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A paper released during the SIGBOVIK 2024 conference details an attempt to simulate the IBM quantum utility experiment on a Commodore 64. The idea might seem preposterous - pitting a 40-year-old home computer against a device powered by 127-Qubit Eagle quantum processing unit (QPU). However, the anonymous researcher(s) conclude that the Qommodore 64 performed faster, and more efficiently, than IBMs pride-and-joy, while being decently accurate on this problem.

At the beginning of the paper, the researchers admit that their Qommodore 64 project is a joke, but, sadly for IBM, its proof of quantum utility was also built upon shaky foundations, and the Qommodore 64 team came up with some convincing-looking benchmarks. There was some controversy about IBMs claims at the time, and we are reminded it took just five days for the quantum experiment to be simulated on an ordinary MacBook M1 Pro laptop. The jokey Quantum Disadvantage paper (PDF link, headlining section starts at page 199) ports this experiment to a machine packing the far more humble MOS Technology 6510 processor.

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To get deep into the weeds with the quantum theory and math behind the quantum utility experiment, please follow the above PDF link. However, to summarize, the C64-based experiment uses the sparse Pauli dynamics technique developed by Begui, Hejazi, and Chan to approximate the behavior of ferromagnetic materials. Famously, IBM claimed such calculations were too difficult to perform on a classical computer to an acceptable accuracy, using the leading approximation techniques, recalls the paper. Not quite, and as already mentioned above, an ordinary laptop can obtain similar results.

The anonymous C64 user(s) provide some interesting details of their quantum-defeating feat. Their aggressively truncated and shallow depth-first search model used just 15kB of the spacious 64kB available on the iconic Commodore machine. Meanwhile, the final code consisted of about 2,500 lines of 6502 assembly, stored on a cartridge that fitted in the C64s expansion port. This code was handled by the mighty 1 MHz 8-bit MOS 6510 CPU. The C64 took approx 4 minutes per data point. (Testing the same code on a modern laptop achieved roughly 800s per data point.)

In conclusion, the researcher(s) asserts that the Qommodore 64 is faster than the quantum device datapoint-for-datapoint it is much more energy efficient and it is decently accurate on this problem. On the topic of how applicable this research is to other quantum problems, it is snarkily suggested that it probably wont work on almost any other problem (but then again, neither do quantum computers right now). Overall, it is difficult to know whether the results are entirely genuine, though a lot of detail is provided and the linked research references in the paper seem genuine.

We know many readers are retro computing enthusiasts, as well as DIYers and makers. So it is good to know that the author(s) of this paper say that they will provide source code to allow others to replicate their results. However, source code will only be supplied in one of three formats, they say: a copy handwritten on papyrus, a slide-show of blurry screenshots recorded on a VHS tape, or that I dictate it to you personally over the phone. So please add an extra pinch of salt to this story for that.

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Read more from the original source:
Commodore 64 claimed to outperform IBM's quantum system sarcastic researchers say 1 MHz computer is faster ... - Tom's Hardware

Editors Note: Why do qubit count and quality matter? Whats the difference between physical qubits and logical qubits? Quantum computer vendors toss these terms and numbers around as indicators of the strengths of their systems. For seasoned quantum computing watchers, the rationale behind the claims are well-known and appreciated. However, there are many who are new to the quantum information science and for whom a qubit count/quality 101 backgrounder can be helpful. Heres a brief explanation from Yuval Boger of QuEra Computing. BTW, QuEra has a nice glossary of quantum terms on its website.

In recent months, several quantum companies have made roadmap announcements with plans to reach 10,000 physical qubits in the next five years or sooner. This is a dramatic increase from the current 20 to 300 qubits, especially given that several of these companies have yet to release their first product.

What makes 10,000 qubits such an important milestone, and what will quantum computers be capable of once that number is reached?

The effort to achieve 10,000 physical qubits in quantum computing is more than a mere pursuit of quantity; it embodies strategic milestones toward unlocking the full potential of quantum computation. Broadly speaking, 10,000 physical qubits allow for the practical realization of over 100 logical qubits, essential for performing longer, more complex computations with a lower chance of errors. Below, I explain the important distinction between physical and logical qubits, the significance of reaching and crossing the 100 logical qubit threshold, and the varied path different quantum computing implementations take to get there.

While increasing the number of qubits is good, increasing the qubit quality is even more important. One key attribute of good qubits is the error rates associated with single- and two-qubit operations and the lifetime of a qubit. The error rate indicates how often qubit operations are successful. These might be operations on single qubits, such as flipping a qubit, or operations on two qubits, such as entangling them. The state-of-the-art in two-qubit operations is approaching 99.9% success. While 99.9% might sound great, this success rate implies that about 1 in 1,000 operations fail. Thus, if an algorithm requires several thousands of two-qubit operations, it will likely produce incorrect results. Truly useful algorithms require millions of such operations.

While pursuing 10,000 physical qubits is critical, its imperative to acknowledge that effective quantum error correction is necessary since it is unlikely that physical qubit error rates will sufficiently improve to enable these longer, more complex algorithms. This is where logical qubits come in. Logical qubits are a collection of physical qubits that address this problem. By cleverly spreading the information from a single qubit across several qubits, detecting and correcting many errors becomes possible. The exact way to do so and the number of physical qubits that are required to create a good enough logical qubit is an active area of research, but depending on the desired error rate and the selected qubit technology, dozens, hundreds, or thousands of physical qubits will be required to create one good fault-tolerant logical qubit.

The transition from noisy, physical qubits to fault-tolerant, logical qubits is not merely technical; its transformative, marking the difference between quantum computing as an experimental curiosity and a practical technological powerhouse. The leap toward 10,000 physical qubits is intrinsically aimed at enabling the construction of a significant number of logical qubits, with 100 being a critical milestone for demonstrating practical quantum advantage in various computational tasks.

One reason reaching 100 logical qubits is significant is the simulation limit. When simulating quantum algorithms, classical computers face exponential growth in computational requirements. Todays most powerful supercomputers can simulate quantum algorithms with about 50 perfect qubits. This is called the simulation limit. Thus, the ability to run algorithms with 100 logical error-corrected qubits would enter an exciting era where quantum computers far exceed the computational capabilities of classical machines while also certifying that the calculation results are accurate. Achieving 100 logical qubits would signify the transition from theoretical or small-scale experimental quantum computing to practical, impactful applications, heralding a new era of computational capabilities.

Imagine a plane with a range of 20 miles. Useful? Not really. Now imagine a plane with a 1,000-mile range. That would be useful for short-haul flights but not for longer trips. A plane with a 10,000-mile range? This is useful for most applications. Similarly, a 100-logical-qubit quantum computer can provide real business value for some applications, such as optimization or machine learning. Larger problems, such as molecular simulations, still require many more logical qubits. Those may require 1,000 logical qubits, while 4,000 logical qubits are expected to be required to crack RSA-2048.

Multiple paths to 10,000 qubits

The journey to 10,000 qubits is navigated through diverse quantum computing technologies, each with unique challenges and advantages:

Each of these technologies is on a unique path to overcoming their respective challenges, with the collective goal of achieving the scale necessary for practical quantum computing.

In conclusion, the quantum computing industrys roadmap toward 10,000 physical qubits and thereby achieving over 100 logical qubits encapsulates both the challenges and the transformative potential of quantum computing. While the winning approach is yet to be determined, it appears that we are getting closer and closer to truly useful quantum computers.

Read more from the original source:
Crossing the Quantum Threshold: The Path to 10,000 Qubits - HPCwire