Lifeboat Foundation

As the name suggests, most electronic devices today work through the movement of electrons. But materials that can efficiently conduct protons—the nucleus of the hydrogen atom—could be key to a number of important technologies for combating global climate change.

Most proton-conducting inorganic materials available now require undesirably high temperatures to achieve sufficiently high conductivity. However, lower-temperature alternatives could enable a variety of technologies, such as more efficient and durable fuel cells to produce clean electricity from hydrogen, electrolyzers to make clean fuels such as hydrogen for transportation, solid-state proton batteries, and even new kinds of computing devices based on iono-electronic effects.

In order to advance the development of proton conductors, MIT engineers have identified certain traits of materials that give rise to fast proton conduction. Using those traits quantitatively, the team identified a half-dozen new candidates that show promise as fast proton conductors. Simulations suggest these candidates will perform far better than existing materials, although they still need to be conformed experimentally. In addition to uncovering potential new materials, the research also provides a deeper understanding at the atomic level of how such materials work.

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.

A brain-computer interface from the startup Inbrain could be used to treat Parkinson’s disease.

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.

Quantum information systems offer faster, more powerful computing methods than standard computers to help solve many of the world’s toughest problems. Yet fulfilling this ultimate promise will require bigger and more interconnected quantum computers than scientists have yet built. Scaling quantum systems up to larger sizes, and connecting multiple systems, has proved challenging.

Though not a new technology, new advancements have progressed this field significantly.

An ordered pattern of atomic spins with possible uses in computing can become more ordered if shaken at the right frequency.

New research has established a reversible framework for quantum entanglement, aligning it with the principles of thermodynamics and paving the way for improved manipulation and understanding of quantum resources.

Bartosz Regula from the RIKEN Center for Quantum Computing and Ludovico Lami from the University of Amsterdam have demonstrated through probabilistic calculations the existence of an “entropy” rule for quantum entanglement. This discovery could enhance our understanding of quantum entanglement, a crucial resource underpinning the potential of future quantum computers. Although quantum entanglement has been a research focus in quantum information science for decades, optimal methods for its effective utilization remain largely unknown.

The second law of thermodynamics, which says that a system can never move to a state with lower “entropy”, or order, is one of the most fundamental laws of nature and lies at the very heart of physics. It is what creates the “arrow of time,” and tells us the remarkable fact that the dynamics of general physical systems, even extremely complex ones such as gases or black holes, are encapsulated by a single function, its “entropy.”

New research has revealed that the lag observed in organic electrochemical transistors (OECTs) when switched on is due to a two-step activation process, providing crucial insights for designing more effective and customizable OECTs for various technological and biological applications.

Researchers who want to bridge the divide between biology and technology spend a lot of time thinking about translating between the two different “languages” of those realms.

“Our digital technology operates through a series of electronic on-off switches that control the flow of current and voltage,” said Rajiv Giridharagopal, a research scientist at the University of Washington. “But our bodies operate on chemistry. In our brains, neurons propagate signals electrochemically, by moving ions — charged atoms or molecules — not electrons.”