Quantum Computing Glossary
A Guide to Quantum Terminology
A
Analog Quantum Computing
Analog quantum computing follows a different approach. Instead of applying discrete gates, the problem is encoded directly into a physical system, which is controlled continuously.
The system evolves gradually from an initial state to a final state that represents the solution. This continuous evolution can reduce certain types of errors compared to gate-based approaches.
This model is particularly well suited for problems like optimization and simulation. A key example is quantum annealing, where the system is guided toward the lowest energy state to find optimal solutions.
B
Bias Tee
A bias tee is a small electronic component that combines two types of electrical signals into a single wire: a slow, steady signal and a fast, pulsed one, keeping them from interfering with each other.
In superconducting quantum processors, qubits need both a constant magnetic field to hold them in the right state and fast pulses to actually perform operations. A bias tee delivers both through the same line simultaneously, which simplifies the hardware design and is essential for running the slow, precise control sequences that Qilimanjaro’s analog quantum computers require.
C
Coherence
It is the time during which a qubit can maintain its quantum state (superposition and entanglement) without degrading before environmental disturbances cause it to lose its information. Basically, it is the time during which the qubit can be used to perform calculations.
Complex Numbers
Complex numbers extend real numbers by including an imaginary component, a quantity whose square is negative. While this sounds abstract, they are the natural language for describing waves, oscillations, and rotations.
Complex numbers are the natural language of quantum mechanics: the state of a quantum system is described by a complex-valued wave function that encodes both measurement probabilities and how quantum states interfere. This is why complex numbers appear everywhere in quantum computing (in qubit states, gate operations, and the mathematics connecting hardware to computation).
D
Decoherence
It is the phenomenon by which a qubit loses its coherence due to interaction with the environment (light, heat, noise, or measurement). Managing decoherence is essential to maintain the quantum state of qubits and to achieve reliable operations.
Digital Quantum Computing
Digital quantum computing is conceptually similar to classical computing: calculations are performed through a sequence of quantum gates applied step by step.
These gates are not physical circuits, but highly precise energy pulses (such as microwaves or lasers) used to control the state of qubits.
Because qubits are extremely sensitive to their environment, errors are unavoidable. To address this, digital quantum computing relies on error correction, where information is distributed across many physical qubits to form a more stable logical qubit.
While this approach is universal and can, in principle, solve any computable problem, it requires a large number of qubits to achieve reliable results.
Digital Annealer
A Digital Annealer is a classical emulator or processor designed specifically to solve optimization problems. It is called “digital” because it is built using conventional transistors and electronic circuits, and an “annealer” because its hardware architecture reproduces the concept of simulated annealing, a classical algorithm inspired by thermodynamic processes.
Simulated annealing can be considered the classical counterpart (or emulator) of the quantum annealing algorithm, even though it does not exploit quantum phenomena such as superposition or entanglement.
A Digital Annealer can offer performance improvements over general-purpose classical architectures for certain optimization problems. However, because these processors rely on conventional electronic technology, their operation falls entirely within the classical computing paradigm and cannot provide a quantum advantage.
E
Entanglement
Two particles can become entangled so that, no matter how far apart they are, measuring one instantly affects the other. This quantum phenomenon is fundamental in applications such as quantum communications or quantum computing.
Emulator
An emulator is a classical system that, using classical algorithms, aims to reproduce a quantum phenomenon. Such emulators work for specific quantum systems and under certain conditions, such as, for example, having little entanglement in the system we want to emulate. Entanglement is precisely one of the fundamental properties that characterize how “deeply quantum” a system is. When the degree of entanglement increases significantly, classical emulation becomes exponentially costly or even impractical. Therefore, emulators do not allow us to overcome the limitations of classical computers and cannot provide a quantum advantage.
F
Full-Stack Quantum Computing
It is a comprehensive approach that encompasses the coordinated development of quantum hardware, electronic control, low- and high-level software, algorithms, and applications under a co-design strategy. This approach optimizes end-to-end system performance by aligning the physical architecture with programming layers and use cases, enabling faster achievement of practical advantage compared to fragmented models.
Fluxonium Qubits
Fluxonium qubits are a type of superconducting qubit designed to improve stability and reduce sensitivity to noise. They use a circuit with a Josephson junction and a large inductance, allowing them to maintain their quantum state for longer periods. This makes them a promising approach for building more reliable quantum systems, especially in analog quantum computing.
H
Hybrid Computing (Classical-Quantum Integration)
It is the model in which quantum computers operate as specialized accelerators within high-performance computing (HPC) infrastructures, delegating to the quantum processor (QPU) the subproblems where it can provide an advantage, while classical computing handles orchestration, pre- and post-processing, and workflow control.
J
Josephson Junction
A Josephson junction is a tiny electronic component made of two superconducting metals (metals that conduct electricity with zero resistance when cooled to temperatures close to absolute zero) separated by an extremely thin barrier. At those temperatures, electrical current can pass through the barrier in a way that is only possible at the quantum level.
