Quantum computing has been a constantly bubbling field of interest, promising a new era of computational capabilities far beyond what’s currently possible with classical computers. In recent years, increased focus has been placed on the practical applications of quantum technology, particularly in solving complex combinatorial problems that are currently implausible for classical algorithms. One such application is the max-cut problem, a representative combinatorial challenge that has attracted significant attention due to the potential quantum speed-up it might offer. A study led by researchers Guerreschi and Matsuura at Intel Labs cast a spotlight on the Quantum Approximate Optimization Algorithm (QAOA) for max-cut problems, suggesting that a quantum speed-up would require hundreds of qubits. This eye-opening research, published in Scientific Reports, reinforces the notion that the journey to quantum advantage in practical applications is still ongoing.
DOI: 10.1038/s41598-019-43176-9
Quantum Supremacy: Beyond the Hype
Quantum supremacy is the threshold where quantum computers can carry out calculations beyond the reach of even the most powerful classical computers. However, this concept typically hinges on artificial tasks designed merely to showcase the potential of quantum processing power. Guerreschi and Matsuura (2019) argue that for quantum computers to have a tangible impact, they must prove useful in addressing actual applications and solving real-world problems. Advancements such as the successful demonstration of quantum supremacy may point towards promising developments, but they do not directly translate to solving practical issues.
The QAOA: An Algorithm in Limbo
The Quantum Approximate Optimization Algorithm (QAOA) is a prominent example of a quantum-classical hybrid algorithm designed to tackle optimization problems. This hybrid nature stems from its employment of quantum resources to execute certain computations while relying on classical technologies for other parts of the process. Guerreschi and Matsuura conducted realistic simulations of the QAOA applied to max-cut problems and concluded that the much-sought-after quantum speed-up is unlikely within the limits of current and near-term quantum devices, which are constrained to roughly 50 qubits without active error correction.
The Quantum Leap: A Need for Hundreds of Qubits
In order to lower bound the utility of quantum computers, the research highlights the need for these devices to hold several hundreds of qubits to achieve any quantum advantage for real-world issues like the max-cut problem. This problem involves dividing a network into two groups, maximizing the number of edges between the groups. It’s a challenge not just prevalent in theoretical computer science; it has practical equivalence in fields like logistics and machine learning.
The Current Quantum Landscape
Today’s quantum computers are dubbed Noisy Intermediate-Scale Quantum (NISQ) devices. They hold a modest number of qubits and, as such, lack the fault tolerance required for large-scale, accurate quantum computation (Preskill John, 2018). The study references the pioneering work of Farhi et al. (2014) for introducing QAOA as well as advancements in quantum supremacy, but emphasizes that such breakthroughs remain initial steps on the long path to solving practical problems efficiently with quantum algorithms.
Optimization: A Critical Frontier
The research underscores the significance of optimization algorithms. In classical computation, optimization has a vast range of applications, from logistics to finance to artificial intelligence. Quantum optimization algorithms, therefore, promise substantial advancements in these fields. However, as Guerreschi and Matsuura point out, the current state of quantum computation capability puts practical quantum optimization just beyond reach, with noise and coherence issues further complicating matters.
Forward into the Quantum Future
The paper by Guerreschi and Matsuura (2019) provides a sobering perspective on the status of quantum computing in terms of practical application. However, their analysis does not signify an end to the aspiration of achieving quantum speed-up, rather a clarion call to the quantum computing community. The study presents a realistic assessment, guiding future research and development efforts by providing a framework of expectations for the near-term capabilities of quantum technology.
References
1. Preskill, J. (2012). Quantum computing and the entanglement frontier. arXiv:1203.5813.
2. Boixo, S., et al. (2018). Characterizing quantum supremacy in near-term devices. Nature Physics, 14(6), 595–600. 10.1038/s41567-018-0124-x.
3. Neill, C., et al. (2018). A blueprint for demonstrating quantum supremacy with superconducting qubits. Science, 360, 195–199. 10.1126/science.aao4309.
4. Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv:1411.4028.
5. Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79. 10.22331/q-2018-08-06-79.
Keywords
1. Quantum Computing Applications
2. Quantum Approximate Optimization Algorithm
3. QAOA Max-Cut Problem
4. Quantum Speed-up Breakthrough
5. Noisy Intermediate-Scale Quantum (NISQ) Devices
In conclusion, the study by Guerreschi and Matsuura from Intel Labs exceptionally marks a critical checkpoint on the quantum computing roadmap, highlighting the challenging yet imperative pursuit of practical quantum speed-up. Their findings not only delineate the current boundaries of quantum technology but also act as a guiding star for future research, ensuring that the quantum community maintains a steadfast focus on solving real-world problems over merely theoretical demonstrations of computational prowess.