It’s been 35 years since Cher first wanted to turn back time, but it turns out that quantum mechanics might have allowed for this wild reversal all along. In new research, scientists from China and Hong Kong show that—in certain quantum systems—the time variable can be reversed by creating a double superposition (one each in opposite directions) and still bear out valid results.
What results from this little bit of quantum trickery is both an input and output that are considered indefinite, meaning that either one can be the input or the output. Basically, the after can go before the before. The peer-reviewed research appears in the journal Physical Review Letters.
In our day-to-day lives, we perceive time as marching inexorably forward, and that means many processes aren’t easily reversible. You can’t put the toothpaste back in the tube, so to speak—it’s a lot more difficult to reset an object back to its original state than it is to change it in the first place. This is called time’s arrow, and we believe it’s partly caused by the fact that our universe has been ever-expanding since the Big Bang. READ MORE...
When it comes to natural disasters, every second counts—and the clock may just be ticking a little slower following a collaboration between Terra Quantum and Honda Research Institute Europe (HRI-EU).
By entangling quantum computing with traditional algorithms, the team has paved a ‘superposition’ of escape routes designed to evacuate people more quickly and efficiently in emergency situations.
This hybrid approach fuses the world of quantum mechanics with the urgency of real-world crises, aiming to create not just the shortest path out, but the ‘quantum leap’ in emergency response we’ve been waiting for.
According to the Honda/Terra Quantum team, disasters have increased fivefold over the past 50 years, primarily due to climatic changes and extreme weather conditions.
In such high-stakes scenarios, emergency response measures like evacuation routes are essential for public safety. “Common” traditional algorithms, such as Dijkstra’s node-wise shortest path algorithm, often encounter limitations when applied to dynamic and unpredictable situations like natural disasters.
The project delved into the capabilities of hybrid quantum technologies in the context of emergency evacuations. A proof-of-concept (POC) was implemented, simulating an earthquake on a realistic small-town map. The problem was modeled as a dynamic computational graph, where the earthquake affects certain areas, creating changes in traffic flows, particularly near the exit points of the town.
The team used a hybrid quantum machine learning model, specifically employing feature-wise linear modulation (FiLM) neural networks. These networks were split into classical and quantum components running in parallel, imitating the node-wise Dijkstra’s algorithm on dynamically changing graphs. The FiLM layer used earthquake coordinates as input features to modulate the traditional neural network, thus making real-time adjustments to the evacuation routes.
The model was trained on simulated data that replicated a town map impacted by an earthquake, collecting routing data generated through Dijkstra’s algorithm.
According to Honda Research, this ML architecture managed to work with less than 1% of the total map data, an advantage in an evolving emergency scenarios.
The team claims their quantum hybrid model demonstrated superior performance in comparison to purely classical approaches. READ MORE...
