What is a Physical Qubit, History, Types and Applications
A physical qubit is the foundation of a quantum computer. It's a qubit's physical example, like a transistor.
To elaborate:
A physical qubit?
To effectively control the |0⟩ and |1⟩ states, a physical qubit must encode its degree of freedom. More than two states can be employed with physical qubits, although two-level systems are best. They represent quantum hardware.
A conventional bit can only exist in either state, whereas a physical qubit can exist in both. Entanglement, which occurs when two or more qubits share the same fate, and the ability to analyse vast volumes of data in parallel and possibly perform some calculations exponentially faster than traditional computers make this possible.
One physical qubit is delicate and error-prone. Noise from inherent qualities or ambient interactions can cause small manipulation errors in physical qubits. These differences can worsen with time and undermine a computation. In the early-stage Noisy Intermediate-Scale Quantum (NISQ) phase, “qubit” is synonymous with “physical qubit.” A 256-qubit neutral atom quantum computer has 256 atoms, a 1:1 ratio.
History
Paul Benioff and Richard Feynman proposed in the 1980s that quantum computers could better simulate physical processes. Schumacher coined “qubit” in 1995. The first quantum algorithm was demonstrated in 1998, advancing the field quickly. Instead than growing physical qubits, the focus has been on developing significant logical qubits by improving their quality (fidelity) and error correction.
The Process
A physical qubit encodes information using a quantum system feature. The |0⟩ and |1⟩ states can be modelled using atom energy levels or electron spin. Path, time-bin, or polarisation can encode photonic qubits.
To perform quantum processes, these states must be carefully managed. Several methods are employed:
Microwave pulses manipulate superconducting qubits.
Laser pulses can modulate neutral atoms or trapped ions.
Magnetism affects electron spin.
Analogous procedures work too.
Being in superposition gives qubits power. When a qubit is measured in superposition, its quantum state determines whether it “collapses” to 0 or 1. Since they transport data, physical qubits are necessary. Input data, single- and two-qubit quantum gate calculations, and outcomes are shown.
Advantages
Entanglement and superposition: Quantum computers can analyse massive volumes of data simultaneously, speeding up procedures.
Exponential scaling: One qubit doubles a system's computational space, allowing few qubits to represent multiple states. The phenomena is exponential scaling.
Physical qubits excel at optimisation, factorisation, and molecular simulation.
Disadvantages
Qubits can make mistakes and lose their quantum state due to environmental noise like temperature changes or electromagnetic radiation. Loss of quantum information limits calculation times.
System scalability: Adding qubits is difficult. Controlling qubits individually and minimising crosstalk becomes a major engineering difficulty as qubit numbers increase.
Due to the natural inclination of physical qubits to make mistakes, quantum error correcting solutions must be complex and resource-intensive. Several physical qubits are needed to create a stable logical qubit.
Implementations, types
Every physical qubit implementation has pros and cons, hence there is no “best” way. This encourages new modality research. Physical systems classify key architectures:
Using superconducting circuits at absolute zero, superconducting qubits are formed. They are fast and can be manufactured using current chip-making technology, but they are noise-sensitive and require severe cold. Google and IBM employ them.
Trapped Ion Qubits: Lasers and electromagnetic fields control charged atoms (ions) suspended. They offer high-fidelity operations and long coherence durations, but they are slower and harder to scale than superconducting qubits.
Lattices or optical tweezers hold neutral atom qubits, also known as cold atoms. Their long coherence periods and ability to form large, reconfigurable arrays make their scalability appealing. Since atoms are not generated, they are considered equivalent and faultless, eliminating the need for calibrations or mapping circuit qubits to physical qubits to lower error rates or improve connections. The largest publicly accessible quantum computer is Aquila, with 256 neutral atoms.
Data is encoded using photons, light particles. They run at room temperature and are good for quantum communication but not complex computations. Encoding photons has many methods: Polarisation encoding uses horizontal and vertical light modes.
Photons in one of two fibre optics are used in “dual-rail” route encoding.
Time-bin encoding uses photon timing within a defined period (early or late).
In carbon lattice vacancies, quantum dots, or vacuum chambers, electron spins can encode information. Electron-constricting semiconductor nanocrystals called quantum dots transmit information through electron presence superpositions.
NV Centre: The nitrogen vacancy centre encodes information in either electron spins or nuclear spins of nitrogen atoms. The largest NV Centre device contains two qubits.
Theoretical topological qubits encode information through qubit movements.
Challenges
A large-scale quantum computer made from physical qubits must overcome many challenges:
The length of a qubit's quantum state before decoherence must be extended for longer computations.
Scalability: Engineering and manufacturing must increase qubits while maintaining performance and low error rates.
Error Correction: A “fault-tolerant” quantum computer requires viable quantum error correction codes (QECC) that can be implemented in hardware. Physical qubits' intrinsic noise makes mistake correction necessary.
As systems grow, reducing qubit crosstalk becomes more critical.
Applications
Physical qubits have many uses, including:
Quantum simulation can simulate complex molecules and materials, which may progress material science and medicine.
Solving complex optimisation problems in supply chain management, finance, and logistics.
Cryptography uses methods like Shor's to break current encryption and construct quantum-safe encryption.
AI: Improving machine learning and AI algorithms by processing massive datasets is AI.