IBM's Quantum Completes Task in 19 Seconds, Estimated at 110 Years for Supercomputer
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IBM's Quantum Completes Task in 19 Seconds, Estimated at 110 Years for Supercomputer

The Nighthawk r2 quantum processor, developed by IBM, managed to execute a computational task in just 19 seconds. Researchers estimate that the same operation would require approximately 110 years if performed on a traditional supercomputer.

This experiment was conducted using the company's cloud quantum computing platform. The test employed a methodology known as Random Circuit Sampling (RCS), a technique designed to verify the potential benefits of quantum computers compared to conventional machines. It is important to note that this study is still awaiting peer review and is available on arXiv.

The Nighthawk r2 is equipped with 120 qubits, which function as the fundamental unit of information in quantum systems, analogous to bits in common machines. For this trial, scientists selected 61 of these qubits and executed random circuits with progressive complexity, reaching up to 40 operation cycles.

The most notable performance was observed with 36 cycles. In this specific configuration, the circuit utilized 918 two-qubit gates, which are operations responsible for modifying and combining the states of these quantum units. It was in this scenario that the processor generated one million samples in only 19 seconds.

A relevant aspect of the test lies in its execution. The team chose not to use specially calibrated experimental equipment for the research, opting instead for the standard operational flow of the IBM cloud quantum computing platform. This approach simplifies the process of replicating the experiment, as the researchers made the codes, samples, and circuits used in the analysis available for other users to verify the results.

The comparison with supercomputers was not done by directly running the same program on a classical machine, as this would be unfeasible. Instead, the team applied a technique called tensor network contraction, used to calculate the workload a traditional computer would need to employ to replicate the behavior of the quantum processor.

Details of the Comparison Calculation

The calculation revealed that generating the 1 million samples would require about 1.2 × 10^27 computational operations, an extremely high number of individual calculations. Based on a more cautious estimate of the sustained performance of Frontier, the researchers arrived at a projection of approximately 110 years of processing time.

However, this disparity must be analyzed with caution. The result depends on the method chosen to perform the simulation and does not establish an absolute limit to the capacity of classical computing. The researchers themselves admit that more optimized algorithms could reduce the workload required to reproduce the experiment. Thus, the study's conclusion is not that classical computers will never reach Nighthawk r2, but rather that, in this specific performance test, the quantum processor demonstrated a significant advantage.

Additionally, the demonstration stands out for having occurred on commercially accessible hardware, rather than on apparatus developed solely for laboratory environments. To ensure the work can be audited and repeated, the authors made the necessary details available. Nevertheless, the work still needs to pass through the peer review process, meaning that until then, the finding represents proof of quantum advantage on commercial hardware, but its conclusions remain open to scientific community evaluation.

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New test compares quantum computers and shows how far they are from practical application
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New test compares quantum computers and shows how far they are from practical application

A new benchmark developed to compare quantum computers on a unified scale revealed significant differences between advanced machines and demonstrated that the technology still lags considerably behind performing some of the calculations for which it was created.

This test, named Quantum Universal Operation Performance System (QUOPS), was created by a team led by Sandia National Laboratories in the United States. Researchers applied this benchmark to systems from Google, IBM, and Quantinuum to measure not only the speed of the machines but also the size of the corresponding quantum circuits they can successfully execute.

The most important result was that the best performance recorded on physical hardware was 1824 QUOPS. However, tasks considered serious challenges for quantum computing require between 250 million and 340 million QUOPS.

This means that current quantum computers need to increase their computational power by approximately 100 thousand times to reach such a level of application.

Comparison of Architectures and Performance

The tests showed noticeable discrepancies between the analyzed computers, which is related to the architectures used to create them. Helios-1, an ion trap processor from Quantinuum, managed the most complex calculations among the evaluated systems, achieving a result above 1500 QUOPS. The advantage of this architecture lies in the flexible placement of qubits, allowing them to interact with any other qubit. Nevertheless, moving ions takes time, making the system relatively slow.

Superconducting processors from Google and IBM use a different approach. In these, qubits are organized in rigid lattices on chips, which limits the connection between certain qubits. Consequently, these systems showed results around 200 QUOPS. On the other hand, superconducting machines are significantly faster; Google's Willow processor reached a speed of 20 million operations per second.

Limitations of Speed Measurement

The results also illustrate why measuring only the speed of a quantum computer can be misleading. A machine may perform operations very quickly but fail to implement a sufficiently large circuit to solve a useful problem. This is the difference that QUOPS aims to capture.

The potential of quantum computing is linked to extremely complex problems, such as cracking RSA-2048 cryptography, widely used in digital security, and modeling complex molecules for chemical applications. Performing these calculations requires approximately 250 to 340 million QUOPS, whereas the highest result on physical hardware currently stands at only 1824 QUOPS.

Thus, there is a difference of about five orders of magnitude in practice between the current capability and what is required to solve these problems. Overcoming this huge gap will require progress in various aspects of the technology, including a significant reduction in error rates, an increase in the number of physical qubits, or the development of more efficient quantum computer architectures.

The authors conclude the complexity of the task, stating that computational power must increase by five orders of magnitude, emphasizing the importance of fault-tolerant approaches to quantum computing. The comparison also shows that there is no clearly superior architecture in all assessed aspects: Helios-1 executed larger circuits but slower, while Google and IBM achieved much higher operating speeds but with lower QUOPS scores. These differences are directly related to the choices made during the development of each system.

For researchers, having a common standard can help more transparently track the evolution of quantum computing. Instead of analyzing isolated metrics, such as the number of qubits or the speed of specific operations, QUOPS aims to measure the actual ability of machines to execute circuits of a corresponding size. The goal is to allow scientists and other technology developers to monitor the progress of systems and independently verify their computational capabilities. The study presenting this benchmark, titled 'Benchmarking the computational power of quantum computers', was published on the arXiv preprint server. The article 'Novo teste coloca computadores quânticos lado a lado e revela quanto falta para máquinas úteis no mundo real' first appeared in Olhar Digital.

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