That quantum behavior makes the Josephson junction unlike any ordinary circuit element, and it is what allows engineers to build a qubit out of a simple circuit. It is the core building block of all superconducting qubits, including those used in Qilimanjaro’s hardware.
L
Logical Qubit
A logical (or “virtual”) qubit is a fault-tolerant unit of quantum information that encodes the state of a qubit across multiple entangled physical qubits using quantum error correction techniques. It is the key element for building scalable and reliable (fault-tolerant) digital quantum computers.
LNA (Low Noise Amplifier)
Reading the state of a qubit produces an extremely faint signal. A Low Noise Amplifier (LNA) amplifies this signal while adding as little noise as possible, making it readable by room-temperature electronics.
In superconducting quantum processors, the LNA is typically a cryogenic amplifier operating at around 4 Kelvin inside the dilution refrigerator, placed as close as possible to the quantum chip. Any attenuation before the first amplifier significantly degrades the quality of the readout signal, so the LNA’s position and quality directly affect how accurately qubit states can be measured.
Q
Qubit
A qubit is the quantum version of a bit: while a bit can only take the values 0 or 1, a qubit has two basis states |0⟩ and |1⟩ and can be in either of them or in a linear combination (or superposition) of both.
It is very sensitive to the environment and inherently noisy, so it suffers from decoherence and errors that limit the reliability of computations. There are different technological implementations of physical qubits, including superconducting circuits, neutral atoms, trapped ions, and photons, among others.
Quantum Computing
Quantum computing is a computing paradigm based on the laws of quantum mechanics, in which information is encoded in qubits and phenomena such as superposition and entanglement are exploited to process information in a way that differs from classical computing. It is not simply a faster supercomputer, but a different computational model that can offer advantages for certain classes of specific problems such as simulation, optimization, certain AI subroutines, and cryptography problems.
Quantum-Inspired Computing
Quantum-inspired computing involves using classical algorithms running on conventional computers (CPUs or GPUs) to emulate the behavior of a quantum system with a finite level of precision. This approach does not rely on quantum hardware or physical quantum phenomena.One of the most well-known classical tools for approximating quantum systems is Tensor Networks. This mathematical formalization was originally developed to emulate the behavior of multi-particle systems. These techniques can represent certain complex systems in a compressed format and have found applications in areas like optimization and the compression of artificial intelligence models.However, these methods have a well-known structural limitation: tensor networks are only efficient at emulating quantum systems when there is a limited level of entanglement between their qubits.Consequently, while these techniques can offer computational advantages in specific contexts, they are approximations based entirely on classical computing resources. Because they do not operate on physical quantum states, they do not directly reproduce genuine quantum phenomena quantum advantage.
S
Superposition
In the classical world, a system is in a well-defined state (for example, a coin is either heads or tails). In the quantum world, the information we have about a system is compatible with it being in a combination of multiple states at the same time, until it is measured. Each state has a weight or percentage to which it can “collapse” during the measurement process.
Simulator
It is a device designed to emulate the quantum phenomena of a specific physical system. Unlike a universal quantum computer, it is not generally programmable; meaning it cannot run a wide variety of algorithms or protocols. Its function is limited strictly to replicating the behavior of the quantum system for which it was created. The quantum simulator runs on a specific-purpose quantum computer, which means it can provide a quantum advantage. The concepts of emulator and simulator are often confused in the field.
T
T1 (Relaxation Time)
T1 is the time it takes for a qubit to spontaneously fall from its excited state back to its ground state, losing its stored energy. It is a measure of how long a qubit can hold the information it was given before that energy dissipates into the environment.
Longer T1 times mean the qubit retains its state for longer, allowing more operations to be performed before errors accumulate. T1 is one of the main figures of merit for qubit quality.
T2 (Dephasing Time)
T2 measures how long a qubit can maintain the phase of its quantum superposition before losing coherence. While T1 describes energy loss, T2 describes a subtler process: the qubit’s internal quantum state gradually loses its well-defined phase, making interference effects essential to quantum computation unreliable.
T2 is always shorter than or equal to T1. It is the more demanding of the two timescales to preserve, and improving it is one of the central challenges in superconducting qubit engineering.
TLS (Two-Level Systems)
Two-Level Systems (TLS) are microscopic defects that currently limit the coherence times of superconducting qubits. They are uncontrolled imperfections, typically found at the interface between the superconducting material and the substrate or in the oxide layer of the Josephson junction, that couple to the qubit and absorb its energy unpredictably.
Because of TLS defects, qubits can lose their quantum state faster than expected, and the defect pattern changes between cooldowns. Understanding and minimizing TLS is one of the central challenges in superconducting quantum hardware.
Z
ZX-Calculus
ZX-Calculus is a graphical language for reasoning about quantum circuits. Instead of equations, it represents quantum operations as colored diagrams (nodes of one of two colors connected by wires) that can be simplified using a set of rewriting rules.
Its main application is quantum circuit optimization: a circuit is converted into a ZX-diagram, simplified step by step, and then converted back into a standard quantum circuit with fewer operations. This makes it a useful tool for quantum compilers aiming to run circuits more efficiently on real hardware.