System (Volex OS), an OS written in the spirit of Unix-like OSes by visionary programmer Voles Sambre. Sambre unified quantum and transistor processing under a single kernel.

Simultaneously, new programming languages emerged to facilitate this integration:

Q-Assembly Language: A machine language designed to unify quantum computation across different qubit types, including leptonic and bosonic hardware.
Q-Language: Building on Q-Assembly, Q-Language introduced compatibility layers, enabling seamless translation of instructions between quantum and transistor architectures.

These innovations created a system where hybrid computers could leverage quantum processing for intensive calculations while relying on transistor cores for logical tasks, offering unprecedented efficiency and flexibility.

Use cases

Quantatransistor computers found their most groundbreaking applications in space exploration—particularly in managing Bridges, the interdimensional passages enabling illusion of faster-than-light on Gly's travel.

With the development of specialized software, human interaction with Bridges became intuitive, as complex scripts and terminal commands were abstracted under the Volex Kernel and StarOS. These interfaces streamlined operations and enhanced safety, integrating QTHC capabilities with deep-space navigation systems.

At the heart of these systems are quantum neural networks (QNNs), engineered to detect patterns in high-dimensional, non-Euclidean data. This includes deciphering the oscillations of Boreon fields, which govern the stability of Bridges. By employing a quantum Fourier transform, QNNs analyze the wave-like properties of these fields, translating them into actionable configurations for interdimensional travel.

Computational process

Preprocessing: Classical channels handle preliminary data—such as coordinates, environmental conditions, and travel requirements.
Quantum engagement: The quantum core activates, leveraging QNNs to propose optimal configurations.
Field analysis: Using the Quantum Fourier Transform, the system deciphers Boreon field oscillations, ensuring the stability of fractal-like geometries within the Bridge.
Solution synthesis: Parallel quantum calculations sift through potential configurations, identifying the most efficient and stable solution.

$\mathcal{S}_{\text{Bridge}} = \int d^5x \sqrt{-g} \left[ \frac{1}{2} R - \Lambda - \frac{1}{2} \left(\alpha_F |\nabla \phi_F|^2 + \alpha_B |\nabla \phi_B|^2 \right) + \beta \sum_{n} \frac{|\phi_{F,n}|^2}{\left(1 + r^2 \right)^{n}} + \gamma \epsilon^{\mu \nu \rho \sigma \tau} \partial_\mu A_\nu \partial_\rho B_{\sigma \tau} + \delta_{FB} \phi_F \phi_B + \eta_{GG} h_{\mu \nu} h^{\mu \nu} + \zeta \mathcal{A}_{\text{Boreon}} \right]$

Galactic Numerical Designation System

The Galactic Numerical Designation System is inspired by the IPv6 Galactic Network Technology, created to provide a Volex kernel machine-understandable coordinate system for galaxies and galaxy-type objects. This system can be used for precise locations within galaxies or for categorizing galaxies accessed via Bridges.

Format

$\text{TYPE:}S_r\text{:}S_\theta\text{:}S_\phi$

For $\large \text{TYPE}$ :

LG: Local Group (Close proximity to the Milky Way)
BG: Bridge Group (Galaxy accessed via Bridge)
DGO: Distant galaxy object (Outside the Local Group with no Bridge access)

The last three segments ( $\large S_r\text{:}S_\theta\text{:}S_\phi$ ) represent the galaxy's coordinates within the system, providing over 281 trillion combinations for precise categorization.

These coordinates encode the galaxy's location using the spherical coordinate system ( $\large r$ , $\large \theta$ , $\large \phi$ ).

Segment 1: Radial distance ( $\large S_r$ )

This is encoded as a 16-bit value (0x0000–0xFFFF), with 0x0000 meaning "close proximity" and 0xFFFF meaning "the far edges of observable space."

$S_r = \lfloor\frac{r}{r_{\text{max}}} \times {\text{FFFF}}_{16}\rfloor$

$\large r$: Radial distance of the galaxy from the Milky Way's center (in million light-years)
$\large r_{\text{max}}$: Maximum radial distance of the coordinate system (in million light-years)
$\large \text{FFFF}$: Maximum value of a 16-bit hexadecimal number (65,535)

Segment 2: Azimuthal angle ( $\large S_\theta$ )

This is encoded as a 16-bit value (0x0000–0xFFFF), with 0x0000 meaning 0° and 0xFFFF meaning 360°.

