Quantum computing witnessed significant advancements in 2024, yet it has not demonstrated a definitive practical advantage over classical digital computers, according to a recent trends report from Forrester Research.

“Despite progress in qubit count, coherence time, and gate fidelity, quantum technology remains largely experimental, with widespread applications likely at least a decade away,” noted the report authored by Forrester Vice President for Emerging Technologies Brian Hopkins and Principal Analyst David Mooter, along with contributions from Stephanie Balaouras, Mike Gualtieri, Charlie Dai, James McGlynn, and Jen Barton.

The analysts highlighted that advancements in optimization, quantum simulation, and quantum machine learning show potential for specific industries such as finance and pharmaceuticals. However, ongoing challenges—including high error rates and scalability limitations—continue to hinder the technology’s broader adoption.

Practical Quantum Applications Remain Elusive

Roger A. Grimes, author of Cryptography Apocalypse: Preparing for the Day When Quantum Computing Breaks Today’s Crypto, acknowledged that fully practical quantum computers have yet to emerge. Nevertheless, he pointed out that certain quantum technologies, such as quantum random number generators, networks, and sensors, are already being deployed.

“No one has publicly demonstrated a problem solved by a quantum computer that offers a substantial, real-world advantage,” Grimes told TechNewsWorld. “At present, your wristwatch has more computing power than current quantum machines. But that’s changing rapidly. We’re making steady progress, and the day when quantum computers achieve practical utility is not far off.”

Quantum Solutions in Action

Trevor Lanting, Chief Development Officer at D-Wave Systems in Vancouver, Canada, agreed that gate-model quantum computing has not yet demonstrated practical benefits. However, he emphasized that annealing quantum computing is already delivering tangible value over classical methods.

Gate-model quantum computers use quantum logic gates to perform operations on qubits, similar to classical logic gates operating on digital bits. In contrast, annealing quantum computing focuses on solving optimization problems, such as workforce scheduling and portfolio optimization.

D-Wave’s hybrid quantum solutions have been employed to solve complex optimization problems. For instance, D-Wave’s technology helped Japan’s largest telecom provider, NTT Docomo, optimize its mobile network resources in just 40 seconds—a task that previously took 27 hours using classical methods.

Forrester’s report predicts that gate-based quantum computing platforms will remain experimental for another 10 to 15 years. Lanting concurs with this timeline but stresses that annealing quantum computing is already solving real-world problems.

“Optimization problems are ubiquitous—from workforce scheduling to vehicle routing—and our annealing quantum computers are delivering measurable results today,” Lanting told TechNewsWorld. He cited examples where D-Wave’s technology reduced an 80-hour scheduling task for Pattison Food Group to just 15 hours and improved cargo-handling efficiency at the Port of Los Angeles by 60%.

The Role of Optimization in Quantum Computing

While D-Wave has long promoted its annealing platform as superior to gate-based solutions for optimization problems, Forrester’s report notes that this claim faced challenges in 2024.

“Q-CTRL challenged D-Wave’s assertions by using IBM’s gate-based quantum computers to outperform D-Wave on an optimization problem,” the analysts wrote. “As qubit counts and quality improve, gate-based algorithms could achieve greater speedups.”

Optimization is critical for many industries. In finance, for example, it plays a role in risk modeling, trading strategy optimization, asset pricing, and portfolio management. In healthcare, it can be used to optimize radiotherapy treatments, develop targeted cancer therapies, and create protein models. For the energy sector, optimization applies to seismic surveys, trading strategies, and reservoir management.

Erik Garcell, Director of Quantum Enterprise Development for North America at Classiq, explained that optimization offers more immediate benefits because it scales well on quantum computers.

“Even with just 100 qubits, quantum computers can handle optimization problems more efficiently than classical systems,” he told TechNewsWorld. “The larger the problem, the harder it is for classical computers to solve, but quantum systems scale differently, making them better suited for large optimization challenges.”

Quantum Machine Learning Gaining Traction

Another significant 2024 breakthrough highlighted by Forrester is quantum machine learning. The emergence of Quantum-as-a-Service (QaaS) has made quantum computing more accessible, spurring advancements in quantum neural networks, quantum support vector machines, and quantum algorithms for image and language processing.

Skip Sanzeri, Co-founder and COO of QuSecure, noted that training AI models on classical computers is time-consuming and resource-intensive.

“Using quantum algorithms like the Quantum Approximate Optimization Algorithm can significantly reduce the time and cost of training machine learning models,” he explained. “Quantum computers can handle combinatorial problems more effectively, which classical machines often struggle with.”

Sanzeri added that quantum algorithms can process and analyze large datasets more efficiently, leading to faster insights and real-time decision-making.

“Generative AI models, which can be computationally intensive for classical systems, could benefit from quantum properties like superposition and entanglement, resulting in more efficient data generation,” he said.

Preparing for Quantum Security Threats

As quantum computing continues to evolve, concerns over quantum security are also growing. The Forrester analysts highlighted the urgency of preparing for quantum threats, especially with the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) setting standards for quantum-resistant algorithms.

They pointed out that while breakthroughs in cryptography and machine learning hold substantial promise, their widespread benefits remain years away. For example, Shor’s algorithm—which could potentially break today’s PKI encryption—is still considered a long-term threat.

However, Sanzeri argued that the timeline for such threats could be shorter than anticipated.

“With advancements in AI and quantum hardware, like Google’s Willow chip, we could see the 10-year timeline for breaking encryption shortened considerably,” he said.

Grimes cautioned that intelligence agencies are likely already working on specialized quantum devices to crack encryption.

“Government agencies won’t wait for fully-capable general-purpose quantum computers,” he said. “They’ll develop specialized quantum devices optimized for breaking encryption, just as they do with today’s traditional encryption-breaking tools.”

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