Quantum Computing Challenges Holding Back Practical Quantum Computers - The Quantum Insider

Insider BriefQuantum computing has attracted billions in investment, sustained media attention, and some of the most concentrated scientific talent on a single engineering problem. Progress has been significant, but the results so far have yet to match the scale of investment and research behind the field.To date, no quantum computer has outperformed a classical computer on a commercially valuable problem. Current systems operate in what researchers call the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by devices with enough qubits to demonstrate quantum behavior but too much noise to run useful algorithms reliably. The gap between where the hardware is and where it needs to be is not a matter of incremental improvement. It involves solving several distinct problems, each of which is difficult on its own and harder in combination.This article covers what those problems are, how serious each one is, and what it would take to solve them.A qubit stores information in a quantum state, a superposition of 0 and 1 that allows quantum computers to process information differently from classical machines. That state is fragile. Any interaction with the surrounding environment can collapse the superposition into a definite 0 or 1, ultimately, destroying the quantum information it held.This process is known as decoherence. It sets a hard time limit on every quantum computation. The coherence time, how long a qubit maintains its quantum state, varies by hardware. Superconducting qubits typically hold coherence for microseconds to milliseconds. Trapped ions can maintain coherence for seconds under careful isolation. Neutral atom arrays demonstrated 13-second coherence times at the 6,100-qubit scale in a September 2025 Caltech demonstration.Every operation a quantum computer performs must be completed within that window. An algorithm requiring more operations than the coherence time allows cannot run reliably. This single constraint shapes the design of every quantum algorithm, every error correction scheme, and every piece of quantum hardware built today.Qubits must be isolated from their environment to preserve coherence, but they must also be controlled and measured, which requires interacting with them. Those interactions are also pathways for noise. Every hardware approach in quantum computing is a different attempt to manage this tradeoff. TQI’s guide to the types of quantum computers covers how each approach handles it differently.A classical processor might experience one error in 10^17 operations. Current quantum computers have gate error rates between roughly 1 in 100 and 1 in 1,000. That difference spans many orders of magnitude.Errors come from multiple sources. Decoherence gradually corrupts states over time. Control pulses that manipulate qubits are imperfect, and small inaccuracies in timing, amplitude, or frequency introduce errors with each operation. Measurement can produce incorrect readings. Neighboring qubits can interfere with each other through unwanted interactions called crosstalk.Errors build on each other. In a quantum computer with a thousand operations being done sequentially, there will be so many errors that the end result will be nonsense unless some kind of correction is done. This is why current quantum computers are limited to short, shallow circuits.Classical error correction works through redundancy, storing multiple copies of data and using majority voting to detect mistakes. Quantum mechanics prohibits this. The no-cloning theorem prevents copying unknown quantum states, so quantum error correction takes a fundamentally different approach.Quantum error correction encodes the information of a logical qubit across multiple physical qubits, allowing errors to be detected and corrected without directly measuring the quantum information. The approach works in principle, but building systems that can perform this reliably at scale remains a major challenge. In December 2024, Google’s Willow chip demonstrated below-threshold error correction, showing that adding physical qubits to a surface code can reduce logical error rates. In June 2025, Quantinuum reported a universal, fully fault-tolerant quantum gate set with repeatable error correction on trapped-ion hardware.Overheads are still significant. Current estimates suggest creating one reliable logical qubit may require hundreds to thousands of physical qubits, depending on error rates and the correction code used. A quantum computer needing 1,000 logical qubits for a useful algorithm might need millions of physical qubits. TQI’s guide to quantum error correction covers the techniques and the companies working on this.Scaling a quantum computer is not like adding servers to a data center. The engineering