Noisy Intermediate-Scale Quantum (NISQ) refers to quantum devices that contain tens to a few hundred qubits, but still suffer from significant noise and errors. These systems represent the current stage of quantum computing, acting as a transition between early experimental setups and future large-scale, fault-tolerant quantum computers. NISQ technology paves the way for the development of more advanced and reliable quantum technologies.
NISQ devices are mid-sized quantum systems that can handle meaningful quantum computations but still experience noise and errors during operation. They don’t yet support full error correction, which limits their accuracy and complexity. Despite these limitations, NISQ devices offer valuable opportunities to test and develop quantum algorithms and explore potential real-world applications.
NISQ devices are current-generation quantum computers with limited scale and stability, yet capable of meaningful quantum processing. Here are the main features of NISQ devices
They typically feature between tens and a few hundred qubits, enabling intermediate-scale quantum computations.
Qubits lose their quantum state quickly, limiting the time available for accurate computations.
Quantum operations often produce errors due to interference, hardware imperfections, and environmental factors.
They don’t yet support full error correction, making computations more error-prone and less reliable.
NISQ devices often work alongside classical systems, using hybrid algorithms to solve real-world problems more effectively.
The NISQ era in quantum computing began when it became evident that early quantum machines were limited by low qubit counts and high noise levels, mainly due to the lack of full error correction.
This phase built upon foundational theories developed in the 1980s and small-scale quantum algorithm experiments conducted in the late 1990s and early 2000s.
A major milestone was reached in 2019 when Google demonstrated quantum supremacy using its 53-qubit Sycamore processor, showcasing the capabilities of NISQ hardware even as researchers worked on finding real-world applications.
By 2024, quantum processors surpassed 1,000 qubits, with Atom Computing's 1,180-qubit processor and IBM's Condor leading the field. However, current NISQ systems still face tough challenges, such as high error rates and limited coherence, which affect their reliability for complex tasks.
Ongoing research is now focused on developing hybrid quantum-classical algorithms and error mitigation methods to harness meaningful results from these noisy systems across various industries.
NISQ devices are limited in terms of qubit counts and are noise-prone, so a selection of special-purpose algorithms that allow operations under these constraints is required. These particular illustrations can be found below.
VQE is a kind of quantum algorithm that is widely used in quantum chemistry and materials science to locate the lowest energy state of molecules. VQE begins by preparing a parameterized quantum state and subsequently utilizes a classical optimizer to minimize energy. One significant advantage of VQE is that it uses fewer quantum gates. Therefore, VQE is a good candidate for use with NISQ devices.
QAOA is explicitly built for combinatorial optimization problems. The problem types that QAOA aims at include max-cut and the traveling salesperson problem. QAOA can take large circuits that reflect a solution to an optimization problem and break them into much smaller components, allowing for a decrease in qubit use and the number of gates, thereby reducing noise.
While full-blown quantum simulations will require fault-tolerant machines, NISQ devices permit small-scale quantum simulations in domains like condensed matter and high-energy physics. These simulations typically leverage variational methods or Trotterization, and often include some error correction.
Quantum machine learning algorithms join quantum computing by interfacing with machine learning to derive improved facilitations for data processing and pattern recognition. For example, variational quantum classifiers and quantum neural networks use quantum circuits to run on datasets and then rely on classical optimizers to train.
Although NISQ computers have challenges like noise and a limited number of qubits, they still offer several important advantages over classical computers:
Quantum Advantage : With hundreds of qubits, NISQ devices can solve specific problems that are beyond the reach of classical computers, demonstrating a clear quantum advantage.
Driving Quantum Research : NISQ systems are crucial for exploring new quantum algorithms and understanding quantum system behavior, paving the way for more powerful and error-resistant quantum computers.
Improving Quantum Hardware and Software : Working with NISQ devices helps developers enhance qubit quality, reduce noise, and create better quantum control and software tools.
Advancing Machine Learning : NISQ computers show promise in accelerating specific machine learning tasks like data classification and clustering through quantum machine learning.
Enabling Hybrid Algorithms : The NISQ era has encouraged hybrid quantum-classical algorithms (e.g., VQE and QAOA) that split tasks between quantum and classical processors, optimizing the use of limited quantum resources.
Access to Real Quantum Hardware : NISQ computing allows testing and refining algorithms on actual quantum processors. This hands-on experience helps developers understand hardware challenges like qubit coherence and gate fidelity, essential for future progress in quantum technology.
