How a Quantum Computer Is Built

1. Making the Qubit (The “Quantum Bit”)

What it is: A qubit is the smallest unit of quantum information. Unlike a classical bit (0 or 1), a qubit can be in superposition (0 and 1 at the same time) and can also be entangled with others.
How it’s made:
- Superconducting qubits: tiny electrical circuits made from superconducting materials (like aluminum on silicon) that are cooled near absolute zero (–273°C) so they lose resistance and act quantum-like.
- Trapped ion qubits: single atoms held in place by electromagnetic fields and manipulated with lasers.
- Photonic qubits: particles of light (photons) encoded with quantum states.
Cost reality: Each qubit isn’t like a cheap transistor. It takes millions of dollars of equipment to control just a few dozen qubits. For example:
- A superconducting system needs a dilution refrigerator (~$500k–$2M).
- Ultrapure materials and nanofabrication facilities are required (tens of millions for the cleanroom environment).
- Lasers for ion traps cost hundreds of thousands each.

So, building even one reliable qubit is extremely expensive compared to making a transistor in a regular chip fab.

2. Controlling the Qubit

Qubits are very fragile; any noise (temperature, vibration, electromagnetic waves) can flip them.
To “speak” to a qubit, you need specialized hardware:
- Microwave generators (for superconducting qubits).
- Laser arrays (for trapped ions).
- Precise optics (for photons).
Cost example: A state-of-the-art control system for a 50–100 qubit device may run into millions of dollars just in RF/microwave equipment and cabling.

3. Reading Out the Qubit

You need detectors (super-sensitive amplifiers, photon detectors, or fluorescence imaging) to measure whether a qubit is in state “0” or “1.”
This requires cryogenic electronics or special cameras, which add more cost and complexity.

4. Scaling Up

Making 1 qubit is hard, but useful computation needs thousands to millions of qubits.
The big challenge is error rates:
- A physical qubit may only be stable for microseconds or milliseconds.
- That’s like having a computer memory chip where each bit flips randomly every few seconds.
To fix this, scientists use error correction, which groups many physical qubits into one logical qubit.

Enter Virtual Qubits (a.k.a. Logical Qubits)

Why We Need Them

Today, a single reliable logical qubit can require hundreds to thousands of physical qubits.
Example: Google’s roadmap estimates ~1,000 physical qubits per logical qubit with today’s error rates.
That means a million-qubit computer might need a billion physical qubits (astronomically expensive).

How Virtual Qubits Help

Qubit virtualization is the process of abstracting noisy physical qubits into stable, usable “virtual” ones through error correction and clever software.
Instead of directly programming raw qubits, you interact with these virtual/logical qubits that hide the mess underneath.
Cost savings come from efficiency:
- Without virtualization: you might need 1,000 raw qubits to run a calculation.
- With virtualization: smarter error correction + compilers can reduce that to 100 or fewer.
- That’s a 10× savings in hardware cost, meaning fewer dilution refrigerators, fewer lasers, less control hardware.
Just like virtualization in classical computing allowed one physical server to act as many machines (cutting data center costs), virtual qubits let one quantum processor handle more useful workloads.

The Money Side

Here’s a rough picture based on public estimates and reported builds:

Prototype scale (50–100 qubits):
- Hardware + cryogenics + control electronics: ~$10M–$25M.
- Requires a team of PhD-level engineers to maintain.
Intermediate scale (1,000 qubits):
- Likely ~$100M–$200M+ per system.
- Think of national labs and big tech companies (IBM, Google, Quantinuum).
Commercial useful scale (millions of qubits):
- Trillions of dollars if you had to scale linearly with today’s fragile physical qubits.
- But with virtual qubits and error correction, the cost could shrink by orders of magnitude — making large-scale, profitable machines actually possible.

Simple Analogy

Think of raw qubits as lightbulbs that burn out after a few seconds.
If you need light in a stadium, you’d need millions of them replaced constantly — impossible.
But if you build a system where 100 fragile bulbs act as one super-reliable bulb (a virtual bulb), suddenly the system becomes practical and affordable.

That’s exactly what virtual qubits are doing for quantum computing.

👉 Bottom line:

Building a quantum computer is not just about making qubits — it’s about making them usable and affordable.
Without virtual qubits, the cost of scaling would explode.
With virtual qubits, quantum computers become something we can actually build, share in the cloud, and one day use for real-world profit.

IBM (Superconducting Gate-Based Quantum Computers)

Approach: IBM pioneered the term virtual qubit in 2020–2021 as part of their effort to make noisy, intermediate-scale quantum (NISQ) devices more practical.
Method:
- They combine physical qubits with calibration tricks and software error mitigation.
- Virtual qubits improve gate fidelity (i.e., fewer wrong answers when applying quantum gates).
- This lets IBM machines run deeper circuits (more operations before errors overwhelm results).
Goal: Step toward logical qubits (the error-corrected units needed for fault-tolerant quantum computing).
User Experience: In Qiskit, users don’t have to worry about mapping their code — the compiler decides whether a logical operation is executed directly or through a virtual qubit layer.

D-Wave (Quantum Annealing)

Approach: D-Wave doesn’t use gate-based qubits but quantum annealing, where qubits are arranged in large networks to solve optimization problems.
Method:
- Their qubits are arranged in structures like Chimera or Pegasus graphs.
- To represent a single logical variable, D-Wave often chains multiple physical qubits together into a virtual qubit.
- These chains act as one stronger, more reliable unit that better holds the intended quantum state.
Goal: Improve reliability of optimization runs, since real hardware qubits can flip states due to noise.
User Experience: Developers using D-Wave’s Ocean SDK specify logical problems, and the system handles qubit chaining automatically.

Rigetti (Superconducting Gate-Based, Similar to IBM)

Approach: Rigetti is focused on hybrid quantum-classical computing.
Method:
- Like IBM, they face the challenge of noisy qubits.
- They use calibration, error mitigation, and software mapping strategies — essentially their own form of virtual qubits.
- Rigetti emphasizes qubit connectivity and compiler-level optimization, where virtual qubits help extend circuit depth before noise dominates.
Goal: To make small-to-medium sized quantum computers useful for near-term hybrid algorithms.
User Experience: In Rigetti’s Quil programming language, users program logical qubits, but the compiler maps them intelligently onto physical/virtual resources.

Comparison at a Glance

Company	Tech Model	Virtual/Logical Qubit Use
IBM	Gate-based (superconducting)	Virtual qubits created by combining physical qubits + calibration; boosts gate fidelity; hidden from user via Qiskit.
D-Wave	Quantum annealing	Virtual qubits formed by chaining multiple physical qubits; improves reliability in optimization problems.
Rigetti	Gate-based (superconducting)	Virtual qubits via compiler mapping + calibration; extends circuit depth and fidelity for hybrid workloads.

Why This Matters

In all cases, virtual qubits are a bridge:
- They make noisy, imperfect hardware usable today.
- They are stepping stones toward the future goal of fault-tolerant logical qubits, which could run powerful algorithms like Shor’s or Grover’s at scale.

👉 So, IBM and Rigetti use virtual qubits to boost accuracy in gate-based quantum circuits, while D-Wave uses them as chains of physical qubits to represent stable logical units in their annealing system.