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Quantum Computing still experimental despite 2024 advances

According to a report published by, quantum computing is making significant progress in 2024 but has yet to prove that it offers a practical advantage to digital computers. Forrester Research.

The report, written by Forrester’s Vice President for Emerging Technologies Brian Hopkins, Principal Analyst David Mooter and Stephanie Balaouras with Mike Gualtieri Charlie Dai James McGlynn and Jen Barton, stated that despite improvements in qubit counts, coherence times, and gate fidelity the technology is still experimental.

The analysts said that “key developments in quantum simulation and quantum machine-learning show promise for certain industries such as finance and pharmaceuticals. However, challenges like high error rates and scaling persist.”

Roger A. Grimes Author of “Cryptography Apocalypse”: Preparing for a Day When Quantum Computing Will Break Today’s Cryptography, published by Wiley, acknowledges that while practical quantum computers are not yet available, some useful quantum applications have already been developed, including quantum random number generators and sensors.

He told TechNewsWorld that “no one has ever publicly demonstrated a problem that was solved by a super-usable quantum computer in the real world.” “Today your wristwatch is more powerful than the quantum computers we use.” That is beginning to change. “We are making steady advances, and the day quantum computers will be sufficiently capable quantum computers is not too far away.”

Quantum Solving Problems Now

Trevor Lanting is the chief development officer of D-Wave Systems, a Vancouver-based company in Canada. He agreed that gate-model quanta computing has yet to demonstrate a practical advantage. However, he pointed out that quantum annealing computing delivers value today over classical computing.

Gate-model quantum computing uses quantum logic gates, just as classical computers do with logic gates, to perform operations on qubits. The gate-model is better for general-purpose computing while annealing quanta computing is more suited to optimization problems such as workforce planning and portfolio optimization.

D-Wave uses annealing quanta in a hybrid approach to solve complex optimization issues. The company was able to optimize mobile network resources for Japan’s largest provider of telecom services, NTT Docomo. This took 40 seconds as opposed to 27 hours with classical methods.

Forrester predicted that gates-based quantum computers would likely remain experimental platforms for 10 to 20 years. Lanting concurs with that prediction. “However, annealing quantum computing — which is uniquely suited for solving complex optimization problems — is here now,” he told TechNewsWorld.

“Optimization problems are everywhere — from workforce scheduling to vehicle routing — and D-Wave’s annealing quantum computers are already delivering measurable results for customers,” he said.

Lanting maintained that D-Wave’s technology helped Pattison Food Group, a Canadian grocery chain, reduce an 80-hour scheduling task to just 15 hours — an 80% time savings — and at the Port of Los Angeles, working with SavantX, cargo handling efficiency was improved by 60%.

He said that these are not theoretical use cases. “These are real businesses that are solving real problems now using quantum and hybrid computing.”

Optimizing Apps Will Lead the Way

Forrester noted that these claims would be challenged by 2024.

Q-CTRL” IBM’s gate-based quanta-computers have challenged D-Wave’s claim, outperforming D-Wave in an optimization task,” wrote the analysts. “Gate-based algorithm offers the potential for faster solution speeds as qubit counts increase and quality improves,” wrote the analysts.

They continued, “This makes Q-CTRL’s claim an intriguing challenge to D-Wave’s self-proclaimed leadership in optimization.”

Forrester notes that quantum computing is a good application for optimization, as it affects most industries. It explained: “For finance, these areas include risk modelling, trading strategy optimizations, asset price optimizations, and portfolio optimization.”

The report added, “Healthcare uses cases include optimizing radiationtherapy treatments, creating targeted cancer drug therapy, and creating proteins models.” “And in the energy sector, there are use cases such as energy exploration, optimizing seismic survey, spot trading and reserve optimization, and optimizing reservoirs.”

Erik Garcell, Director of Quantum Enterprise development for North America, said that in the near future, quantum will be able to generate a return for users through optimization tools. Classiq, an international quantum computer software manufacturer.

He told TechNewsWorld that “optimization is more beneficial in the near term because it scales well on quantum computers.” “Even a few resources of quantum computing, like 100 qubits, on a quantum chip can be a big help for this kind of problem.”

He continued: “The larger the problem, it’s harder for a classic computer to solve. But that many more resources won’t be needed for a quantum computer due to its scaling.” You’ll see quantum computers being used to solve very large optimization issues that make classical computers chug.

Quantum Machine Learning is a Living System

Grimes believes that focusing solely on optimization is too restrictive for quantum. He said that optimization “completely rules out” the possibility of new advancements. “I don’t know if the quantum world is similar to that of AI, but I think it has the feeling of the AI.

“It’s like we could be on the brink of an enormous, sudden change,” he said. There are many organizations that have made steady improvements. “It seems odd to me that none of these vendors would have made a major breakthrough out of all the thousands.”

Forrester analysts have also cited quantum machine-learning as a breakthrough for 2024. Quantum-as a service (QaaS), they explained, has increased access to quantum computing and enabled breakthroughs in quantum learning. Researchers are now working on quantum neural networks, support vector machines and quantum algorithms that can be used for complex tasks, such as natural language and image processing.

