Insider Brief
- Quantum computing encompasses multiple hardware approaches built on different physical foundations, and the field can be understood along two practical dimensions: system reliability and physical architecture.
- Most current systems operate in the NISQ (Noisy Intermediate-Scale Quantum) regime, where limited qubit counts and error rates constrain large-scale, fault-tolerant computation.
- Leading hardware modalities – including superconducting circuits, trapped ions, photonics, and neutral atoms – each present distinct engineering trade-offs in scalability, speed, stability, and infrastructure requirements.
Quantum computing is often discussed as if it were a single technology. Underneath that label, however, are multiple hardware approaches built on very different physical foundations – from superconducting circuits to trapped ions and photonic systems, all fall under the same umbrella – but the engineering behind them differs significantly.
The field can be understood along two practical dimensions: system reliability and hardware architecture. Together, these axes provide a more practical way to understand where the field stands and where it is heading.
But, first here are some foundational terms to ensure that key concepts are defined consistently for the discussion ahead.
The Core Concept of Quantum Computing
A quantum computer is a computational system that uses principles of quantum mechanics to process information.
Quantum computers operate on quantum bits, or qubits. Qubits obey the laws of quantum physics, which allow them to behave in ways classical bits cannot.
Unlike a classical bit, which exists strictly as 0 or 1, a qubit can exist in a superposition meaning it can theoretically represent both possibilities at the same time. A common analogy is a spinning coin – while in motion, it is not strictly heads or tails. Only when measured does it resolve into one outcome.
This does not mean quantum computers simply do everything at once. Instead, they carefully adjust the likelihood of different possible answers. Through interference effects, they amplify correct outcomes and suppress wrong ones. For certain classes of problems, this allows quantum algorithms to explore solution spaces more efficiently than classical systems.
The Fragility of Quantum States
Superposition gives quantum systems their computational potential, but only as long as coherence is preserved.
Quantum coherence refers to the ability of a qubit to preserve the precise relationships within its quantum state.
The difficulty is that qubits are extremely sensitive to their environment. Temperature changes, stray electromagnetic fields, and material defects can disrupt their state. When this happens, decoherence sets in, and the qubit loses its quantum behavior.
As a result, coherence time becomes a practical limitation. It defines the short time interval during which a quantum computer can perform reliable computations before errors begin to overwhelm the system.
Entanglement – How Qubits Work Together
Entanglement happens when two or more qubits become linked so their states are no longer independent. One can’t think about each qubit separately; the system behaves as one combined state.
When one qubit is measured, the other immediately adjusts to match. For example, if the first qubit shows 0, the second qubit will also be 0. If the first shows 1, the second becomes 1. This instant correlation is what Einstein called “spooky action at a distance” though it doesn’t transmit information faster than light.
This connected behavior gives quantum computers their edge. By linking qubits, they can handle many combinations of information at once, performing complex calculations in parallel that classical computers cannot match.
Axis One – System Reliability
Earlier, we discussed coherence and how fragile quantum states can be. That fragility directly shapes how today’s quantum computers operate. One practical way to understand the current landscape is through reliability.
Most current systems operate within what is known as the NISQ era.
The NISQ Era – Where Quantum Computing Stands Today
The term Noisy Intermediate-Scale Quantum (NISQ) was introduced by physicist John Preskill in 2018 to describe the systems available today. It reflects a simple reality: current quantum computers are limited in size and prone to errors. They can perform controlled quantum operations, but they cannot yet run long, reliable computations at scale.
To understand what this means, it helps to break the term down.
What Noisy Means
‘Noise’ refers to errors that arise from decoherence. Quantum states are fragile and even small disturbances introduce inaccuracies in operations. As circuits grow deeper – meaning more operations are performed in sequence – these errors accumulate.
The result is a practical limitation – computations must remain short and carefully designed. Longer, more complex algorithms quickly become unreliable. This restricts the types of problems NISQ systems can realistically address.
What Intermediate-Scale Means
‘Intermediate-scale’ refers primarily to the number of physical qubits available in today’s systems. Several leading platforms, including those developed by IBM and Google, now operate processors in the hundreds and in some cases exceeding a thousand – physical qubits. In late 2025, QuantWare announced delivery of a 10,000-qubit processor, marking another step in raw hardware scaling.
Yet these are physical qubits, not fully error-corrected logical qubits. In practice, usable qubits with strong connectivity and high fidelity are fewer than headline numbers suggest. There is also a trade-off at play. Increasing qubit count often introduces additional control and stability challenges. More qubits do not automatically mean better performance if error rates remain high.
