Lifeboat Foundation

Quantum simulators are a strange breed of systems for purposes that might seem a bit nebulous from the outset. These are often HPC clusters with fast interconnects and powerful server processors (although not usually equipped with accelerators) that run a literal simulation of how various quantum circuits function for design and testing of quantum hardware and algorithms. Quantum simulators do more than just test. They can also be used to emulate quantum problem solving and serve as a novel approach to tackling problems without all the quantum hardware complexity.

Despite the various uses, there’s only so much commercial demand for quantum simulators. Companies like IBM have their own internally and for others, Atos/Bull have created these based on their big memory Sequanna systems but these are, as one might imagine, niche machines for special purposes. Nonetheless, Nvidia sees enough opportunity in this arena to make an announcement at their GTC event about the performance of quantum simulators using the DGX A100 and its own custom-cooked quantum development software stack, called CuQuantum.

After all, it is probably important for Nvidia to have some kind of stake in quantum before (and if) it ever really takes off, especially in large-scale and scientific computing. What better way to get an insider view than to work with quantum hardware and software developers who are designing better codes and qubits via a benchmark and testing environment?

Circa 2020 o.,.o!

By Leah Crane.

Google researchers have used a quantum computer to simulate a chemical reaction for the first time. The reaction is a simple one, but this marks a step towards finding a practical use for quantum computers.

Continue reading “Google performed the first quantum simulation of a chemical reaction” »

O,.o circa 2020.

With the rapid developments in quantum hardware comes a push towards the first practical applications on these devices. While fully fault-tolerant quantum computers may still be years away, one may ask if there exist intermediate forms of error correction or mitigation that might enable practical applications before then. In this work, we consider the idea of post-processing error decoders using existing quantum codes, which are capable of mitigating errors on encoded logical qubits using classical post-processing with no complicated syndrome measurements or additional qubits beyond those used for the logical qubits. This greatly simplifies the experimental exploration of quantum codes on near-term devices, removing the need for locality of syndromes or fast feed-forward, allowing one to study performance aspects of codes on real devices. We provide a general construction equipped with a simple stochastic sampling scheme that does not depend explicitly on a number of terms that we extend to approximate projectors within a subspace. This theory then allows one to generalize to the correction of some logical errors in the code space, correction of some physical unencoded Hamiltonians without engineered symmetries, and corrections derived from approximate symmetries. In this work, we develop the theory of the method and demonstrate it on a simple example with the perfect [[5, 1, 3]] code, which exhibits a pseudo-threshold of p≈0.50 under a single qubit depolarizing channel applied to all qubits. We also provide a demonstration under the application of a logical operation and performance on an unencoded hydrogen molecule, which exhibits a significant improvement over the entire range of possible errors incurred under a depolarizing channel.

A team led by Prof. GUO Guangcan and Prof. ZOU Changling from the University of Science and Technology of China of the Chinese Academy of Sciences realized efficient frequency conversion in microresonators via a degenerate sum-frequency process, and achieved cross-band frequency conversion and amplification of converted signal through observing the cascaded nonlinear optical effects inside the microresonator. The study was published in Physical Review Letters.

Coherent frequency conversion process has wide application in classical and quantum information fields such as communication, detection, sensing, and imaging. As a bridge connecting wavebands between fiber telecommunications and atomic transition, coherent frequency conversion is a necessary interface for distributed quantum computing and quantum networks.

Integrated nonlinear photonic chip stands out because of its significant technological advances of improving nonlinear optical effects by microresonator’s enhancing the light-matter interaction, along with other advantages like small size, great scalability, and low energy consumption. These make integrated nonlinear photonic chips an important platform to covert optical frequency efficiently and realize other nonlinear optical effects.

Circa 2020

Nanotechnology development company UbiQD announced an optical fiber-coupled luminescent concentrator technology as a new tool for optimizing light in controlled environments, enabling light-guiding to future UbiGro spectrum-control greenhouse products.

Qubits offer a fast, highly reliable way to solve one of the great mysteries in physics. Some kind of invisible material is out there affecting the motions of stars and galaxies, but thus far, no one has been able to directly detect the substance—called dark matter—itself. But some are hoping that.

Cambridge Quantum Computing (CQC) hiring Stephen Clark as head of AI last week could be a sign the company is boosting research into ways quantum computing could be used for natural language processing.

Quantum computing is still in its infancy but promises such significant results that dozens of companies are pursuing new quantum architectures. Researchers at technology giants such as IBM, Google, and Honeywell are making measured progress on demonstrating quantum supremacy for narrowly defined problems. Quantum computers with 50–100 qubits may be able to perform tasks that surpass the capabilities of today’s classical digital computers, “but noise in quantum gates will limit the size of quantum circuits that can be executed reliably,” California Institute of Technology theoretical physics professor John Preskill wrote in a recent paper. “We may feel confident that quantum technology will have a substantial impact on society in the decades ahead, but we cannot be nearly so confident about the commercial potential of quantum technology in the near term, say the next 5 to 10 years.”

Continue reading “Cambridge Quantum pushes into NLP and quantum computing with new head of AI” »

This year-old zdnet article notes that the company plans a photo-sensitivi ty range from ultraviolet through visible light to 2000nm infrared. The sensor itself retains almost 4x the light of ordinary CMOS sensors, while being 2000x more sensitive to light. This will put it on par with the best analogue image intensification tubes used for night vision. Up until now, there have not been any digital night vision systems that can match analogue systems. This will be better, with higher resolution and multichromatic. It also has a 100x greater dynamic range than ordinary CMOS sensors, according to the specifications from SeeDevice’s site linked below. (This means that it can image both bright and dark areas clearly and simultaneously, instead of having the bright areas washing out the image, or the dark areas being black. The included photo is from its website, demonstrating a wide dynamic range photo produced by the system. On a normal photo, either the sky would appear black, or the road would be so bright that it would look washed out.)

Hopefully coming soon to a cell phone camera near you…

SeeDevice’s site: https://www.seedeviceinc.com/technology

Nobel laureate in physics Richard Feynman once described turbulence as “the most important unsolved problem of classical physics.”

Understanding turbulence in classical fluids like water and air is difficult partly because of the challenge in identifying the vortices swirling within those fluids. Locating vortex tubes and tracking their motion could greatly simplify the modeling of turbulence.

But that challenge is easier in quantum fluids, which exist at low enough temperatures that quantum mechanics — which deals with physics on the scale of atoms or subatomic particles — govern their behavior.