The Surprisingly Cheap Purchase Price Of A Quantum Computer. How Much Do Quantum Computers Cost In 2024? (2024)

Like in the early days of classical computing, devices cost millions of dollars. But now we can have computers that are millions of times more potent in the form factor of a smartphone and available for just a few hundred dollars. But how much do Quantum Computers cost, and can you buy one?

Prices of a Quantum Computer?

Before we discuss the prices of quantum computers, we need to state there are so many differing technologies that researchers have used to build the devices. Some devices use qubit technologies that range from superconducting to photonic. However, one thing that all these devices have in common is their cost. That is, they are expensive, in fact, super expensive. Despite the devices only having a few hundred qubits at most, they can typically cost millions, just like those early computers at the start of the digital revolution.

Despite their often multi-million dollar price tag, you won’t find a device in your office or workplace soon (unless you work in the burgeoning quantum sector). Most quantum computers are connected to cloud services that allow users to log in remotely and run circuits from anywhere on the planet. These services also contribute to the cost of running a quantum computer—that remote or cloud access.

Quantum Computers Coming to a … Fridge Near You

One of the leading technologies researchers use to develop qubit devices relies on cooling and refrigeration. Circuits from the likes of Rigetti and IBM need low temperatures to operate. This is another reason quantum computers come with a hefty price tag. IBM has recently reached 433 qubits in its superconducting quantum computer. However, while such a device is not for sale, we can put a price tag on it, likely in the hundreds of millions.

Superconducting circuits are made from materials that exhibit zero electrical resistance at extremely low temperatures, usually close to absolute zero. These circuits can carry an electric current indefinitely without dissipating energy, making them ideal for maintaining the delicate quantum states of qubits. Josephson junctions, which are thin insulating barriers between superconducting materials, are often used to control the quantum behavior of these circuits.

Semiconductor Qubits: Scaling the price down

One of the most compelling advantages of semiconducting qubits for quantum computing is their cost-effectiveness. Unlike superconducting qubits, which require extremely low temperatures close to absolute zero to maintain their quantum states, semiconducting qubits can operate at slightly higher temperatures. This reduces the need for expensive cryogenic cooling systems, making the overall setup more affordable.

Additionally, semiconducting qubits can be fabricated using existing semiconductor manufacturing techniques, which are well-established and relatively inexpensive compared to the specialized processes needed for superconducting circuits. This means that the infrastructure for large-scale production of semiconducting qubits is already in place, potentially lowering the barrier to entry for new players in the quantum computing field.

Another financial advantage comes from the potential for better scalability with semiconducting qubits. As quantum computing technology progresses, the need for more qubits to perform increasingly complex calculations grows. Semiconducting qubits, often made from materials like silicon, can be miniaturized more quickly than their superconducting counterparts.

This miniaturization is crucial for packing more qubits onto a single chip, thereby increasing computational power without a proportional increase in cost. Leveraging existing semiconductor technology for miniaturization could lead to more cost-effective scaling solutions in the long run.

Lastly, there is compatibility with semiconducting qubits such as those developed by Intel. Existing semiconductor technology offers hybrid systems integrating classical and quantum components on the same chip. This could result in more efficient data transfer between the classical and quantum parts of the system, reducing the need for separate, specialized hardware and thereby lowering costs. Such hybrid systems could be particularly advantageous for applications requiring pre- and post-processing quantum calculations, as they would allow for seamless integration with classical computing resources, again leveraging the cost benefits of established semiconductor manufacturing processes.

A Desktop Quantum Computer for a price of $5,000

SpinQ Technology, a Chinese company specializing in quantum computing, has recently introduced a desktop quantum computer priced at $5,000. This new model is a more affordable and compact version of their SpinQ Gemini, priced at $50,000. The quantum computer price reduction is significant and aims to make the technology more accessible for educational purposes.

The SpinQ machine operates on only two qubits and utilizes nuclear magnetic resonance for processing. The technology involves trapping selected molecules in a magnetic field and manipulating them with radio frequency pulses. Although the process traditionally required expensive superconducting magnets, SpinQ managed to lower the quantum computer price by using permanent magnets and a technique called “shimming” to maintain a strong magnetic field.

The SpinQ machine comes with various features that contribute to its price. These include instrument calibration, nuclear magnetic resonance spectroscopy, and a quantum computing interface. It also has a task management and dynamic library module supporting cloud computing and APIs, all factoring into the quantum computer price.

Quantum Computer Prices in General

Most developers of quantum devices do not publish the cost of their machines. The key is to reduce the price of quantum computers to as low as possible and make them as ubiquitous as possible. However, some academic literature has discussed the costs and prices of the various parts of the quantum stack, which we won’t go into here as developments and innovations constantly affect the cost.

