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Hyper-entanglement in neutral atoms

In 2025, physicists at the California Institute of Technology demonstrated a breakthrough in quantum engineering by achieving hyper-entanglement of neutral atoms using laser-based optical tweezers. Unlike conventional entanglement—where quantum particles share a single property such as spin or polarization—hyper-entanglement involves the simultaneous entanglement of multiple degrees of freedom, such as both the internal electronic state and the external motion of an atom. This advancement significantly increases the capacity of individual qubits to store and process information, offering new avenues for scalable quantum computing architectures and quantum simulations.¹

The researchers utilized alkaline-earth neutral atoms, cooling them to near absolute zero and then isolating and manipulating them with finely controlled beams of laser light. The optical tweezers enabled precise control over atomic positioning, allowing entanglement to be engineered deterministically rather than probabilistically. This approach is expected to improve the fidelity and efficiency of qubit operations in next-generation quantum processors.¹

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Footnotes

1. "Physicists force atoms into state of quantum hyper-entanglement using tweezers made of laser light", Live Science, 31 May 2025.https://www.livescience.com/physics-mathematics/particle-physics/physicists-force-atoms-into-state-of-quantum-hyper-entanglement-using-tweezers-made-of-laser-light

Quantum Computing

Quantum computing is a rapidly advancing field of computing focused on the development of computer technologies based on the principles of quantum theory, which explains the nature and behavior of energy and matter on the quantum (atomic and subatomic) level. Unlike classical computers, which use bits that are either 0 or 1, quantum computers use quantum bits, or qubits, which can represent both 0 and 1 simultaneously due to a phenomenon called superposition¹.

Basic Principles

Quantum computers leverage three main principles of quantum mechanics:

1. Superposition: Qubits can exist in multiple states simultaneously.
2. Entanglement: Pairs or groups of qubits can become correlated in ways that the state of one qubit is dependent on the state of another.
3. Quantum Interference: The manipulation of qubit states can increase the probability of correct answers and reduce the probability of incorrect ones.

These principles allow quantum computers to solve certain problems much faster than classical computers, especially those involving large-scale computation such as cryptography, molecular modeling, and optimization problems².

Applications

One of the most well-known applications of quantum computing is in cryptography, particularly through Shor's algorithm, which can factor large integers exponentially faster than the best-known classical algorithms. This has significant implications for public-key cryptography, which underpins many of today’s secure digital systems³.

Other notable areas of application include:

• Drug discovery and molecular simulation: Quantum computers can simulate complex molecular interactions at a quantum level, potentially accelerating the development of new medicines⁴.
• Artificial Intelligence and Machine Learning: Quantum machine learning algorithms may allow for faster data classification and pattern recognition⁵.
• Logistics and optimization: Quantum algorithms are being explored to solve complex routing and scheduling problems more efficiently⁶.

Development and Limitations

As of the 2020s, quantum computers are still in the early stages of development. Companies like IBM, Google, and D-Wave have built early-stage quantum devices, but these machines are limited by decoherence, error rates, and scalability. Quantum error correction is an active area of research, aiming to make quantum computations more reliable and scalable⁷.

Despite the challenges, many countries and corporations are investing heavily in quantum technologies. The United States, China, and the European Union have launched national strategies and significant funding initiatives to become leaders in quantum computing research and development⁸.

See Also

• Quantum Mechanics
• Classical Computing
• Shor’s Algorithm
• Quantum Supremacy

References

1. Nielsen, M.A. & Chuang, I.L. (2010). Quantum Computation and Quantum Information. Cambridge University Press.
2. Arute, F. et al. (2019). "Quantum supremacy using a programmable superconducting processor", Nature, 574, 505–510. https://www.nature.com/articles/s41586-019-1666-5
3. Shor, P.W. (1994). "Algorithms for Quantum Computation: Discrete Logarithms and Factoring", Proceedings 35th Annual Symposium on Foundations of Computer Science, IEEE.
4. Cao, Y. et al. (2019). "Quantum Chemistry in the Age of Quantum Computing", Chemical Reviews, 119(19), 10856–10915.
5. Biamonte, J. et al. (2017). "Quantum Machine Learning", Nature, 549, 195–202.
6. McKinsey & Company. (2023). "What is quantum computing? An overview for business leaders". Retrieved from https://www.mckinsey.com
7. Preskill, J. (2018). "Quantum Computing in the NISQ era and beyond", Quantum, 2, 79.
8. Wikipedia contributors. (2024). Quantum computing. In Wikipedia, The Free Encyclopedia. Retrieved from https://en.wikipedia.org/wiki/Quantum_computing