Quantum computing: A reality check from the experts

Quantum computing is often hailed as the , promising to solve some of the most complex and challenging problems in science, engineering, and business. But how close are we to achieving this quantum dream, and what are the limitations of this emerging field? As shares in its detailed report, some of the leading voices in quantum computing have recently expressed doubts and concerns about the technology’s current state and prospects. They argue that quantum computers are far from being ready for practical use and that their applications are more restricted than commonly assumed.

The quantum hype train Quantum computers use quantum mechanics to manipulate information in impossible ways for classical computers, exploiting phenomena like superposition and entanglement to perform multiple calculations simultaneously. This has sparked excitement about their potential to solve problems such as optimizing complex systems, modeling financial markets, and enhancing AI. have claimed they will soon deliver machines that outperform the best classical supercomputers.

However, only some are convinced by these claims. Yann LeCun, the head of AI research at Meta, is skeptical of the feasibility of building useful quantum computers. Oskar Painter, the head of quantum hardware for Amazon Web Services, also believes there is a lot of hype in the industry, making it difficult to filter the optimistic from the unrealistic.

The quantum error problem One of the main challenges facing quantum computing is the issue of errors. Quantum computers are extremely sensitive to noise and interference, which can cause their qubits – the basic units of quantum information – to lose their quantum state and produce incorrect results. This makes quantum computers very unreliable and prone to errors.

Some researchers have proposed that these noisy quantum computers, also known as NISQ (noisy intermediate-scale quantum) devices, could still be useful for tasks such as sampling, optimization, and machine learning. However, Painter says this is unlikely and that implementing quantum error correction is the only way to achieve reliable and scalable quantum computing. Quantum error correction involves encoding information in to create a single logical qubit more resilient to errors, but this requires many physical qubits.

Critics argue that it might be impossible. Painter believes it’s crucial for building a fault-tolerant quantum computer, but it’s a tough problem and could take a decade to achieve. The quantum application problem Another challenge facing quantum computing is the question of what it can do.

Quantum computing faces challenges in terms of the limited scope of applications. Matthias Troyer, a technical fellow at Microsoft, in Communications of the ACM, stating that many quantum algorithms proposed in the last decade were flawed or impractical. Unrealistic assumptions like perfect quantum memory or arbitrary operations on any pair of qubits were some of the reasons behind it.

He also says that the main advantage of quantum computing is not speed but rather the ability to solve impossible problems for classical computers. Quantum computing can solve problems that classical computers cannot. The only two domains where quantum algorithms offer a speedup are factoring large numbers and simulating quantum systems.

These two problems are quantum hard and cannot be efficiently solved by classical computers. Other problems like linear algebra and machine learning are quantum-easy and can be solved by classical and quantum computers. There are no quantum-medium problems, which means they can only be efficiently solved by quantum computers.

He concludes that quantum computing is not a general-purpose technology but a niche technology that can only solve a few specific problems. He says that this does not diminish the importance of quantum computing but rather clarifies its role and potential. The quantum reality check Troyer’s paper shows that quantum computing may need a clearer advantage in solving problems such as optimization, drug design, and fluid dynamics.

Some of the quantum algorithms offer only a quadratic speedup, which may need to be more impressive to offset the huge computational overhead of quantum computing. According to him, quantum computers are much slower than classical computers due to the complexity of operating a qubit. For smaller problems, classical computers will always be faster, and the point at which the quantum computer gains a lead depends on how quickly the complexity of the classical algorithm scales.

He and his colleagues compared a single Nvidia A100 GPU to a hypothetical future fault-tolerant quantum computer with 10,000 logical qubits and faster gate times. They found that a quantum algorithm with a quadratic speedup would have to run for centuries or even millennia before outperforming a classical one on problems big enough to be useful. He also points out another major obstacle for quantum computing: data bandwidth.

Quantum computing faces a major obstacle in data bandwidth due to the slow operating speeds of qubits that limit the rate at which classical data can be transferred in and out. This means data-intensive applications like machine learning or searching databases are not practical. Quantum computers will excel only on small-data problems.

Many Microsoft customers appreciated the clarity of quantum computing applications. Some companies downsized or shut down their quantum computing teams in the finance and life sciences sectors. The quantum optimism Despite limitations, experts are optimistic about quantum computing.

Scott Aaronson, a computer science professor at UT Austin, says these challenges should be familiar to everyone following quantum computing. There have always been claims about how quantum computing will revolutionize industries, but skepticism was always warranted. Practical applications are still far off, but recent progress has given hope, like QuEra and Harvard’s experiment that generated using a 280 qubit processor.

Yuval Boger, the CMO of QuEra, says that a recent lab demonstration has made some people reconsider their timelines for fault-tolerant quantum computing. The experiment showed that quantum error correction is possible and scalable, paving the way for more powerful and reliable quantum computers in the future. Companies are quietly shifting resources away from quantum computing due to growing interest in AI, driven partly by impressive results in natural language processing and other tasks from large language models like GPT-3, which have attracted significant attention and investment.

While the hype around quantum computing has helped get funding and talent for the field, it has also led to unrealistic expectations and disappointment. Quantum computing is a long-term vision that requires patience and perseverance. It can complement classical computing as a new tool, offering unique advantages for problems like cryptography and quantum simulation, but it won’t replace classical computing for most problems.

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