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Conventional components perform incredibly inefficiently at these sub-freezing temperatures, the scientists said. They’re also very hard to maintain — as more and more qubits are added to a system, the more heat is emitted, which makes it more difficult and expensive to sustain these ultralow temperatures.

Because the new transistor — dubbed the “cryo-CMOS transistor” — is optimized to operate at temperatures under 1 K and emit near-zero heat, it offers plenty of advantages over traditional electronics, representatives of the Finnish company SemiQon, which developed the transistor, said in a statement.

https://scirate.com/arxiv/2411.

Researchers present a #quantummachinelearning advantage of families of constant depth local quantum circuits over reasonably constrained log-log-depth classical circuits.

Quantum…

One of the core challenges of research in quantum computing is concerned with the question whether quantum advantages can be found for near-term quantum circuits that have implications for practical applications. Motivated by this mindset, in this work, we prove an unconditional quantum advantage in the probably approximately correct (PAC) distribution learning framework with shallow quantum circuit hypotheses. We identify a meaningful generative distribution learning problem where constant-depth quantum circuits using one and two qubit gates (QNC^0) are superior compared to constant-depth bounded fan-in classical circuits (NC^0) as a choice for hypothesis classes. We hence prove a PAC distribution learning separation for shallow quantum circuits over shallow classical circuits. We do so by building on recent results by Bene Watts and Parham on unconditional quantum advantages for sampling tasks with shallow circuits, which we technically uplift to a hyperplane learning problem, identifying non-local correlations as the origin of the quantum advantage.

Continue reading “An unconditional distribution learning advantage with shallow quantum circuits” »

A recurrent, transformer-based neural network, called AlphaQubit, learns high-accuracy error decoding to suppress the errors that occur in quantum systems, opening the prospect of using neural-network decoders for real quantum hardware.

The V-score benchmarks classical and quantum algorithms in solving the many-body problem. The study highlights quantum computings potential for tackling complex material systems while providing an open-access framework for future research innovations.

Scientists aspire to use quantum computing to explore complex phenomena that have been difficult for current computers to analyze, such as the characteristics of novel and exotic materials. However, despite the excitement surrounding each announcement of “quantum supremacy,” it remains challenging to pinpoint when quantum computers and algorithms will offer a clear, practical advantage over classical systems.

A large collaboration led by Giuseppe Carleo, a physicist at the Swiss Federal Institute for Technology (EPFL) in Lausane and the member of the National Center for Competence in Research NCCR MARVEL, has now introduced a method to compare the performance of different algorithms, both classical and quantum ones, when simulating complex phenomena in condensed matter physics. The new benchmark, called V-score, is described in an article just published in Science.

118 likes, — _quantummech_ on September 11, 2024: ‘Dr Michio Kaku explains how aliens might be traveling through space’

Researchers have developed a new quantum theory that for the first time defines the precise shape of a photon, showing its interaction with atoms and its environment.

This breakthrough allows for the visualization of photons and could revolutionize nanophotonic technologies, enhancing secure communication, pathogen detection, and molecular control in chemical reactions.

A groundbreaking quantum theory has allowed researchers to define the exact shape of a single photon for the first time.

A breakthrough at Rice University enhances thermophotovoltaic systems with a new thermal emitter design, achieving over 60% efficiency.

This could transform energy conversion, making it a viable alternative to batteries for grid-scale energy storage and sustainable industry practices.

Researchers at Rice University have developed an innovative way to enhance thermophotovoltaic (TPV) systems, which convert heat into electricity using light. Drawing inspiration from quantum physics, engineer Gururaj Naik and his team designed a highly efficient thermal emitter that works within realistic design constraints.

Heeding those sentiments, the Australian Army is strategically investing in technological innovation to find better solutions to the complex logistics challenges they face in managing the efficient and safe deployment of personnel and equipment on the battlefield. For a difficult class of problems in an area called “optimization”, quantum computing is on the roadmap for exploration.

With the help of our quantum infrastructure software, they’ve now been able to test and validate a quantum computing solution on real hardware that promises to outperform their existing methods.

The U.S. faces a critical cybersecurity threat as quantum computers edge closer to disrupting the cryptographic systems that secure vital government and infrastructure data, according to a Government Accountability Office (GAO) report.

U.S. faces significant cybersecurity risks from quantum computing due to leadership gaps and an incomplete national strategy.