There have nevertheless been technical milestones – most notably Google’s 2019 quantum supremacy experiment. In that demonstration, a quantum processor completed a highly specialized sampling task faster than the best-known classical methods at the time.
Still, benchmark demonstrations are not the same as sustained, commercially meaningful performance. In areas such as optimization, chemistry, and machine learning, NISQ devices have not yet shown a scalable advantage over classical systems.
At the same time, the field is not stagnant. As Chris Coleman, condensed matter physicist and consultant to The Quantum Insider, notes:
“The NISQ era is quantum computing’s version of the first wave of A.I. Although still needing to overcome limitations, in many instances we’re seeing the foundation being laid for bigger things to come and there is no doubt that the field is making steady progress. This can be seen across the ecosystem.”
Importantly, most existing quantum computers – regardless of whether they rely on superconducting circuits, trapped ions, or photonic systems – are considered NISQ devices.
Fault Tolerance – Where Quantum Computing Aims to Go
A fault-tolerant quantum computer is designed to operate correctly even in the presence of errors. Because qubits are extremely sensitive to their environment, they are prone to decoherence and operational noise. Fault tolerance, in general terms, refers to the ability to detect and correct these errors continuously, allowing computations to proceed reliably over long durations.
Although some groups report early demonstrations of fault-tolerant behavior, these remain limited in scope. Meaningful fault tolerance must scale beyond isolated experiments. It requires systems capable of sustaining extensive logical operations across many qubits, not just demonstrating short-lived corrections.
Challenges in Achieving Fault Tolerance
Several challenges explain why this remains a long-term goal:
- Limited numbers of physical qubits – Building a single logical qubit requires substantial redundancy, meaning many physical qubits are needed to create one stable unit of computation. Even with recent hardware advances, this dramatically limits how many logical qubits can be realized in practice.
- Physical error rates – Error correction only works effectively below certain performance thresholds. Today’s systems are approaching these thresholds, but maintaining consistently low error rates across larger devices remains difficult.
- Engineering complexity – Scaling fault-tolerant architectures requires precise control, dense connectivity, advanced cooling systems, and tight integration between quantum and classical hardware. Coordinating these elements reliably presents substantial systems-level challenges.
- Error decoding – it must occur continuously and at high speed creating significant computational overhead on classical control systems. If decoding cannot keep pace with error generation, corrections may be applied too late, allowing faults to accumulate and compromise the computation.
Current Status and Outlook
There has been tangible progress. Research groups have demonstrated small logical qubits and improvements in error correction fidelity. These milestones indicate that the foundational principles are sound.
However, large-scale, commercially useful fault-tolerant systems do not yet exist.
Industry expectations vary. Some anticipate that achieving full fault tolerance will require sustained effort over many years. Others argue that accelerating hardware improvements could shorten that timeline. Roadmaps across major players reflect this divergence.
In general, fault tolerance is best understood not as a separate type of quantum computer, but as a different level of reliability. The same underlying hardware approaches – superconducting circuits, trapped ions, photonic systems, and others – are all attempting to move toward this objective.
Axis Two – Physical Implementation
So far, we’ve focused on reliability. Another way to frame the industry is by looking at how different quantum machines are physically built. While most systems today operate in the NISQ regime, they differ significantly in how their qubits are created and controlled.
Superconducting Quantum Computers
In broad terms, superconducting quantum computers use tiny electrical circuits to form qubits. These circuits are made from materials that become superconducting at extremely low temperatures. To operate properly, they are cooled close to absolute zero. Once cooled, microwave signals are used to control the qubits and carry out computations.
One common way to understand the appeal of this architecture is speed. Superconducting qubits can generally execute operations very quickly. The approach also benefits from established semiconductor-style fabrication techniques, allowing companies to draw on existing manufacturing knowledge.
From an engineering standpoint, these systems are often viewed as highly integrable and compatible with chip-based scaling strategies. This has contributed to steady increases in qubit counts over recent years.
However, there are trade-offs. These systems require sophisticated cryogenic equipment and are sensitive to small disturbances. As more qubits are added, maintaining uniform performance becomes harder.
Companies building superconducting quantum computers include IBM, Google, Rigetti Computing, IQM, and Oxford Quantum Circuits.
Trapped-Ion Quantum Computers
Trapped-ion quantum computers, in general terms, use charged atoms as qubits. These ions are held in place using electromagnetic fields inside a vacuum chamber and controlled with laser pulses that perform quantum operations.
