Most of us first encounter complex numbers through a slightly uncomfortable equation:
x² + 1 = 0
Therefore:
x² = −1
But no real number squared gives −1.
Mathematics solved this by extending the number system and defining:
i = √−1
A complex number can therefore be written as:
z = a + bi
where:
- a is the real part
- b is the imaginary part
- i² = −1
At first glance, this can look like a mathematical trick.
It isn’t.
Complex numbers are one of the most useful mathematical abstractions in science, engineering and computing.
1. From a Number Line to a Number Plane
Real numbers live on a one-dimensional number line.
Complex numbers give us a two-dimensional plane:
z = a + bi ↔ (a, b)
The horizontal axis represents the real component and the vertical axis represents the imaginary component.
For example:
z = 3 + 4i
can be represented by the point (3,4).
Its magnitude is:
|z| = √(3² + 4²) = 5
Its angle or phase is:
θ = tan⁻¹(4/3)
So a complex number can represent both:
Magnitude + Direction
This is where complex numbers become extraordinarily useful.
2. Cartesian and Polar Forms
The same complex number can be represented in different ways.
Cartesian form
z = a + bi
Polar form
z = r(cos θ + i sin θ)
where:
r = √(a² + b²)
Using Euler’s formula:
e^(iθ) = cos θ + i sin θ
we get:
z = re^(iθ)
This is a remarkably powerful representation.
Instead of thinking only about two numbers, we can think in terms of:
Amplitude + Phase
And amplitude and phase appear everywhere in physical and computational systems.
3. Euler’s Formula — A Beautiful Mathematical Bridge
One of the most famous equations in mathematics is:
e^(iπ) + 1 = 0
It connects five fundamental mathematical constants:
0, 1, e, i and π
But Euler’s formula is much more than mathematical beauty.
e^(iθ) = cos θ + i sin θ
provides a bridge between:
exponentials ↔ trigonometry ↔ rotation ↔ oscillation
That bridge is extremely useful when studying waves, signals, electrical systems, communications and control systems.
4. Complex Numbers and Rotation
Suppose:
z = re^(iθ)
Multiplying it by:
e^(iφ)
gives:
z’ = re^(i(θ+φ))
In simple terms, multiplication by a complex exponential can rotate a point.
This gives us a very elegant mathematical mechanism for representing rotations.
Instead of repeatedly manipulating sine and cosine equations, many rotation and oscillation problems become multiplication problems.
This idea appears in graphics, robotics, signal processing, physics and engineering.
5. Electrical Engineering and AC Circuits
One of the classic applications of complex numbers is alternating-current circuit analysis.
Electrical quantities such as voltage and current oscillate.
Instead of repeatedly working with expressions such as:
V(t) = V₀ cos(ωt + φ)
engineers can represent oscillating quantities using complex numbers and phasors.
Circuit impedance can be represented as:
Z = R + jX
where:
- R = resistance
- X = reactance
- j represents √−1 in electrical engineering
The magnitude tells us the overall opposition to current, while the phase captures the relationship between voltage and current.
A difficult time-domain problem can often become a much simpler algebraic problem.
6. Signal Processing and Fourier Analysis
Suppose we have audio, vibration, radar, network or sensor data.
A signal that looks complicated in the time domain may actually contain combinations of simpler frequencies.
Fourier analysis decomposes signals into these frequency components.
Complex exponentials provide an elegant representation:
e^(iωt) = cos(ωt) + i sin(ωt)
This idea forms part of the mathematical foundation behind tools such as:
Fourier Transform
Discrete Fourier Transform (DFT)
Fast Fourier Transform (FFT)
These are used across:
- Audio processing
- Image processing
- Telecommunications
- Radar
- Medical imaging
- Vibration analysis
- Sensor analytics
- Spectral analysis
- Scientific computing
Complex numbers therefore help us move between:
Time Domain ↔ Frequency Domain
7. Communication Systems
Modern communication systems depend heavily on amplitude and phase.
Wireless systems can encode information by changing these properties of a carrier signal.
For example, in Quadrature Amplitude Modulation (QAM), symbols can naturally be represented as points on a complex plane.
Think of a transmitted symbol as:
z = I + jQ
where:
- I = In-phase component
- Q = Quadrature component
The constellation of these complex-valued points represents digital information.