These advancements push the boundaries of machine learning, making it an important area of growth,” wrote they.

Skip Sanzeri (co-founder and COO at Deep Learning Networks) observed that training AI models with classical computers can be time-consuming and expensive, particularly when using deep learning networks. QuSecure San Mateo (California) is the home of, a company that manufactures quantum-safe security products.

TechNewsWorld reported that using the Quantum Approximate optimization algorithm, as well as other quantum enhancements such gradient descent, would speed up the machine learning model training by orders-of-magnitude.

Sanzeri also noted that AI on classic systems is not exponentially-scalable. Therefore, classical machines have a tendency to struggle with combinatorial tasks like optimization. He explained that quantum computers, because they are exponential by nature, will be better able to deal with these combinatorial issues.

Quantum algorithms, such as the Quantum Fourier Transform can be used for processing and analyzing large data sets in a more efficient manner, leading to real-time insights and faster decision-making.

Adding to this, he said that the generation of AI models is also a challenge for classical computing systems. “Superposition and entanglement — quantum properties — can be used to generate data distributions more efficiently and accurately,” he said.

Quantum Threats – Urgent Need for Preparation

As quantum advancements push the limits of what machine-learning can do, they are also driving a new focus on quantum safety. “With NIST [the U.S. Department of Commerce’s National Institute of Standards and Technology] “The need to protect data from future quantum threats becomes more urgent as we set standards for quantum resistant algorithms,” Forrester analysts wrote.

The experts added that machine learning and cryptography have a lot of potential but the benefits are still years away. They noted that Shor’s algorithm may one day be able to break the PKI cryptography of today, but this will likely take a decade or longer.

Sanzeri disagreed With Forrester. “With the use of ever increasingly powerful AI, combined with other breakthroughs in the quantum computing industry — like Google’s Willow chip — we could see that 10-year time frame getting cut in half,” he argued.

Grimes warned that the government agencies working on breaking quantum-susceptible cryptography don’t require a quantum computer capable of breaking today’s encryption. He said that they would make quantum devices specialized for breaking encryption.

He continued: “The NSA doesn’t use laptops, servers and cloud computing for crypto-breaking.” “They use specialized devices that maximize the efficiency of crypto-breaking. They do the exact same with quantum cracking. “It would be insane for me to do anything different if I were in their shoes.”

He also warned of the dangers of using Shor’s algorithm, created by the U.S. government in 1994, was used as a standard to measure how much quantum computing power is required to crack cryptography. He said that there was a high probability that the U.S. Government had access to an algorithm far more efficient than Shor. If you fixate on Shor’s algorithm, you probably aren’t focused on the best algorithm.

Tomas Gustavsson said that even if it takes 10 years to find a quantum-based solution that will crack PKI, now is the time to take action. Keyfactor Cleveland is the home of, a digital identification management company. He told TechNewsWorld that a decade was not enough time to complete this massive migration.

“We have to act immediately to ensure that the migration is completed within a decade. Gustavsson stated, “Organizations must not begin in a decade.” Forrester, by saying that the Shor algorithm will be practical in a decade, is repeating what NIST, and others, have said. I hope it is at least a decade off. “If not, then we’re in big trouble.”

Winter of Quantum Investors Discontent?

Forrester believes that investment in quantum technology will “winterize” despite its promise. Forrester analysts noted that although quantum computing deals reached a new record in 2023 the investment dollar totals had peaked by 2021. Since then, they have been declining sharply as investor funding has been dominated by generative AI.

Also, geopolitical forces are at play, as in the case of Chinese vendors transferring their intellectual property (IP) to universities. This will cause startups to be under pressure, and many of them to look for exits without much to show,” said the experts.

Analysts argued that, on the plus side of the equation, the investment freeze will delay the moment when quantum computing platforms can be used by the general public. This means a delayed Y2Q – the day when quantum computer systems break current asymmetric cryptography.

But they cautioned against procrastinating over Y2Q. This may give security leaders more time to implement the post-quantum encryption. However, they urge them to start planning for how to prevent “harvest now and decrypt later” vulnerabilities.

Forrester’s dismal weather forecast is not shared by all. Forecasters’ growth estimates of the quantum computing market range from a CAGR as low as 27.04% in the next 8 years to an extremely high CAGR at 32.7% within the next 5 years. This market will grow from US$6,95billion to $16.22billion in early 2030s.

Sanzeri said that while he did not anticipate a “quantum winter”, he predicted the same or even increased level of investment in 2025. Sanzeri predicted that quantum computing had made significant progress by 2024, with some fundamental breakthroughs. We cannot see a reason for the same investment level in 2025.

Grimes added that “the recent relative decline in quantum investment was really only a result of the initial, overstated hype about quantum technology dying down around the time AI exploded.” “Quantum has plenty of investment. As the constant improvements lead to sufficiently-capable computers, the required investment will flood in. “I am not concerned.”