Lifeboat Foundation

A new photonic processor efficiently solves complex NP-complete problems using light, offering faster computation and scalability for future applications in optical neural networks and quantum computing.

As technology continues to evolve, the limitations of traditional electronic computers are becoming more evident, particularly when addressing highly complex computational problems. NP-complete problems, which grow exponentially in difficulty as their size increases, are among the most challenging in computer science. These issues affect a wide range of fields, from biomedicine to transportation and manufacturing. To find more efficient solutions, researchers are turning to alternative computing methods, with optical computing showing significant promise.

Breakthrough in Photonic Processor Development.

Here’s Malur Narayan of Latimer AI sharing about removing bias, and setting a standard for identifying and measuring it in artificial intelligence systems, and LLM’s.

Malur is a tech leader in AI / ML, mobile, quantum, and is an advocate of tech for good, and responsible AI.

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Quantum, Blockchain & AI | Sarah Baldeo, Founder, ID Quotient Advisory Group. Sarah will be presenting at the upcoming Fin+AI 2024 Conference.

Register with code EARLYBIRD until July 15th — www.finaiconference.com.

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SandboxAQ began as Alphabet’s moonshot AI and quantum computing and now has an impressive roster of projects.

A recent breakthrough in frequency conversion has achieved substantial bandwidth, opening new possibilities for more efficient quantum information transfer and advanced integrated photonic systems.

Advancements in quantum information technology are enabling faster and more efficient data transfer. A major challenge, however, lies in transferring qubits—the fundamental units of quantum information—across different wavelengths while preserving their crucial properties, such as coherence and entanglement.

As reported in Advanced Photonics, researchers from Shanghai Jiao Tong University (SJTU) recently made significant strides in this area by developing a novel method for broadband frequency conversion, a crucial step for future quantum networks.

Here we propose a novel protected erasure qubit, the Floquet fluxonium molecule (FFM). The FFM qubit exhibits (i) extremely long predicted logical coherence times and relatively long erasure lifetimes, (ii) a simple superconducting circuit structure, and (iii) high-fidelity single-qubit gates, which are much faster than the coherence timescale. Based on a Floquet-driven pair of inductively coupled fluxonium circuits [13–15], the FFM is a multi-DOF superconducting circuit with engineered, highly coherent quasieigenstates.

Our key technical contribution is a novel form of Floquet protection in a multi-DOF qubit, which strongly suppresses phase-flip errors, removing them at first and second order in the flux noise. The combination of drive and multi-DOF allows the low-lying eigenstates to be disjoint and delocalized with a nonvanishing energy gap. The second-order sweet spot has no analogue in the single-DOF circuits that have been studied thus far [16–18]; in fact, in single-DOF circuits there is a generic trade-off between bit-and phase-flip errors arising from the inability to keep two eigenstates simultaneously disjoint and flux delocalized using accessible circuit QED Hamiltonians [19].

The higher-order phase-flip insensitivity allow the predicted coherence time of the FFM qubit to significantly outperform other multi-DOF circuits. These include the following: the dual-rail erasure transmon, with experimentally achieved logical lifetimes of approximately ms and erasure lifetimes of approximately [12]; the dual-rail cavity, with logical lifetimes predicted [10] (achieved [11]) at approximately ms (3 ms), limited by cavity and ancilla dephasing, and erasure lifetimes of approximately in both cases; and the cold echo qubit, with predicted logical lifetime of ms with erasure rates unreported [8]. Theoretically, we find the FFM exhibits long bit-flip coherence times of approximately 50 ms while suppressing phase flips even further, along with a 500-erasure lifetime.

The AI system is dubbed a “quantum-tunneling deep neural network” and combines neural networks with quantum tunneling. A deep neural network is a collection of machine learning algorithms inspired by the structure and function of the brain — with multiple layers of nodes between the input and output. It can model complex non-linear relationships and, unlike conventional neural networks (which include a single layer between input and output) deep neural networks include many hidden layers.

Quantum tunneling, meanwhile, occurs when a subatomic particle, such as an electron or photon (particle of light), effectively passes through an impenetrable barrier. Because a subatomic particle like light can also behave as a wave — when it is not directly observed it is not in any fixed location — it has a small but finite probability of being on the other side of the barrier. When sufficient subatomic particles are present, some will “tunnel” through the barrier.

After the data representing the optical illusion passes through the quantum tunneling stage, the slightly altered image is processed by a deep neural network.

Not only does God play dice, that great big casino of quantum physics could have far more rooms than we ever imagined. An infinite number more, in fact.

Physicists from the University of California, Davis (UCD), the Los Alamos National Laboratory in the US, and the Swiss Federal Institute of Technology Lausanne have redrawn the map of fundamental reality to demonstrate the way we relate objects in physics could be holding us back from seeing a bigger picture.

For about a century, our understanding of reality has been complicated by the theories and observations that fall under the banner of quantum mechanics. Gone are the days when objects had absolute measures like velocity and position.

The type of semiconductive nanocrystals known as quantum dots are both expanding the forefront of pure science and also hard at work in practical applications including lasers, quantum QLED televisions and displays, solar cells, medical devices, and other electronics.