So when your phone communicates using sophisticated wireless networks, complex-number mathematics is operating underneath many layers of abstraction.
8. Control Systems
Complex numbers also appear naturally when studying the stability and behaviour of dynamic systems.
Engineers examine poles and zeros in the complex plane.
A pole might look like:
s = σ + jω
The real component can tell us about growth or decay.
The imaginary component relates to oscillation.
This makes the complex plane extremely useful for reasoning about:
Stability + Oscillation + Damping + System Response
Applications range from industrial automation to aerospace, robotics and power systems.
9. Quantum Mechanics
Complex numbers are fundamental to quantum mechanics.
Quantum states are represented using complex-valued wave functions.
A simplified representation might be:
ψ = a + bi
The directly observable probability is not simply ψ itself.
Instead, quantities involving its magnitude, such as:
|ψ|²
play a central role.
Here complex numbers are not merely a convenient calculation technique—they are embedded deeply in the mathematical framework used to describe quantum systems.
10. Computer Graphics and Robotics
Complex numbers can represent rotations elegantly in two dimensions.
If a point is represented by:
z = x + iy
multiplication by:
e^(iθ)
rotates the point through an angle θ.
This provides a compact way of understanding transformations.
For 3D rotations, related mathematical ideas extend into structures such as quaternions, widely used in robotics, aerospace systems, simulations and computer graphics.
11. Complex Numbers in Data Science and AI
Most introductory machine-learning models operate on real-valued data.
But complex-valued representations become useful when the underlying information naturally contains phase, frequency, waves or spectral characteristics.
Examples can arise in:
- Signal classification
- Radar analytics
- Wireless communications
- Medical imaging
- MRI reconstruction
- Audio processing
- Computer vision
- Spectral methods
- Scientific machine learning
- Complex-valued neural networks
This highlights an important lesson for data science:
The mathematical representation should follow the structure of the problem.
If the phenomenon contains magnitude and phase, forcing everything prematurely into purely real-valued representations can sometimes hide useful structure.
12. A Small Python Example
Python supports complex numbers directly.
z = 3 + 4j
print(z.real)
print(z.imag)
print(abs(z))The result is:
Real part = 3
Imaginary part = 4
Magnitude = 5
Scientific Python libraries such as NumPy can also perform complex-valued numerical computations, Fourier transforms and linear algebra.
So the journey from:
i = √−1
to computational engineering is surprisingly short.
The Bigger Lesson
Complex numbers demonstrate something important about mathematics.
Sometimes mathematics advances not by solving a problem inside the existing system, but by expanding the system itself.
Natural numbers were not enough.
We introduced integers.
Integers were not enough.
We introduced rational numbers.
Rational numbers were not enough.
We introduced real numbers.
And real numbers were not enough.
We introduced complex numbers.
What initially looks “imaginary” can eventually become indispensable for describing reality.
WHY → WHAT → WHERE → WHEN → HOW
For learning complex numbers, I would approach the topic in this order:
WHY? Real numbers alone cannot conveniently represent every mathematical and physical phenomenon.
WHAT? A complex number combines real and imaginary components: a + bi.
WHERE? Signals, circuits, communications, control systems, physics, graphics, robotics and scientific computing.
WHEN? Especially when the problem involves oscillation, rotation, frequency, magnitude and phase.
HOW? Complex algebra, Euler’s formula, polar representation, Fourier analysis—and computational tools such as Python, NumPy, MATLAB and scientific libraries.
AI can increasingly help us with the HOW.
But understanding the WHY and WHAT remains essential if we want to know whether the answer actually makes sense.
Sunday Mathematics
The objective of this series is not mathematics for examinations.
It is mathematics for computer science, data science, AI, engineering, technology and decision-making—connecting equations with the systems around us.
Sunday Mathematics #3: Complex Numbers
From √−1 to signals, circuits, wireless communication, quantum mechanics, robotics and AI.
Sometimes the numbers we call imaginary help us understand the real world.
HSOPC — Harwani Systems https://www.harwanisystems.in/
TechAndTrain https://www.techandtrain.com/
Neil Harwani — LinkedIn https://www.linkedin.com/in/neil27/
Email: Neil@HarwaniSystems.in
Narrative and concept: Neil Harwani
Creation help: ChatGPT
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