$S_\theta = \lfloor\frac{\theta}{360^\circ} \times {\text{FFFF}}_{16}\rfloor$

$\large \theta$: Azimuthal angle in the galactic plane relative to the Milky Way’s center (in degrees)
$\large 360^\circ$: Total angular range of the plane (in degrees)
$\large \text{FFFF}$: Maximum value of a 16-bit hexadecimal number (65,535)

Segment 3: Elevation ( $\large S_\phi$ )

This is encoded as a 16-bit value (0x0000–0xFFFF), with 0x8000 representing the galactic plane (less than 0x8000 is below the plane and greater than 0x8000 is above the plane).

$S_\phi = \lfloor\frac{\phi + \phi_{\text{offset}}}{\phi_{\text{max}}} \times {\text{FFFF}}_{16}\rfloor$

$\large \phi$: Elevation angle relative to the galactic plane (in degrees)
$\large \phi_{\text{offset}}$: Offset added to handle negative values
$\large \phi_{\text{max}}$: Total elevation range
$\large \text{FFFF}$: Maximum value of a 16-bit hexadecimal number (65,535)

Example

$\text{LG:7F4A:5C2E:9B10}$

$\large \text{LG}$: Local Group ( $\large \text{TYPE}$ )
$\large \text{7F4A}$: Radial distance ( $\large S_r$ ); This represents a distance of approximately 15,000 light-years from the Milky Way.
$\large \text{5C2E}$: Azimuthal angle ( $\large S_\theta$ ); This represents an angle of around 130° in the galactic plane.
$\large \text{9B10}$: Elevation ( $\large S_\phi$ ); This represents a position above the galactic plane by a specific number of light-years.

Galaxy classifcation tags

The following table outlines the galaxy classification tags based on standard galactic morphology (Hubble sequence, dwarf galaxies, and irregular types). These tags can be integrated into the Galactic Numerical Designation System.

Galaxy classifcation tags
Tag	Type	Description
E0–E7	Elliptical galaxy	Spherical to elongated galaxies, with E0 being the roundest and E7 the most oval.
S0	Lenticular galaxy	Disk-shaped galaxies with no spiral arms, between ellipticals and spirals.
Sa, Sb, Sc	Spiral galaxy	Normal spirals: "a" has tight arms; "c" has loose, open arms.
SBa, SBb, SBc	Barred spiral galaxy	Spirals with a central bar: "a" has tight arms; "c" has loose, open arms.
Sd, SBd	Late-type spiral galaxy/Barred spiral galaxy	More diffuse, poorly defined arms.
Irr	Irregular galaxy	No regular shape or structure.
dE	Dwarf elliptical galaxy	Small, faint elliptical galaxy.
dSph	Dwarf spheroidal galaxy	Very faint, low-mass spherical galaxy.
dIrr	Dwarf irregular galaxy	Small irregular galaxies without clear structure.
BCD	Blue compact dwarf galaxy	Compact galaxies with intense star formation (blue color).
cD	Giant elliptical galaxy	Massive galaxies found in galaxy clusters, extended envelopes.
ULIR	Ultra-luminous infrared galaxy	Galaxies emitting enormous energy in infrared, often merging systems.
LIRG	Luminous infrared galaxy	Less energetic than ULIRGs, still infrared-dominated.
AGN	Active galactic nucleus	Galaxies with highly energetic cores powered by black holes.
QSO	Quasar (quasi-stellar object)	Extremely luminous galactic cores with active gravitational well.
RG	Radio galaxy	Galaxies emitting strong radio waves, often powered by jets from AGNs.
SMBH	Supermassive black hole galaxy	Galaxies dominated by their gravitational well activity.
MW	Milky Way-like galaxy	Standard spiral galaxy analogous to the Milky Way.
LMC/SMC	Magellanic-type galaxy	Irregular dwarf galaxies similar to the large and small magellanic clouds.
T	Tidal galaxy	Formed due to gravitational interaction (tidal forces) between galaxies.
M	Merger galaxy	Galaxies in the process of merging or post-merger.

A combination of tags can be used for more specific classifications (example: $\large \text{LG:ABCD:EF01:2345::SBc::AGN}$ ).