What is quantum-centric supercomputing?

A quantum-centric supercomputer is a next-generation connection of a quantum computer with a classical supercomputer that uses error mitigation and error correction algorithms to yield results in practical runtimes.

In the era of quantum computing, quantum-centric supercomputing is predicted to help researchers make major breakthroughs in the fields of material sciences, machine learning, generative AI, high energy physic and more, potentially ahead of large-scale fully quantum systems.

A fully realized quantum-centric supercomputer uses advanced middleware to integrate quantum circuits with classical computing resources. Quantum-centric supercomputers based on the IBM Quantum System Two™ architecture—the building blocks of quantum-centric supercomputing—combine quantum technology with traditional supercomputers to complement and improve the performance of both elements. 

In 1994, MIT mathematician Peter Shor discovered an algorithm that can divide large numbers into prime factors exponentially faster than the best classical algorithms, by using a hypothetical quantum computer. Two years later, Lov Grover discovered a quantum algorithm that can search a database faster than a classical search algorithm. These discoveries greatly accelerated interest in quantum computing. 

Shor and Grover proved, at least in theory, that a useful quantum computer can process certain complex workloads faster than classical methods—hundreds of thousands of years faster. Even the most advanced supercomputers in the world, like those used in high-profile data centers and universities, are simply not able to process large quantum workflows fast enough. 

No longer theoretical, quantum processors like IBM Quantum Heron have proven the viability of quantum computing. However, today’s quantum computers are limited by obstacles such as the number of qubits they can process with and errors innate to quantum hardware.

Quantum-centric supercomputing combines the strengths of quantum and classical computing, by using the unique properties of qubits to perform calculations that are otherwise infeasible for classical systems. This approach aims to overcome the limitations of classical high-performance computing by introducing quantum computers into existing workflows, thereby enhancing the computational efficiency and capability of both types of systems.

The following are some of the main differences between HPC and quantum-centric supercomputing:

Traditional HPC:

• Built on classical computer architecture
• Limited by binary processing and linear scalability

Quantum-centric supercomputing:

• Includes quantum computers to use quantum and classical resources in parallelized workloads
• Optimized to orchestrate work across the quantum computers’ and HPC’s compute clusters in the same data center or in the cloud
• Offers potentially exponential speedups and processing power greater than either quantum or classical computing can provide for certain problems

As experimental quantum computing continues to advance rapidly, we predict that quantum-centric supercomputing will be a pivotal bridge to achieving quantum advantage—the milestone by which researchers measure if a quantum machine can outperform classical hardware simulating a quantum system or any other classical methods for solving a practical problem. However, quantum computing is not expected to fully replace classical computing. Instead, quantum-centric supercomputers combine quantum computers and classical computers, with each type of system working together to run computations beyond what’s possible on either alone.

Globally, multiple supercomputer facilities have already begun to incorporate quantum-computing hardware, including Germany’s Jupiter, Japan’s Fugaku and Poland’s PSNC. As part of the IBM Quantum Roadmap, IBM hopes to build quantum-centric supercomputers with thousands of logical qubits by 2033.

Unlike traditional computers, quantum computers use the fundamental qualities of quantum physics to potentially solve complex problems. The four key principles of quantum computers are as follows:

• Superposition: Superposition is the state in which a quantum particle or system can represent not just one possibility, but a combination of multiple possibilities.
• Entanglement: Entanglement is the process in which multiple quantum particles become correlated more strongly than regular probability allows.
• Decoherence: Decoherence is the process in which quantum particles and systems can decay, collapse or change, converting into single states measurable by classical physics.
• Interference: Interference is the phenomenon in which entangled quantum states can interact and produce more and less likely probabilities.

While classical computers rely on binary bits (zeros and ones) to store and process data, quantum computers can encode even more data at once using quantum bits (qubits) in superposition. 

A qubit can behave like a traditional bit and store a value of either a zero or a one, but its power comes from its ability to store superpositions: a weighted combination of zero and one at the same time. When combined, a set of qubits in superposition can store more information than the same number of bits. However, each qubit can only output a single bit of information at the end of the computation. Quantum algorithms work by storing and manipulating information in a way inaccessible to classical computers, which can provide speedups for certain problems.

Controlling qubits requires delicate hardware that is sensitive to interference and must be kept at extremely cold temperatures. Quantum researchers use cryogenic refrigeration to keep qubits at temperatures colder than the void of space. 

Currently, quantum hardware is expensive, large and error-prone. While researchers work daily to address the challenges of building larger quantum computers, quantum computing is not expected to completely replace traditional computing anytime soon, or potentially ever. That's because quantum computing is best suited for certain complex problems.