This approach is commonly associated with stability. Trapped-ion qubits tend to maintain their quantum properties for longer periods compared to several other platforms, which supports longer coherence times and enables the execution of more complex algorithms.
In terms of engineering, the qubits are naturally uniform and well understood, which simplifies calibration. These systems also avoid the extreme cryogenic cooling required by some other platforms.
The trade-off typically appears in speed and scaling. Gate operations are generally slower, and controlling large chains of ions introduces growing complexity as systems expand.
Companies developing trapped-ion quantum computers include IonQ, Quantinuum, and Alpine Quantum Technologies.
Photonic Quantum Computers
Photonic quantum computers use particles of light also known as “photons” as qubits. These systems rely on optical components such as beam splitters, phase shifters, and detectors to manipulate light and perform computations.
One way to frame this architecture is through its natural compatibility with communication networks. Photons can move through fiber networks with relatively low disturbance, making the approach attractive for quantum communication and distributed computing. Some photonic systems can operate without large cooling systems.
However, photon loss remains a central challenge. Managing and synchronizing individual photons reliably at scale is difficult, and implementing high-fidelity two-qubit gates is particularly challenging because photons do not naturally interact strongly with each other. As a result, many photonic approaches rely on probabilistic gates or measurement-based techniques, which increase resource overhead and make error correction more complex.
Companies pursuing photonic quantum computing include PsiQuantum, Xanadu, Quandela, Orca Computing, and Quix Quantum.
Neutral Atom Quantum Computers
Neutral atom quantum computers use individual atoms with no net electric charge as qubits. These atoms are trapped using focused laser beams and arranged in programmable arrays, where their quantum states can be controlled with additional laser pulses.
One way to frame this architecture is through its scalability. In their ground state, neutral atoms interact only weakly with one another, which reduces unintended coupling between qubits. This makes it easier to organize large, orderly arrays. The ability to rearrange atoms within these arrays further enhances architectural flexibility.
Unlike superconducting systems, neutral atom platforms do not require dilution refrigerators operating at millikelvin temperatures. Instead, they rely on laser cooling to reach microkelvin temperatures within ultra-high vacuum environments. This avoids the extensive cryogenic infrastructure required by some other quantum computing approaches.
However, scaling neutral atom systems introduces engineering challenges. As arrays grow larger, maintaining precise laser control and consistent performance across all qubits becomes increasingly difficult. Gate operations are also generally slower than in superconducting systems, partly because atoms may need to be repositioned to enable interactions. Improving speed and reliability at scale remains an active area of development, and several approaches have been proposed to address these limitations.
Companies building neutral atom quantum computers include Atom Computing, Pasqal, Infleqtion and QuEra Computing.
The Hardware Landscape
Beyond the principal hardware architectures discussed above, additional approaches – including quantum dots, topological qubits, and quantum annealing systems – are also under development. Some extend gate-based architectures, while others represent alternative computational models. In most cases, these approaches remain earlier in maturity or are optimized for more specialized use cases.
A central question, however, is whether one hardware model will ultimately dominate.
The presence of several viable hardware strategies introduces complexity for investors and enterprise adopters. In classical computing, things were messy at first too – but eventually everyone standardized around a few main architectures. That standardization made it easier to scale, invest, and build a whole industry around it. Quantum computing might follow the same path – lots of experimentation now, then consolidation around the best approach later.
According to a report by TechTarget, Carl Dukatz, global lead of the quantum program at Accenture, suggested that history may point toward eventual consolidation.
“If we look at history, it tells us that there will generally be one that’s selected as the way, the preferred device, simply for the economies of scale,” Dukatz said.
That uncertainty is reflected across the ecosystem.
Michael Biercuk, founder and CEO of Q-CTRL, whose firm develops quantum control infrastructure across platforms, offers a similarly measured view.
“Each modality has its own strengths and weaknesses,” he said. “We don’t have a favorite.”
A Practical Perspective
For now, quantum computing remains in a formative stage. Large-scale fault tolerance has not yet been achieved, and engineering maturity varies across platforms. Advancements in error correction, operational reliability, and scalable manufacturing processes are likely to shape long-term outcomes more decisively than theoretical distinctions alone. Leaders seeking to deepen their understanding may consult published roadmaps and technical updates from IBM Quantum, Google Quantum AI, and Quantinuum. Ongoing reporting and industry briefings from The Quantum Insider also provide structured coverage of developments across modalities.
