Understanding qubits isn’t about grasping an abstract scientific concept; it’s about comprehending the building blocks of a technology that will reshape everything from drug discovery to artificial intelligence, from financial modeling to climate prediction. 

As we stand at a technological inflection point in how we process information, with coherence times improved by 100,000-fold in just two decades and error rates approaching the thresholds needed for fault-tolerant computation, the qubit has evolved from laboratory curiosity to the foundation of a coming computational revolution [4]. Each qubit, protected by a cryogenic environment and entangled with its neighbors, represents both tremendous progress achieved and exciting challenges ahead in our quest to build truly scalable quantum computers.

What Is A Qubit?

A qubit, or quantum bit, is the basic unit of information used to encode data in quantum computing and serves as the quantum equivalent of the traditional bit used by classical computers to encode information in binary. The term “qubit” is attributed to American theoretical physicist Benjamin Schumacher [9].

To understand what makes a qubit revolutionary, we must first appreciate how radically it differs from the classical bits that power our current computers. Traditional computers operate on bits that exist definitively as either 0 or 1—like a light switch that’s either on or off. In traditional computing, bits are typically represented as electrical voltage or current pulses, or by the electrical state of a flip-flop circuit. When there is no current flowing, the circuit is considered off (represented as 0), and when current is flowing, the circuit is considered on (represented as 1) [9]. This binary certainty has served humanity remarkably well, enabling everything from moon landings to artificial intelligence.

A qubit, short for quantum bit, is the fundamental unit of quantum information and the basic building block of quantum computers [10]. According to DiVincenzo’s seminal work on the physical implementation of quantum computation, a qubit is, at its most essential, “simply a quantum two-level system” [7]. Yet this deceptively simple definition encompasses a profound departure from classical computing – qubits exist in a realm where the conventional rules of computing dissolve [8].

What Makes A Qubit Special? Superposition & Entanglement

Superposition

Unlike classical bits that must be either 0 or 1, qubits can exist in a quantum superposition of both states simultaneously. The quantum state of a qubit, in general, is a linear superposition of the two basis states |0⟩ and |1⟩, written as |ψ⟩ = α|0⟩ + β|1⟩, where α and β are complex numbers and |α|² + |β|² = 1 [1]. This superposition means a qubit can be partially in the |0⟩ state and partially in the |1⟩ state simultaneously. This isn’t merely uncertainty about which state the qubit is in—it’s a fundamental quantum property where the qubit genuinely exists in both states at once until measured [8].

A superposition represents 0, 1, and all the positions in-between taken at once, for a total of three separate positions. While qubits can encode three separate positions, they are still used to convey information through a binary system. On the quantum level, qubit probability is measured as a wave function, and the probability amplitude of a qubit can be used to encode more than one bit of data and carry out extremely complex calculations when combined with other qubits [9]. Here’s an analogy: imagine a pot of water that could be both boiling and not boiling simultaneously—this is the quantum realm. Only when you measure the qubit (lift the lid) does it collapse into one definite state or the other. This property enables quantum computers to process vastly more information than conventional computers [10].

This superposition property is what gives quantum computers their computational advantage. While a classical bit stores one piece of information, a qubit in superposition effectively explores multiple computational paths simultaneously [1]. While a classical computer with n bits can only be in one of 2^n possible states at any given moment, n qubits can exist in a superposition of all 2^n states simultaneously, potentially exploring vast solution spaces in parallel [8].

To illustrate this difference, imagine standing in the center of a complicated maze. A traditional computer would have to “brute force” the problem, trying every possible combination of paths to find the exit, using bits to explore new paths and remember which ones are dead ends. A quantum computer, figuratively speaking, might at once derive a bird’s-eye view of the maze, testing multiple paths simultaneously and revealing the correct solution. However, quantum computers don’t actually test multiple paths at once—instead, they measure the probability amplitudes of qubits to determine an outcome. These amplitudes function like waves that overlap and interfere with each other. When asynchronous waves overlap, it effectively eliminates possible solutions to complex problems, and the realized coherent wave or waves present the solution [9].

Entanglement

Beyond superposition lies another quintessentially quantum phenomenon: entanglement [8]. Entanglement creates a special relationship between qubits where they cannot act independently of each other. When qubits become entangled, their states remain linked even when physically separated by large distances—far beyond atomic scales. This quantum connection means that the state of one qubit directly influences another, allowing for complex correlations that don’t exist in classical computing [10].

First described by Einstein as “spooky action at a distance,” quantum entanglement is a phenomenon in which two qubits (or any two or more quantum particles) intertwine in such a way that the state of one particle cannot be described independently of the state of the other, regardless of the distance between them [3, 9]. When two qubits are entangled, they both exist in a superposition until either is measured. Once observed, the quantum superposition of both is collapsed, and whichever qubit isn’t observed assumes the opposite position of the one that was observed. For example, if one half of an entangled qubit pair is measured in a 1 position, the other qubit can instantly be measured as a 0. Quantum entanglement can dramatically increase the power of quantum circuits [9].

Two-qubit gates such as CNOT and CPHASE are called “entangling gates,” because they can take product states as inputs and output entangled states. They are thus an indispensable component of a universal gate set for quantum logic [2]. This entanglement scales exponentially: n qubits require a 2ⁿ-dimensional complex vector space for their complete description. This exponential scaling underlies quantum computing’s potential advantage—while classical systems would need to track 2ⁿ separate numbers to simulate n qubits, a quantum computer naturally embodies this vast state space in just n physical qubits [7].

What Are The Qubit Engineering Challenges?