While NISQ computers mark progress in quantum computing, several limitations and challenges hold back their full potential:
Noise and Lack of Fault Tolerance : NISQ devices are prone to errors due to noisy qubits and gates, and they lack full error correction, limiting the complexity and reliability of computations.
Limited Qubit Count and Coherence Time : Most NISQ systems have fewer than 1,000 qubits with very short coherence times, restricting the size and depth of quantum algorithms that can be executed reliably.
Scalability Challenges : As the number of qubits increases, noise also grows, making it challenging to scale NISQ systems for solving large, practical problems.
High Cost and Complex Infrastructure : Quantum hardware requires expensive, specialized infrastructure like cryogenic cooling and precise control systems, which raises costs and limits widespread adoption.
Lack of Standardization and Skilled Workforce : The absence of standardized software frameworks slows development, and there is a shortage of quantum computing experts, which together hinder rapid progress in the field.
NISQ computers are being investigated for real-world applications across a wide range of industries. Below are several key areas where NISQ technology will be impactful:
Quantum Chemistry and Materials Science : Although imperfect, NISQ systems can simulate molecular structure and chemical reactions, thus having the potential for healthcare and material scientists to unlock drug discovery, materials development, and even catalysis. As one small example, NISQ devices can model some complex molecules where the behavior of the molecules is difficult for classical computers to simulate, because of the complexity involved, exponential resources are needed for quantum-level accuracy.
Industry Optimizations : Industries like finance, transportation, logistics, and supply chain are investigating NISQ solutions to problems like portfolio optimization, traffic management, and production scheduling. As one example in finance, researchers have been experimenting with NISQ computers to help tackle complex, high-dimensional optimization tasks, including portfolio selection and risk management.
Quantum Machine Learning : Quantum ML algorithms implemented on NISQ devices are being created to analyze large datasets, fulfill predictive analytics applications, and resolve pattern classification problems. In healthcare, quantum machine learning's potential could rapidly diminish the time to analyze medical-based data, which would lead to faster and better diagnoses.
Cryptography : While NISQ computers do not yet have enough power to crack existing public-key cryptography, such as RSA, they do help further programs that examine post-quantum cryptography techniques and quantum key distribution schemes, intended to increase secure communication channels.
Scientific Research : Academics and industry researchers are using NISQ systems to study the fundamentals of physics, such as quantum chaos, entanglement, and quantum thermodynamics, to help further our understanding of quantum systems.
Battery and Energy Storage : NISQ computing is being used to model molecular quantum properties and electrochemical processes of materials used for batteries. Early studies will model lithium-containing molecules to gain atomic-level detail of the relevant processes, such as ion diffusion. These stimulating studies will modernize and develop new materials for modern batteries and other energy storage systems.
The future of NISQ (Noisy Intermediate-Scale Quantum) technology represents a crucial phase in quantum computing development. NISQ devices, with tens to hundreds of noisy qubits, enable exploration in quantum simulation, optimization, and complex problem-solving beyond classical capabilities. These machines are valuable for scientific research in quantum physics and practical uses in materials science, drug discovery, and logistics. However, they face limitations due to noise and lack of error correction.
Despite these challenges, NISQ serves as an essential stepping stone toward fully fault-tolerant quantum computers. Advances in qubit technology, error mitigation, and algorithms are expected to improve their performance and applications. Ultimately, NISQ devices pave the way for future quantum machines that can tackle larger, more complex tasks with higher precision and commercial impact.
NISQ devices represent the current stage of quantum computing, featuring tens to hundreds of noisy qubits. Despite limitations like errors and a lack of full error correction, they enable meaningful computations, hybrid algorithms, and real-world applications in chemistry, optimization, and machine learning. Serving as a crucial bridge, NISQ technology drives advances toward more powerful, fault-tolerant quantum computers.
NISQ stands for Noisy Intermediate-Scale Quantum, referring to quantum devices with tens to a few hundred qubits that experience noise and errors.
NISQ devices face noise, limited qubit counts, short coherence times, and lack full error correction, which restricts their accuracy and scalability.
NISQ computers can tackle specific tasks like quantum chemistry simulations, optimization problems, quantum machine learning, and small-scale quantum simulations.
NISQ devices are intermediate quantum machines with noise and limited error correction, while future quantum computers aim to be large-scale, fault-tolerant, and highly reliable.
Hybrid algorithms combine quantum and classical processing to optimize computations, helping overcome NISQ devices’ noise and limited qubit numbers for practical applications.