Quantum computer vs. supercomputer: Key differences

What is a supercomputer?

Supercomputers are thousands or even millions of individual computers that work together to process data on a much faster scale than regular computers. For example, the world's fastest supercomputer -- HP's Frontier -- processes 1 quintillion (1 billion billion) calculations per second, in contrast to a regular computer, which processes up to 500 billion calculations per second.

This huge computing power helps supercomputers predict weather forecasts in minutes, something that would take a regular computer years to accomplish due to the massive amounts of data and complex calculations involved. Supercomputers also help to explore space, improve nuclear weapons and aviation, test new chemical compounds and fine-tune cryptology, among other applications. As supercomputers became more affordable in the 1990s, more businesses began to use them for tasks such as simulation, modeling and big data analytics, including market research. However, supercomputers face challenges with power consumption and performance.

What is a quantum computer?

Quantum computers process information using quantum bits -- also known as qubits -- instead of the binary bits (0s and 1s) of classical computers, enabling quantum computers to perform at least a million computations in the time it takes a conventional computer to process just one. Their incredible computational power comes from two key principles:

1. Superposition. In superposition, qubits exist in simultaneous states, meaning a single qubit can represent, for instance, 90% 0s and 10% 1s at the same time. This contrasts with classical computing, where bits operate sequentially.
2. Entanglement. Here, qubits spin in unison rather than functioning independently, as in classical computing. This enables quantum processors to generate a continuum of possibilities rather than one fixed output. For example, 50 qubits can represent more than 1 quadrillion (1,125,899,906,842,624) states simultaneously.

These two features enable quantum computers to solve certain problems far faster than supercomputers. For instance, Google's Sycamore processor was able to solve a complex mathematical problem in just 200 seconds, while the same problem would take the world's fastest supercomputer 10,000 years to complete. However, quantum computing is still in its early stages, with challenges in error correction, qubit stability and scalability limiting its practical applications.

Comparing quantum computers and supercomputers

Quantum computers and supercomputers are both powerful computing systems, but they differ fundamentally in how they process information and solve problems. Supercomputers use thousands or millions of classical processors to perform parallel calculations at high speeds. They are designed for tasks requiring massive data processing, such as weather forecasting, molecular simulations and AI implementations.

Quantum computers, on the other hand, apply the principles of quantum mechanics -- specifically superposition and entanglement -- to process information in ways that classical computers cannot. This capability enables them to solve complex problems exponentially faster than supercomputers for specific applications, such as cryptography, optimization and quantum simulation.

For example, Google claims that its Sycamore quantum computer can process the same information that would take the world's fastest supercomputer, Frontier, more than 47 years to complete in just a little over six seconds.

Using quantum as a service vs. high-performance computing as a service

Quantum as a service (QaaS) and high-performance computing as a service (HPCaaS) are cloud-based technologies that provide access to advanced computational power without requiring organizations to invest in expensive infrastructure:

• HPCaaS. High-performance computing as a service gives users access to powerful computing resources for tasks such as data analysis, simulations and research. For example, Microsoft uses supercomputers to run its Weather Research and Forecasting model across 100,000 processors simultaneously, delivering near-instant, highly accurate weather predictions. Results can be accessed and replicated by 40,000 Microsoft users worldwide who contribute back to the code, achieving a level of collaboration unattainable with any single on-premises machine.
  
  Top cloud providers offering HPCaaS include Amazon Web Services (AWS), Microsoft Azure, Google Cloud, IBM Cloud and Oracle Cloud Infrastructure. One of the limitations with HPCaaS is its high cost of use, as accessing powerful computing resources on demand can become expensive, especially for long-term or large-scale projects.
• QaaS. Quantum as a service lets users run algorithms on quantum computers remotely, employing qubits and the principles of quantum mechanics to tackle complex problems such as optimization, cryptography and molecular modeling. For example, IBM's 127-qubit quantum platform aims to help users "accelerate their experiments faster and with less effort," according to an IBM blog post.
  