Coherence Times & Decoherence

A critical aspect of qubit performance is maintaining quantum coherence. Coherence time represents how long a qubit can maintain its quantum state. Qubits are extremely sensitive to their environment and quickly lose their quantum properties through interaction with surrounding “noise.” For quantum computing to work, systems must create and store information, leave it undisturbed, and retrieve it later—but current qubits struggle to maintain their states long enough for complex calculations [10].

Random, uncontrollable physical processes in the qubit control and measurement equipment or in the local environment surrounding the quantum processor are sources of noise that lead to decoherence and reduce the operational fidelity of the qubits [2].

The Materials Science Challenge

Dielectric loss represents one of the primary challenges in qubit design. The electric field energy emanating from qubit metallization interacts with imperfections in surrounding dielectric materials, particularly at crucial interfaces: substrate-to-metal, substrate-to-air, and metal-to-air boundaries. Each interface contributes to the total loss through what researchers call “participation”—the fraction of electric field energy flowing through that region [5].

Microscopic two-level systems (TLSs)—localized low-energy excitations predominantly found in noncrystalline dielectric materials—represent one of the primary sources of decoherence [6]. These can be visualized as ions or electrons that tunnel between two spatial quantum states, creating dipole moments that interact with electromagnetic fields and disturb nearby qubits [6].

Even in the superconducting state, thermal and radiative energy can break Cooper pairs into quasiparticles—unpaired electrons that introduce dissipation. While theory predicts exponentially suppressed quasiparticle populations at the millikelvin temperatures of dilution refrigerators, non-equilibrium quasiparticles persist, likely generated by environmental radiation, cosmic rays, or incomplete thermalization [5].

Temperature Requirements

While powerful, qubits are extremely temperamental and face significant operational challenges. To function properly, qubits must be cooled to a temperature only a fraction of a degree higher than absolute zero—colder than outer space. This extreme refrigeration is necessary to create a state of coherence, where quantum particles are sufficiently controlled to function as qubits. When a qubit loses this ability, it is described as decoherent [9].

The high-powered refrigeration required to maintain coherence represents a major challenge for quantum computing. Even under the coldest conditions, qubit systems are generally susceptible to failure caused by decoherence. Researchers have established various ways to either create qubits or use naturally occurring quantum systems as qubits, but in nearly all instances, quantum computers require extreme refrigeration to isolate qubits and prevent interference [9].

Error Correction

Error correction presents another fundamental challenge. Even with reduced noise, quantum systems will produce errors that must be corrected before quantum computers can make a meaningful impact 10].

Advancements in the emerging field of algorithmic quantum error correction have the potential to stabilize previously tenuous quantum systems, offering hope for more reliable quantum computing in the future [9]. Current estimates suggest thousands of physical qubits may be needed to create a single error-corrected logical qubit when physical error rates are around 0.1%. Recent achievements include demonstrations of up to 48 logical qubits using 280 physical atoms, pointing toward medium-scale logical quantum processors in the near future [8].

Final Thoughts

Where classical computing reached its zenith through relentless miniaturization and optimization of a fundamentally simple binary principle, quantum computing demands that we master an entirely new realm – one where uncertainty becomes a computational resource and measurement itself transforms reality. As we look toward a future where quantum processors might untangle protein folding mysteries, optimize global supply chains, or reveal new materials with unprecedented properties, we must remember that today’s qubits—fragile, temperamental, and demanding—are the quantum equivalent of vacuum tubes in the 1940’s – the early days of classical computing. 

The revolution ahead isn’t just computational – it’s conceptual, as well, requiring us to think in superpositions rather than certainties, in entanglements rather than isolations, and in probability amplitudes rather than deterministic paths. With all its quantum strangeness, the qubit invites us to think in new, creative, and strange ways, ourselves. Personally, I embrace “strange” thinking and invite you to do the same.

Thanks for reading!

References

[1] Kwon, et al. – ‘Gate-based superconducting quantum computing‘ – https://pubs.aip.org/aip/jap/article/129/4/041102/957183/Gate-based-superconducting-quantum-computing

[2] Krantz, et al. – ‘A quantum engineer’s guide to superconducting qubits‘ – https://pubs.aip.org/aip/apr/article/6/2/021318/570326/A-quantum-engineer-s-guide-to-superconducting

[3] Fleming – ‘Entanglement and Schrödinger’s Cat’ – https://chem.libretexts.org/Bookshelves/Physical_and_Theoretical_Chemistry_Textbook_Maps/Quantum_Chemistry_with_Applications_in_Spectroscopy_(Fleming)/02%3A_Particle_in_a_Box/2.08%3A_Entanglement_and_Schrodinger’s_Cat#:~:text=This%20implies%20that%20the%20cat,incorrect%20description%20of%20the%20atom.

[4] Gambetta, et al. – ‘Building logical qubits in a superconducting quantum computing system‘ – https://www.nature.com/articles/s41534-016-0004-0

[5] Murray – ‘Material matters in superconducting qubits‘ – https://arxiv.org/pdf/2106.05919

[6] Oliver & Welander – ‘Materials in superconducting quantum bits‘ – https://www.researchgate.net/publication/259437347_Materials_in_superconducting_quantum_bits

[7] DiVincenzo – ‘The Physical Implementation of Quantum Computation‘ – https://arxiv.org/pdf/quant-ph/0002077

[8] Chae, et al. – ‘An elementary review on basic principles and developments of qubits for quantum computing‘ – https://nanoconvergencejournal.springeropen.com/articles/10.1186/s40580-024-00418-5

[9] Schneider & Smalley – ‘What is a qubit?‘ – https://www.ibm.com/think/topics/qubit

[10] Shea – ‘Creating the Heart of a Quantum Computer: Developing Qubits‘ – https://www.energy.gov/science/articles/creating-heart-quantum-computer-developing-qubits