challenges compound as systems grow, and solving one problem can make another worse.Each qubit requires precise control signals to perform operations. As qubit counts increase, the number of control lines, signal generators, and associated electronics grows with them. Managing thousands or millions of control channels while keeping each one precise enough not to introduce errors is an unsolved problem at production scale.Superconducting quantum computers operate inside dilution refrigerators at temperatures near absolute zero. Each qubit needs wiring to connect it to control and readout electronics at room temperature. Routing thousands of wires into a cryogenic environment while managing heat load and physical space is one of the most concrete engineering bottlenecks in the field. The heat generated by the wiring itself can exceed what the cooling system can remove.Building quantum computers requires fabricating qubits with consistent properties. Variations in manufacturing cause qubits to differ in frequency, coherence time, or error rate. For superconducting systems in particular, achieving the uniformity needed across a chip with thousands of qubits while maintaining quality at each one remains difficult.Packing more qubits into a processor can increase unwanted interactions between neighboring qubits. An operation on one qubit may inadvertently disturb others, introducing additional errors into the computation. Increasing the distance between qubits can reduce crosstalk, but it also makes the system physically larger and adds to the wiring and cooling challenges.These challenges are closely connected. Solving one scaling problem can introduce another constraint, making the path from today’s hundreds or thousands of qubits to the millions required for fault-tolerant systems difficult to achieve.Even with perfect hardware, quantum computing faces software challenges that limit what the technology can do.Quantum computers can provide advantages for specific types of problems. Shor’s algorithm for factoring, Grover’s algorithm for search, and quantum simulation algorithms for modeling quantum systems are among the main areas where quantum speedups have been demonstrated or established theoretically. For many other problems, no quantum algorithm has shown a meaningful advantage over the best classical methods. Developing new quantum algorithms also requires expertise in both quantum computing and the specific problem being solved, so turning theoretical results into practical applications remains difficult.Quantum programming differs fundamentally from classical programming. Algorithms must account for superposition, entanglement, and the probabilistic nature of measurement. Debugging is particularly difficult because observing a quantum system’s state destroys the superposition being examined. Frameworks including Qiskit, Cirq, and PennyLane have made quantum development more accessible, but the tools remain immature relative to classical software ecosystems.Before a quantum algorithm can run, it must be compiled into operations that match the constraints of the target hardware. The compiler has to account for which qubits can interact, which gates the processor supports natively, and how to reduce the number of operations that expose the computation to errors. Poor compilation can add unnecessary operations and consume valuable coherence time, reducing the performance of the final circuit. The quantum computing workforce reached approximately 16,500 professionals globally in 2025, according to QED-C’s State of the Global Quantum Industry 2026 report, an increase of roughly 2,000 workers in a single year. TQI’s economic impact analysis projects 250,000 quantum-sector jobs by 2030 and 840,000 by 2035.The supply side cannot meet that trajectory with current training pipelines. A PhD in a relevant field takes four to five years, and quantum computing competes with AI, biotech, and semiconductor sectors for the same physics and engineering graduates. Riverlane’s QEC Report 2025 estimates only 1,800 to 2,200 quantum error correction specialists exist worldwide. The field needs 5,000 to 16,000 by 2030. Roughly 50 to 66% of quantum job openings currently go unfilled.The interdisciplinary requirement makes the problem worse. Quantum computing needs physicists who understand engineering, engineers who understand quantum mechanics, and computer scientists who understand both. That combination is rare in a single person and difficult to assemble in a team. A Forbes analysis published in August 2026 argued that people, not capital, may be quantum’s real bottleneck, noting that no country can meet its quantum workforce needs domestically.Keeping this in view, more universities are launching quantum programs, which is helping produce people who can enter the industry directly.