  Major QaaS providers include AWS, Microsoft Azure, IBM, Google Cloud, Rigetti and Honeywell, offering processors that range from 50 qubits to over 100 qubits. In some ways, QaaS can be cheaper than HPCaaS because users avoid frequent hardware updates. On the other hand, its issues of data security and control of sensitive information are problematic. QaaS is especially valuable in industries such as logistics, pharmaceuticals and finance, where solving complex optimization problems quickly can provide a significant competitive advantage.

Quantum cloud computing models

Since quantum needs an extremely cold environment -- around minus 270 degrees Celsius -- and special conditions, the only reasonable approach for quantum businesses is to host services in the cloud.

QaaS, which is also known as quantum computing as a service (QCaaS), has two delivery options:

1. Serverless quantum computing. Users pay only for resources and storage, while cloud vendors handle infrastructure and maintenance. This makes the service not only cheaper, since users are only charged for the time the code runs, but faster as well, since it enables developers to focus on code instead of also having to concentrate on servers. Drawbacks include potential vendor lock-in, latency from function delays and debugging issues. IBM's Qiskit Serverless is an example of this model.
2. Hybrid quantum-classical computing. This model combines the strengths of quantum computing, such as quantum parallelism and entanglement, with the power and flexibility of classical computing. One real-world example is using quantum for intensive calculations and classical computing to fine-tune the results. A key drawback is the latency between classical and quantum systems, which can range from 10 to 20 minutes depending on the distance between the two computers. Microsoft's Azure Quantum offers hybrid quantum computing as part of its cloud delivery service.

Ultimately, choosing between quantum as a service and high-performance computing as a service depends on the problem at hand. QaaS is ideal for research and innovation in quantum-advantaged fields but is not yet widely applicable for general computing needs. HPCaaS remains the best choice for large-scale, high-speed classical computation, offering reliability and performance for a broad range of applications. For many users, a hybrid approach that integrates quantum and high-performance computing might provide the most effective solution.

Key applications of supercomputers

Supercomputers play an important role in the field of computational science and are used for a wide range of computationally intensive tasks in various fields, including the following:

• Weather forecasting. The Derecho supercomputer analyzes the effects of releasing sun-blocking aerosols into the stratosphere on thunderstorms and rainfall patterns.
• Space exploration. NASA employs the Aitken supercomputer to simulate models for sustaining long-term human presence on the moon.
• Molecular modeling. In 2022, Stanford researchers used supercomputers to reduce a genome sequencing process that used to take 13 years to just over five hours.
• Physical simulations. Supercomputers have run simulations of the early moments of the universe, airplane and spacecraft aerodynamics, the detonation of nuclear weapons and nuclear fusion. In aviation, GE Aerospace uses the Frontier supercomputer to test whether it can reduce carbon dioxide emissions by over 20%.

Supercomputers are also indispensable in cryptanalysis, which involves breaking encryption through high-speed parallel computations, brute-force attacks and the analysis of complex cryptographic algorithms.

Key applications of quantum computers

Quantum computing has already been used to address critical problems in the following fields:

• Cybersecurity. Quantum cryptography uses quantum mechanics to secure and transmit data so it cannot be hacked. It encodes information in tiny particles called photons. If someone intercepts the data, the quantum state of the photons changes, which makes hacking detectable.
• New pharmaceutical treatments. Quantum computers excel at handling and processing large amounts of data, making quantum processing invaluable for drug research. For example, scientists at Alveolix use quantum computing to enable faster processing of genetic and genomic data, helping prevent side effects, reduce complications and minimize the need for animal experiments.
• Improved logistics operations. Quantum computing could improve logistics operations and energy efficiency by solving complex optimization problems more quickly and accurately. For example, quantum algorithms have helped increase the speed and capacity of cargo movement at the Port of Los Angeles, enabling the facility to operate more efficiently and with reduced risk.
• Faster and more accurate forecasting. Quantum computing can simulate real-time systems that take too much compute power on classical computers. For example, Spanish quantum AI startup Multiverse Computing collaborates with Bosch to predict supply chain failures and optimize maintenance schedules in real time.
• Machine learning. Multiverse Computing claims quantum computing has the potential to run machine-learning algorithms with 25 times less electricity.