In a matter of minutes, a quantum computer can potentially solve a simulation problem that would take a traditional supercomputer hundreds of thousands of years. This performance speedup, known as the quantum advantage, has only yet been proven theoretically. However, IBM quantum computers have already demonstrated quantum utility, the ability to solve problems at a scale beyond brute force classical simulation. 

Quantum computing is built on the principles of quantum mechanics, which describe how subatomic particles behave differently from macrolevel physics. But because quantum mechanics provides the foundational laws for our entire universe, on a subatomic level, every system is a quantum system.

For this reason, we can say that while conventional computers are also built on top of quantum systems, they fail to take full advantage of quantum mechanical properties during their calculations. Quantum computers take better advantage of quantum mechanics to conduct certain calculations that even high-performance computers cannot. 

Classical computation models use strings of binary digits (bits) to reduce all information into binary code composed of zeros and ones. Using a set of simple logic gates, like AND, OR, NO and NAND, we can process that information to perform advanced calculations. However, each logic gate can only act on one or two bits at a time. We determine the “state” of a classical computer is based on the states of all its bits. Classical computers use transistors and semiconductors to store and process binary information. 

Quantum computers use a special kind of quantum hardware called a quantum processing unit (QPU) to store and process data differently. Classical computers use transistors to store bits of information, but quantum computers use qubits typically made of quantum particles (those that behave like the smallest known building blocks of the physical universe). Unlike traditional bits, qubits hold more than two states of information. 

While a digital computer can be in just one state, the qubits of a quantum computer can be in many logical states at once during a computation. This phenomenon is known as superposition—a third position representing zero, one and all the positions in between based on a probability. At the end of the calculation, each qubit will assume the value of zero or one with a probability corresponding to their contribution to the superposition.

Different types of qubits are better for different use cases and systems. IBM uses superconducting qubits favored for speed and precise control. Qubits made from photons (individual light particles) are commonly used in quantum communication and quantum cryptography. Other types of qubits include trapped ions, neutral atoms and single electrons held by small semiconductors known as quantum dots.  

At the heart of a quantum-centric supercomputer is the quantum processing unit (QPU). IBM’s QPU includes the hardware that takes inputs and outputs circuits, as well as a multilayer semiconductor chip etched with superconducting circuits. It’s these circuits that contain the qubits used to perform calculations and the gates that perform operations on them. The circuits are divided into a layer with the qubits, a layer with resonators for readout and multiple layers of wiring for input and output. The QPU also includes the interconnects, amplifiers and signal-filtering components.

The type of physical qubit used by IBM is made of a superconducting capacitor wired to components called Josephson junctions behaving like lossless, nonlinear inductors. Because of the superconducting nature of the system, the current flowing across Josephson junctions can only assume specific values. The Josephson junctions also space out those specific values so that only two of those values are accessible.

The qubit is then encoded in the lowest two values of the current, which then become zero and one (or as a superposition of both zero and one). Programmers change the qubit states and couple qubits together with quantum instructions, commonly known as gates. These are a series of specially crafted microwave waveforms. 

To keep the qubits operating at the required temperature, some of the QPU components must be held inside a dilution refrigerator, which keeps them cold using liquid-helium. Other QPU components require room-temperature classical computing hardware. Then, the QPU is connected to runtime infrastructure, which also does error mitigation and results processing. This is a quantum computer.

The integration of quantum and classical systems is achieved through middleware and hybrid cloud solutions that facilitate seamless interaction between the two. This hybrid approach helps ensure that quantum processing units can be effectively used within quantum computers connected to existing computational frameworks, maximizing their impact without needing a complete overhaul of current infrastructures.

Despite recent advancements, controlling qubits is a major challenge. External noise and cross-talk between control signals destroys the fragile quantum properties of qubits, and controlling these noise sources has been key in furthering the development of useful quantum-centric supercomputers. 

Alongside hardware improvements, researchers have demonstrated the ability to deal with some noise by using error-mitigation algorithms that analyze how system noise changes program outputs. Researchers use this information to create a noise model, and then use classical computing to reverse engineer a noise-free result based on the model’s predictions. Quantum error mitigation is part of the continuous path that will take today’s quantum hardware to tomorrow’s fault-tolerant quantum computers.

In the following video, IBM Quantum researchers Andrew Eddins and Youngseok Kim explain the crucial role that error mitigation will play in achieving useful quantum computing in the near term.

Unlike error mitigation, where postprocessing fixes noise after a computation, quantum error correction can remove noise in real-time during processing, without the need to create a specific noise model first. Though effective to a point, error mitigation is limited in scale. As quantum circuits increase in complexity, error correction remains effective in large-scale systems.

Quantum error correction requires numerous resources, such as more qubits and more gates in a circuit. Computing with more qubits requires many more qubits for error correction. Better hardware and better error-correcting codes are bringing error correction closer to reality. Earlier this year, IBM published a new kind of error-correcting memory that can conceivably be implemented on near-term quantum computers.

