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Category: quantum physics – Page 194

US quantum tech dethrones supercomputers at solving tough tasks

In a new demonstration, a U.S. researcher showcased that a quantum computer outperforms supercomputers in approximate optimization tasks.

The University of Southern California-led (USC) study demonstrated the first quantum scaling advantage for approximate optimization problem-solving using a quantum annealer.

Quantum annealing is a specific type of quantum computing that can use quantum physics principles to find high-quality solutions to difficult optimization problems. Rather than requiring exact optimal solutions, the study focused on finding solutions within a certain percentage (≥1%) of the optimal value, according to researchers.

Supercharged Qubits: How MIT’s Quarton Coupler Accelerates Quantum Computing

A new MIT-designed circuit achieves record-setting nonlinear coupling, allowing quantum operations to occur dramatically faster.

The heart of this advance is the “quarton coupler,” which boosts both light-matter and matter-matter interactions. This progress could lead to quicker quantum readouts, crucial for error correction and computation fidelity.

Unlocking Quantum Computing’s Speed Potential.

An operating system for quantum computers emerges 🖥️

Researchers have achieved a crucial milestone in quantum computing. They have created an operating system capable of enabling communication between quantum computers using different technologies.

This system, named QNodeOS, represents a significant advancement for quantum machine interoperability. Unlike classical systems like Windows or iOS, it is designed to handle the unique complexity of qubits, regardless of their physical nature. This innovation paves the way for more flexible and powerful quantum networks.

Quantum-Neural Hybrid Solves Impossible Math

The worlds of quantum mechanics and neural networks have collided in a new system that’s setting benchmarks for solving previously intractable optimization problems. A multi-university team led by Shantanu Chakrabartty at Washington University in St. Louis has introduced NeuroSA, a neuromorphic architecture that leverages quantum tunneling mechanisms to reliably discover optimal solutions to complex mathematical puzzles.

Published March 31 in Nature Communications, NeuroSA represents a significant leap forward in optimization technology with immediate applications ranging from logistics to drug development. While typical neural systems often get trapped in suboptimal solutions, NeuroSA offers something remarkable: a mathematical guarantee of finding the absolute best answer if given sufficient time.

“We’re looking for ways to solve problems better than computers modeled on human learning have done before,” said Chakrabartty, the Clifford W. Murphy Professor and vice dean for research at WashU. “NeuroSA is designed to solve the ‘discovery’ problem, the hardest problem in machine learning, where the goal is to discover new and unknown solutions.”

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