Eventually, the qubit count will increase, and the cost per qubit will decrease. But the price per qubit could likely range at the moment of anywhere between $2500, so for a thousand qubits (not possible with the Spin Q device), we could estimate that a machine with 1,000 qubits would have a lower bound cost of $2,500,000. But this simple argument is floored in so much as not every technology scales the same way, and in reality, a more helpful machine from IBM with 433 qubits already likely costs many times that. But as is the nature of scaling, whatever the price, the price per qubit will fall, and quantum computers will get cheaper.

Quantum Computers don’t need to get cheaper; they get more usable.

But that neglects the fact that price isn’t a key consideration at the moment. Moreover, the sheer usefulness of quantum computers needs to be proven first. Naturally, as scaling improves and qubit count increases, we can expect the price per qubit to fall while the machine’s price may initially increase. Eventually, the cost of additional qubits will likely become marginal. Then, just like conventional devices, the entire price of a Quantum Computer can fall, and who knows, maybe they’ll be as ubiquitous as your smartphone with a price tag to match.

One of the key aspects of enhancing usability is the development of robust software and development tools. Platforms like IBM’s Qiskit, Microsoft’s Quantum Development Kit, and Google’s Cirq provide frameworks that allow researchers and developers to write quantum algorithms more easily. These tools abstract much of the underlying complexity of quantum hardware, making it more accessible to those without deep expertise in quantum mechanics.

Quantum computers are inherently prone to errors due to decoherence and noise. Improving error correction techniques and developing more stable qubits are crucial for making quantum computers more reliable. Advances in quantum error correction codes and fault-tolerant quantum computing architectures will help ensure that quantum computations yield accurate and reliable results, thereby enhancing their usability for real-world applications.

The integration of quantum and classical computing resources in hybrid systems can significantly enhance usability. By leveraging the strengths of both types of computing, users can tackle complex problems more efficiently. For instance, hybrid algorithms can use classical processors for tasks they handle well and reserve quantum processors for specific parts of the problem where they offer significant advantages. This approach makes it easier to incorporate quantum computing into existing workflows.

Developing intuitive user interfaces and cloud-based quantum computing platforms is another critical step towards usability. Services like IBM Quantum Experience, Azure Quantum, and AWS Braket provide user-friendly interfaces that allow users to access quantum computers remotely. These platforms offer educational resources, tutorials, and examples that help users understand how to utilize quantum computing effectively, thus lowering the barrier to entry.

Quantum Computing, Lessons from History

Mainframes and modern-day quantum computers share several similarities, despite their significant differences in technology and application. Both types of systems represent advanced stages of their respective eras’ computing capabilities, offering powerful processing abilities for specific, high-demand tasks. Here are a few key similarities:

Both mainframes and quantum computers are designed with specialized hardware and architecture to handle specific types of computing tasks efficiently. Mainframes were developed to process large volumes of transactions and data with high reliability, using complex, custom-built components. Similarly, quantum computers are built using unique quantum bits (qubits) and quantum gates, designed to perform quantum calculations that can solve specific problems faster than classical computers.

Both systems require substantial initial investments in terms of hardware, infrastructure, and ongoing maintenance. Mainframes are known for their high cost, both in terms of purchase and operation, necessitating specialized environments and personnel. Quantum computers also demand significant financial resources for their development, maintenance, and operation, including specialized cooling systems to maintain quantum coherence and environments isolated from external noise and interference.

Mainframes and quantum computers are both utilized in critical and specialized applications where high performance and reliability are paramount. Mainframes are used in sectors like banking, insurance, and government for large-scale transaction processing and database management. Quantum computers are being explored for applications in optimization, cryptography, material science, and complex simulations, where their unique capabilities can provide significant advantages over classical computing methods.

Access to both mainframes and quantum computers is typically limited to large organizations, research institutions, and enterprises due to their complexity and cost. Mainframes have historically been centralized resources accessible to specific users within an organization. Quantum computing services, while increasingly available via cloud platforms like IBM Quantum, Azure Quantum, and AWS Braket, are still primarily accessed by specialized users and researchers due to the need for advanced knowledge to develop and run quantum algorithms effectively.

Just as mainframes have been integrated into broader IT ecosystems, quantum computers are being integrated into hybrid quantum-classical systems to leverage their strengths alongside classical computing resources.

Reliability and high uptime are critical for both mainframes and quantum computers. Mainframes are renowned for their robustness and ability to run without interruption for long periods, which is essential for applications requiring consistent and reliable performance. Quantum computers, while still in developmental stages, also emphasize reliability and stability to ensure accurate quantum operations and maintain the coherence of qubits during computations.

In the early 1940s, IBM’s president,Thomas J Watson, underestimated computer demand. Will the same happen for quantum computers? Back then, computers that would only compute simple calculations that could be done on a pocket calculator took a room full of equipment. For the most part, Quantum Computers also take up an entire room full of hardware – certainly those from IBM, which use superconducting as the technology of the qubits and require cryogenic cooling.