Quantum computing could someday also revolutionize fields such as climate modeling, financial optimization and materials science by solving complex problems more efficiently and accurately.

Problems with supercomputers

Supercomputers face four major obstacles:

• Power consumption. Supercomputers require massive amounts of electricity, leading to high operational costs and environmental concerns.
• Data movement. Moving large data sets between processors and memory can slow computations, limiting performance despite powerful hardware.
• Hardware failures. Complex components lead to frequent failures and maintenance issues.
• Parallel programming. With supercomputers, thousands of processing cores need to operate simultaneously. This requires special programming methods to make sure all parts work together efficiently without the slowdown or breakdown of any one machine. Supercomputers are also extremely expensive to build, maintain and operate. They also require specialized knowledge to operate and are vulnerable to cyberthreats.

Problems with quantum computers

Quantum computers have their own challenges, including the following:

• Fragility. Any perturbation, such as a slight vibration or change in temperature, could cause disentanglement, disintegrating the entire fabric and causing stored information to be lost.
• Scalability. Quantum computers need tens of thousands of top-notch qubits to be effective. These qubits also must be error-free. It would take a long time -- maybe 10 years or more, according to a recent academic review of quantum cloud computing -- to build a problem-free quantum computer with enough qubits to run significant problems. At Zurich's technical university, professor of theoretical physics Renato Renner said the work has barely begun.
• Data security. Public key encryption, which keeps data safe, can be broken by quantum computers. Quantum computers are much faster than regular computers at solving certain problems, so they can potentially unlock data that was thought to be secure. If bad actors use quantum computers, sensitive information could be exposed.
• Physical infrastructure. Today's quantum computers are roughly the size of a car because of the cooling equipment that prevents chips from overheating. More advancements in refrigeration are needed to improve feasibility and scalability.
• Manufacturing. A standardized system is needed to manufacture quantum computers at scale. Current quantum cloud providers use proprietary software platforms and development toolkits.
• Cost and accessibility. Quantum computers are expensive and require highly specialized environments to operate. Additionally, finding skilled personnel is challenging and costly, as quantum computing demands expertise in quantum mechanics, computer science and engineering.

Future of quantum computers and supercomputers

Since Google introduced its Sycamore quantum processor in 2019, a growing number of tech companies -- including Microsoft and IBM, along with nation-states such as China -- have invested heavily in the field. Small to midsize businesses are also turning to QaaS startups to gain first-mover advantage and explore this powerful technology.

Although quantum computing is still in its early stages, it is expected to become an essential tool in scientific research, enabling the resolution of problems once deemed impossible. Some proponents say that by 2026, quantum computers will be able to perform tasks that classical computers can't. However, for quantum computing to go mainstream, it will need to be integrated seamlessly with classical systems.

As regards supercomputers, the future lies in exascale computing that will be able to perform more than 1 quintillion calculations per second, powering even more breakthroughs in fields including AI, climate modeling and drug discovery. Energy efficiency should improve through the use of neuromorphic computing -- a type of computing modeled on the human brain -- specialized chips and better cooling technologies. Cloud computing will make supercomputing power more accessible, while advancements in parallel programming will enable it to execute multiple tasks simultaneously. New materials, such as graphene and photonic processors, could revolutionize the hardware, making supercomputers faster and more compact.

Finally, supercomputers could integrate quantum processes to become quantum supercomputers able to solve complex problems neither technology could handle alone, leading to breakthroughs across industries.

Leah Zitter, Ph.D., is a seasoned writer and researcher on generative AI, drawing on over a decade of experience in emerging technologies to deliver insights on innovation, applications and industry trends.