Lifeboat Foundation

Machine learning (ML) is one of the most important subareas of AI used in building great AI systems.

In ML, deep learning is a narrow area focused solely on neural networks. Through the field of deep learning, systems like ChatGPT and many other AI models can be created. In other words, ChatGPT is just a giant system based on neural networks.

However, there is a big problem with deep learning: computational efficiency. Creating big and effective AI systems with neural networks often requires a lot of energy, which is expensive.

Error-correcting a quantum computer can mean processing 100TB every second.

Learn more about neural networks and large language models on Brilliant! First 30 days are free and 20% off the annual premium subscription when you use our link ➜ https://brilliant.org/sabine.

A lot of big banks are banking on quantum computing because they think it’ll give them an edge in trading. Though I have on previous occasions noted my doubt that we’ll see any useful quantum computers within the next ten years, two new papers detailing new methods of scaling quantum computers have shifted my perspective. Let’s have a look.

Continue reading “Big News for Quantum Computing: First Scalable Platforms” »

Quantum: Check out Our Video on Aduro: https://twobit.link/AduroVideoLearn More About Aduro: https://twobit.link/AduroQuantum Computers are are confusing and…

The Quantum Insider (TQI) is the leading online resource dedicated exclusively to Quantum Computing.

Claudia de Rham thinks that gravitons, hypothetical particles thought to carry gravity, have mass. If she’s right, we can expect to see “rainbows” in ripples in space-time.

By Joshua Howgego

The quantum advantage, a key goal in quantum computation, is achieved when a quantum computer’s computational capability surpasses classical means. A recent study introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, which was challenged by IBM Quantum and IonQ researchers who developed a faster classical simulation algorithm. IQP circuits are beneficial due to their simplicity and moderate hardware requirements, but they also allow for classical simulation. The IQP circuit, known as the HarvardQuEra circuit, is built over n 3m 32k inputs. There are two types of simulation for quantum computations: noiseless weak/direct and noisy.

The quantum advantage is a key goal for the quantum computation community. It is achieved when a quantum computer’s computational capability becomes so complex that it cannot be reproduced by classical means. This ongoing negotiation between classical simulations and quantum computational experiments is a significant focus in the field.

A recent publication by Bluvstein et al. introduced a type of Instantaneous Quantum Polynomial-Time (IQP) computation, complemented by a 48-qubit logical experimental demonstration using quantum hardware. The authors projected the simulation time to grow rapidly with the number of CNOT layers added. However, researchers from IBM Quantum and IonQ reported a classical simulation algorithm that computes an amplitude for the 48-qubit computation in only 0.00257947 seconds, which is roughly 10³ times faster than that reported by the original authors. This algorithm is not subject to a significant decline in performance due to the additional CNOT layers.

Quantum field theory (QFT) was a crucial step in our understanding of the fundamental nature of the Universe. In its current form, however, it is poorly suited for describing composite particles, made up of multiple interacting elementary particles. Today, QFT for hadrons has been largely replaced with quantum chromodynamics, but this new framework still leaves many gaps in our understanding, particularly surrounding the nature of strong nuclear force and the origins of dark matter and dark energy. Through a new algebraic formulation of QFT, Dr Abdulaziz Alhaidari at the Saudi Center for Theoretical Physics hopes that these issues could finally be addressed.

The emergence of quantum field theory (QFT) was one of the most important developments in modern physics. By combining the theories of special relativity, quantum mechanics, and the interaction of matter via classical field equations, it provides robust explanations for many fundamental phenomena, including interactions between charged particles via the exchange of photons.

Still, QFT in its current form is far from flawless. Among its limitations is its inability to produce a precise description of composite particles such as hadrons, which are made up of multiple interacting elementary particles that are confined (cannot be observed in isolation). Since these particles possess an internal structure, the nature of these interactions becomes far more difficult to define mathematically, stretching the descriptive abilities of QFT beyond its limits.

Here, the authors demonstrate a combined atom array-nanophotonic chip platform for quantum networking and distributed quantum computing, enabled by a high-fidelity background-free imaging technique, a semi-open photonic chip geometry, and free-space coupling to the nanophotonic cavities.