QCaaS. Quantum Computing as a Service

Quantum Computing as a Service (QCaaS) is an emerging paradigm that allows users to access quantum computing resources over the cloud. This service model is analogous to classical cloud computing, providing users with remote access to quantum hardware and software platforms without the need for them to own or maintain quantum computers. QCaaS enables researchers, developers, and businesses to experiment with quantum algorithms and applications, thereby democratizing access to cutting-edge quantum technology. It supports a variety of use cases, including optimization problems, cryptography, and machine learning, by leveraging the unique capabilities of quantum processors.

IBM Quantum is a pioneer in the QCaaS landscape. Through its IBM Quantum Experience platform, the company offers access to a suite of quantum computers, ranging from smaller, experimental devices to more advanced systems with higher qubit counts. IBM Quantum provides a robust set of tools and resources, including Qiskit, an open-source quantum computing software development framework. This platform supports educational initiatives and fosters a global community of researchers and developers. IBM Quantum’s strategy includes not only providing access to quantum hardware but also advancing the development of quantum algorithms and applications, thereby contributing significantly to the progress of the quantum computing field.

Azure Quantum, developed by Microsoft, is another key player in the QCaaS market. Azure Quantum integrates quantum computing into the broader Azure cloud ecosystem, offering a range of quantum solutions and resources. Microsoft’s approach is unique in that it supports multiple quantum hardware providers, including IonQ, Honeywell, and QCI. This multi-vendor strategy allows users to choose from different quantum technologies, each with its own strengths. Azure Quantum also features the Quantum Development Kit (QDK), which includes Q#, a specialized programming language for quantum computing. By embedding quantum capabilities within Azure, Microsoft enables seamless integration with classical computing resources and a variety of enterprise applications.

AWS Braket, Amazon Web Services’ quantum computing service, provides a comprehensive environment for exploring and developing quantum algorithms. Braket offers access to quantum processors from D-Wave, IonQ, and Rigetti, allowing users to experiment with different types of quantum systems. The platform supports hybrid quantum-classical workflows, enabling the combination of quantum computing with AWS’s extensive classical computing infrastructure. AWS Braket is designed to be user-friendly, with a focus on providing the tools necessary for both beginners and experts to advance their quantum computing projects. By integrating with other AWS services, Braket facilitates the deployment of quantum-enhanced applications in a variety of industries.

In summary, QCaaS platforms like IBM Quantum, Azure Quantum, and AWS Braket are transforming the accessibility and application of quantum computing. Each platform offers unique features and capabilities, catering to a wide range of users and use cases. These services are instrumental in driving the advancement of quantum computing technology and fostering innovation across multiple sectors. As quantum computing continues to evolve, QCaaS will play a crucial role in enabling researchers and businesses to leverage the power of quantum processors without the need for significant upfront investment in hardware and expertise.

Initial Use-Cases for Quantum Computers

Optimization problems are among the most promising early use cases for quantum computers. Many industries face complex optimization challenges that classical computers struggle to solve efficiently. Quantum computers, leveraging algorithms like the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), can potentially find solutions more quickly and accurately. Applications range from optimizing supply chains, traffic flow, and financial portfolios to improving manufacturing processes and logistics.

Cryptography is another critical early use case for quantum computing. Quantum computers have the potential to break widely-used encryption methods, such as RSA and ECC, by employing Shor’s algorithm, which can factor large integers exponentially faster than the best-known classical algorithms. This poses a significant threat to current cybersecurity protocols. Conversely, quantum computing also offers new cryptographic techniques, such as quantum key distribution (QKD), which can provide theoretically unbreakable encryption, enhancing data security and privacy.

Drug discovery and molecular simulation represent a transformative use case for quantum computing in the pharmaceutical and chemical industries. Quantum computers can simulate molecular interactions at an atomic level with much higher precision than classical computers. This capability can accelerate the discovery of new drugs, materials, and chemicals by allowing researchers to model complex molecules and reactions accurately, reducing the time and cost associated with experimental trials.

Financial modeling and risk analysis are areas where quantum computing can make a substantial impact. Financial institutions deal with highly complex and uncertain environments, requiring sophisticated models to predict market behaviors, manage risks, and optimize trading strategies. Quantum algorithms can handle large datasets and complex calculations more efficiently, providing better insights and enabling more effective risk management and decision-making processes.

Artificial intelligence and machine learning are also set to benefit from quantum computing. Quantum algorithms, such as quantum neural networks and quantum-enhanced machine learning models, can potentially process vast amounts of data more efficiently than classical algorithms. This can lead to significant advancements in various AI applications, including natural language processing, image recognition, and predictive analytics. Quantum-enhanced AI can improve the performance and speed of learning algorithms, leading to more accurate and robust AI systems.