Chicago Quantum Exchange analysis of more than 5,000 job postings found that 55% required only a bachelor’s degree or less. The field is opening to a wider talent pool, but the training pipeline still lags demand. TQI’s quantum computing jobs and salaries guide covers the roles, compensation, and entry paths in detail.Each challenge affects the timeline on a different horizon.Decoherence and error rates are improving through better hardware and error correction techniques. Progress is steady and accelerating. Milestones from Google and Quantinuum in 2024 and 2025 showed the principles of fault tolerance working on real hardware, and industry roadmaps now converge on the late 2020s for early fault-tolerant systems. Scaling may prove the longest constraint to resolve. Moving from thousands to millions of qubits requires new approaches to control, wiring, cooling, and manufacturing that go beyond incremental improvement. These are decade-scale engineering problems.Software and algorithm development proceeds in parallel with hardware, but the limited number of algorithms with clear quantum advantage means that even a perfect quantum computer would outperform classical systems only on specific problem types.The workforce gap interacts with all three. Solving hardware, scaling, and software problems simultaneously requires people that the field does not yet have in sufficient numbers.Industry roadmaps point toward 2028 to 2029 for early fault-tolerant demonstrations and the early to mid-2030s for systems capable of commercially valuable computation. These timelines carry significant uncertainty. What they share is directional agreement that the field is moving toward fault tolerance, and that the challenges described above are what stands between current hardware and that goal.For readers looking to go deeper, TQI’s guide to the types of quantum computers covers each hardware approach and its trade-offs; understanding quantum error correction covers the techniques being developed; and quantum computing jobs and salaries in 2026 maps the workforce landscape.Decoherence and error rates. Quantum states lose their information through interaction with the environment, and quantum operations introduce errors at rates many orders of magnitude higher than classical computers. Correcting these errors requires hundreds to thousands of physical qubits per logical qubit. Scaling to the system sizes needed for practical applications while maintaining low error rates remains the central obstacle.Each additional qubit adds control complexity, wiring requirements, heat load, manufacturing demands, and crosstalk potential. These problems compound rather than scale linearly. Solving them at the level of millions of qubits may require fundamentally new engineering approaches rather than incremental improvement on current designs.Coherence times range from microseconds to seconds depending on the hardware. Superconducting qubits typically maintain coherence for microseconds to milliseconds. Trapped ions can hold coherence for seconds under isolation. Neutral atom systems have demonstrated 13-second coherence at the 6,100-qubit scale. All computation must complete within this window before decoherence degrades the results.Quantum error correction encodes one logical qubit across many physical qubits, allowing errors to be detected and corrected without directly measuring the quantum information. Quantum computers make errors at rates far higher than classical computers, and without correction, algorithms involving many operations produce meaningless results. The challenge is the overhead: a useful quantum computer may need millions of physical qubits to support the thousands of logical qubits its algorithms require.No. The global quantum workforce was approximately 16,500 in 2025. Projections call for 250,000 by 2030. Roughly 50 to 66% of quantum job openings currently go unfilled. The field requires expertise spanning physics, engineering, computer science, and mathematics, and finding individuals who bridge these disciplines is difficult. Training programs are expanding, but the pipeline is not keeping pace with demand.Industry roadmaps target 2028 to 2029 for early fault-tolerant demonstrations. Systems capable of commercially valuable computation beyond classical reach may take until the early to mid-2030s. Applications in quantum simulation for chemistry and materials science may arrive sooner. These timelines carry substantial uncertainty, and depend on progress across hardware, software, error correction, and workforce development simultaneously.TopicsShare Get the latest research, company news, and market intelligence every week. MENTIONED IN THE ARTICLEGoogle is a global technology powerhouse, founded in 1998, that is actively expanding its footprint into aerospace. The company pioneers AI driven satellite telemetry analysis, collaborates with the The Aerospace Corporation, researches space weather forecasting, and tests orbital data center concepts like Project Suncatcher. Google’s Project Suncatcher is an aerospace "moonshot" exploring orbital data centers. It envisions constellations of solar-powered satellites operating in sun-synchronous Low Earth Orbit (LEO).Quantinuum is a quantum computing company advancing the aerospace sector through the development of algorithms for aerodynamic modeling, composite materials optimization, and sustainable aviation fuel cell engineering. Founded in 2021 through the merger of Cambridge Quantum and Honeywell Quantum Solutions, the firm provides high fidelity trapped ion hardware and software to accelerate industrial applications.More in Research