Circuit knitting is a technique that breaks down one quantum-computing problem into multiple problems and then runs them in parallel on different quantum processors. Quantum and classical computers precisely combine the individual circuit results together to come to a conclusive result. Circuit knitting allows quantum researchers to run quantum circuits much more efficiently by incorporating classical computing with quantum processing. 

Common conceptions of “quantum computers” often envision a single QPU, using millions of physical qubits, to run programs independently. “Instead,” writes VP of Quantum and IBM Fellow Jay Gambetta, “we envision computers incorporating multiple QPUs, running quantum circuits in parallel with distributed classical computers.” Another technique relies on classical computing for most of the calculation, saving just the most quantum piece for the quantum processor.

Reaching large-enough scales to solve problems with quantum computers require error correction plus larger QPUs or multiple connected QPUs. In addition to Qiskit, IBM’s full-stack quantum-computing software for running quantum workloads, IBM is also developing the middleware to manage accurate circuit knitting and the dynamic provisioning of computing resources.

Quantum computers excel at solving certain complex problems with the potential to speed up the processing of large-scale data sets. From the development of new drugs to supply-chain optimization to material science and climate change challenges, quantum computing might hold the key to breakthroughs in several critical industries.

• Pharmaceuticals: Quantum computers capable of simulating molecular behavior and biochemical reactions can massively speed up the research and development of new, life-saving drugs and medical treatments.

• Chemistry: For the same reasons quantum computers might impact medical research, they might also provide undiscovered solutions for mitigating dangerous or destructive chemical byproducts. Quantum computing can lead to improved catalysts that enable petrochemical alternatives or better processes for the carbon breakdown necessary for combating climate-threatening emissions.

• Machine learning: As interest and investment in artificial intelligence (AI) and related fields like machine learning ramps up, researchers are exploring whether some quantum algorithms might be able to look at datasets in a new way, providing a speedup for some machine learning problems.

Quantum computers, as they exist today, are scientific tools useful for running specific programs beyond the brute-force ability of classical simulations—at least when simulating certain quantum systems. However, for the foreseeable future, quantum computing will work in tandem with modern and future classical supercomputing to be useful. In response, quantum researchers are preparing for a world where classical supercomputers can use quantum circuits to help solve problems.

The key challenges facing quantum-centric supercomputing include maturing the middleware that allows classical and quantum computers to communicate, as well as general challenges facing quantum computers themselves. Before achieving quantum advantage, developers have identified the following key obstacles to overcome.

A fully realized large-scale quantum computer requires millions of physical qubits. However, practical hardware constraints make scaling single chips to these levels prohibitively challenging. As a solution, IBM is developing next-generation interconnects capable of shifting quantum information across multiple chips. This solution provides a modular scalability to reach the required qubits needed to perform error correction. IBM plans to demonstrate these new interconnects—called l-couples and m-couplers—with proof-of-concept chips called Flamingo and Crossbill, respectively. These couplers are responsible for scaling chips. IBM plans to demonstrate c-couplers by the end of 2026 with a chip called Kookaburra. These are responsible for assisting with error correction.

While quantum processors relying on qubits used in quantum computing have the potential to massively outperform bit-based processors, current quantum processors can only support a few potential qubits. As research progresses, IBM plans to introduce a quantum system with 200 logical qubits capable of running 100 million quantum gates by 2029, with a goal of 2,000 logical qubits capable of running 1 billion gates by 2033.  

Although powerful, qubits are also quite error-prone, requiring large cooling systems capable of creating temperatures lower than outer space. Researchers are developing ways to scale qubits, electronics, infrastructure and software to reduce footprint, cost and energy usage.

Qubit coherence is brief, but integral, for generating accurate quantum data. Decoherence, the process in which qubits fail to function properly and produce inaccurate results, is a major hurdle for any quantum system. Quantum error correction requires that we encode quantum information into more qubits than we would otherwise need. In 2024, IBM announced a landmark new error-correcting code about 10 times more efficient than prior methods. While error correction is not a solved problem, this new code marks a clear path toward running quantum circuits with a billion logic gates or more. 

Quantum advantage requires two components. The first is viable quantum circuits, and the second is a means to demonstrate that those quantum circuits are the best way to solve a quantum problem over any other state-of-the-art method. Quantum algorithm discovery is what will take current quantum technologies from quantum utility to quantum advantage. 

The crux of quantum algorithm discovery relies on a highly performant and stable software stack to write, optimize and execute quantum programs. IBM’s Qiskit is by far the most widely used quantum software in the world. It is Python-based and composed of open source SDK and supporting tools and services—useful for executions both on IBM’s fleet of superconducting quantum computers and on systems that use alternative technologies, such as ions trapped in magnetic fields or quantum annealing.