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Classical machine learning (ML) algorithms have proven to be powerful tools for a wide range of tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computing and can struggle to process large and complex datasets or to achieve high levels of accuracy and precision.

Enter quantum machine learning (QML).

The early 20^th century saw the advent of quantum mechanics to describe the properties of small particles, such as electrons or atoms. Schrödinger’s equation in quantum mechanics can successfully predict the electronic structure of atoms or molecules. However, the “duality” of matter, referring to the dual “particle” and “wave” nature of electrons, remained a controversial issue. Physicists use a complex wavefunction to represent the wave nature of an electron.

“Complex” numbers are those that have both “real” and “imaginary” parts—the ratio of which is referred to as the “phase.” However, all directly measurable quantities must be “real”. This leads to the following challenge: when the electron hits a detector, the “complex” phase information of the wavefunction disappears, leaving only the square of the amplitude of the wavefunction (a “real” value) to be recorded. This means that electrons are detected only as particles, which makes it difficult to explain their dual properties in atoms.

The ensuing century witnessed a new, evolving era of physics, namely, attosecond physics. The attosecond is a very short time scale, a billionth of a billionth of a second. “Attosecond physics opens a way to measure the phase of electrons. Achieving attosecond time-resolution, electron dynamics can be observed while freezing molecular motion,” explains Professor Hiromichi Niikura from the Department of Applied Physics, Waseda University, Japan, who, along with Professor D. M. Villeneuve—a principal research scientist at the Joint Attosecond Science Laboratory, National Research Council, and adjunct professor at University of Ottawa—pioneered the field of attosecond physics.

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Researchers have designed smart, color-controllable white light devices from quantum dots – tiny semiconductors just a few billionths of a meter in size – which are more efficient and have better color saturation than standard LEDs, and can dynamically reproduce daylight conditions in a single light.

The researchers, from the University of Cambridge, designed the next-generation smart lighting system using a combination of nanotechnology, color science, advanced computational methods, electronics, and a unique fabrication process.

Continue reading “LED Smart Lighting System Based on Quantum Dots More Accurately Reproduces Daylight” »

Australian engineers have discovered a new way of precisely controlling single electrons nestled in quantum dots that run logic gates. What’s more, the new mechanism is less bulky and requires fewer parts, which could prove essential to making large-scale silicon quantum computers a reality.

The serendipitous discovery, made by engineers at the quantum computing start-up Diraq and UNSW Sydney, is detailed in the journal Nature Nanotechnology.

Continue reading “New spin control method brings billion-qubit quantum chips closer” »

Water molecules that are close enough to “see’’ each other but far enough apart to be gas-like can undergo a quantum phase transition, a finding of relevance for making future water-based quantum devices.

The new algorithm could render mainstream encryption powerless within years.

Chinese researchers claim to have introduced a new code-breaking algorithm that, if successful, could render mainstream encryption powerless within years rather than decades.

The team, led by Professor Long Guilu of Tsinghua University, proclaimed that a modest quantum computer constructed with currently available technology could run their algorithm, South China Morning Post (SCMP) reported on Wednesday.

Silently churning away at the heart of every atom in the Universe is a swirling wind of particles that physics yearns to understand.

No probe, no microscope, and no X-ray machine can hope to make sense of the chaotic blur of quantum cogs whirring inside an atom, leaving physicists to theorize the best they can based on the debris of high-speed collisions inside particle colliders.

Researchers now have a new tool that is already providing them with a small glimpse into the protons and neutrons that form the nuclei of atoms, one based on the entanglement of particles produced as gold atoms brush past each other at speed.

The world of the very, very small is a wonderland of strangeness. Molecules, atoms, and their constituent particles did not readily reveal their secrets to the scientists that wrestled with the physics of atoms in the early 20th century. Drama, frustration, anger, puzzlement, and nervous breakdowns abounded, and it is hard for us now, a full century later, to understand what was at stake. What happened was a continuous process of worldview demolition. You might have to give up believing everything you thought to be true about something. In the case of the quantum physics pioneers, that meant changing their understanding about the rules that dictate how matter behaves.

In 1913, Bohr devised a model for the atom that looked somewhat like a solar system in miniature. Electrons moved around the atomic nucleus in circular orbits. Bohr added a few twists to his model — twists that gave them a set of weird and mysterious properties. The twists were necessary for Bohr’s model to have explanatory power — that is, for it to be able to describe the results of experimental measurements. For example, electrons’ orbits were fixed like railroad tracks around the nucleus. The electron could not be in between orbits, otherwise it could fall into the nucleus. Once it got to the lowest rung in the orbital ladder, an electron stayed there unless it jumped to a higher orbit.

Clarity about why this happened started to come with de Broglie’s idea that electrons can be seen both as particles and waves. This wave-particle duality of light and matter was startling, and Heisenberg’s uncertainty principle gave it precision. The more precisely you localize the particle, the less precisely you know how fast it moves. Heisenberg had his own theory of quantum mechanics, a complex device to compute the possible outcomes of experiments. It was beautiful but extremely hard to calculate things with.

Long before we had quantum computers, classical computers, or even calculus, an ancient Greek philosopher known as Zeno of Elea used thought experiments to probe apparent paradoxes. Zeno imagined an arrow flying through the air. At each instant of time, he reasoned, the arrow is stationary. If the arrow’s trajectory is entirely composed of stationary instants, how can the arrow ever move through space? Motion is impossible!

Zeno’s ancient arrow paradox has since evolved into a quantum thought experiment, “the quantum Zeno effect,” whereby we can freeze the state of quantum systems by continuously observing them. In the latest installment of our Quantum Paradoxes content series, I explain the quantum Zeno effect, and show how we can test it out using Qiskit on quantum computers. Read on to find out how this counterintuitive quantum freezing works, and how to create your own quantum freezer game — which even works with entangled qubits! All the code you need is in this Jupyter Notebook, and you’ll also find a detailed explanation in our latest Quantum Paradoxes video.

Continue reading “The Quantum Zeno Effect: From Motionless Arrows to Entangled Freezers” »