First proposed in the 1980s, quantum computer development has come a long way from pure theory to practical hardware applications. Today, IBM Quantum makes real quantum hardware—a tool scientists only began to imagine three decades ago—available to hundreds of thousands of developers.  

When physicists and engineers encounter difficult problems, they turn to supercomputers. However, even supercomputers are binary-code-based machines reliant on 20th-century transistor technology, and they struggle to solve highly complex problems. These classical computers are also subject to material restrictions, such as overheating, putting hard limits on their ability to process information. There are some complex problems, such as the modeling of individual atoms in a molecule, that we do not know how to solve with classical computers at any scale.

The laws of quantum mechanics dictate the order of the natural world. Computers that make calculations using the quantum states of quantum bits should, in many situations, be our best tools for understanding it and solving our most complex problems. 

When studying quantum computers, it is important to understand that quantum mechanics is not like traditional physics. Describing the behaviors of quantum particles presents a unique challenge, as most common-sense paradigms for the natural world simply lack a vocabulary to comprehend the seemingly counterintuitive behaviors of quantum particles. 

A classical bit can only exist in either a 0 position or a 1 position. Qubits, however, can also occupy a third state known as a superposition. 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. In such systems, the term bit can refer to either the material or process used to represent a 0 or 1, or the measurement of that bit (i.e., a 0 or a 1). 

In traditional or classical computing, a single bit can be thought of as a piece of binary information, notated as either a 0 or a 1. Modern computers typically represent bits as either an electrical voltage or current pulse (or by the electrical state of a flip-flop circuit).

In these systems, when there is no current flowing, the circuit can be considered to be off, and this state is represented as a 0. When current is flowing, the circuit is considered on, and this state is represented as a 1.

The term “bit” is itself a portmanteau for “binary digit,” and binary bits are the foundational basis of all computing. Whether recording a digital video, animating a 3D model or using a calculator app—all data from operating systems to software are built out of binary code, which is a collection of bits. A computer byte consists of eight bits, which is the minimum number of bits needed to convey a single textual character in binary. 

Bits can be represented electrically, by running (or not running) current through a silicon chip, for example. Bits can also be represented physically, as a hole or the absence of a hole in a piece of paper, as was used in antiquated punch-card computing. Any two-level system in which the state of the system can be described in only one of two potential positions (e.g., up or down, left or right, on or off) can be used to represent a bit. 

While quantum technologies do use binary code, the quantum data derived from a quantum system—such as a qubit—encodes data differently from traditional bits, with a few remarkable advantages. Researchers have established a variety of ways to either create qubits or use naturally occurring quantum systems as qubits. However, in nearly all instances, quantum computers require extreme refrigeration to isolate qubits and prevent interference. 

Theoretically, any two-level quantum system can be used to make a qubit. A quantum system is described as two-level when certain system properties can be measured in binary positions, such as up or down. Multi-level quantum systems can be used to create qubits, as well, as long as two aspects of that system can be effectively isolated to produce a binary measurement. Just as traditional computers can use multiple types of bits—such as electrical current, electrical charge, or holes punched (or not punched) in a piece of paper for punch-card computing—quantum computers can use multiple types of bits. Certain bits are better suited to certain functions, and an advanced quantum computer will likely use a combination of bit types to achieve different operations.

Since each bit can represent either a 0 or a 1, by pairing two bits of information, we can create up to four unique binary combinations:

1. 0 0
2. 0 1
3. 1 0
4. 1 1

While each bit can be either a 0 or a 1, a single qubit can be either a 0, a 1, or a superposition. A quantum superposition can be described as both 0 and 1, or as all the possible states between 0 and 1 because it actually represents the probability of the qubit’s state. 

On the quantum level, qubit probability is measured as a wave function. 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.

When processing a complex problem, such as factoring a large prime number, traditional bits become bound up by holding large quantities of information. Quantum bits behave differently. Because qubits can hold a superposition, a quantum computer using qubits can calculate a much larger volume of data. 

As a helpful analogy for understanding bits vs. qubits, imagine you are standing in the center of a complicated maze. To escape the maze, a traditional computer would have to “brute force” the problem, trying every possible combination of paths to find the exit. This kind of computer would use bits to explore new paths and remember which ones are dead ends.

Comparatively, a quantum computer might, figuratively speaking, at once derive a bird’s-eye view of the maze, testing multiple paths simultaneously and revealing the correct solution. However, qubits do not “test multiple paths” at once. Instead, quantum computers measure the probability amplitudes of qubits to determine an outcome. As these amplitudes function like waves, they also 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. 

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. The implications of quantum entanglement are as vast as our understanding of this phenomenon are limited. Suffice to say that traditional bits do not become entangled. In this way, entangled qubits seemingly can transfer information across even lightyears instantaneously, faster than the speed of light. While qubits do not actually transfer data faster than light, quantum entanglement can dramatically increase the power of quantum circuits. 

Quantum particles are said to have coherence when they are sufficiently controlled to function as qubits. When a qubit loses this ability, it is described as decoherent. The high-powered refrigeration required to create a state of coherence for functional qubits is a major challenge for quantum computing. 

Even under the coldest conditions, qubit systems are also generally susceptible to failure caused by decoherence. Thankfully, advancements in the emerging field of algorithmic quantum error correction have the potential to stabilize previously tenuous quantum systems.