Dwaipayan Biswas

Indian Statistical Institute kolkata

MIT researchers have developed a process to manufacture and integrate "artificial atoms," created by atomic-scale defects in microscopically thin slices of diamond, with photonic circuitry, producing the largest quantum chip of its type.

The accomplishment "marks a turning point" in the field of scalable quantum processors, says Dirk Englund, an associate professor in MIT's Department of Electrical Engineering and Computer Science. Millions of quantum processors will be needed to build quantum computers, and the new research demonstrates a viable way to scale up processor production, he and his colleagues note.

Unlike classical computers, which process and store information using bits represented by either 0s and 1s, quantum computers operate using quantum bits, or qubits, which can represent 0, 1, or both at the same time. This strange property allows quantum computers to simultaneously perform multiple calculations, solving problems that would be intractable for classical computers.

The qubits in the new chip are artificial atoms made from defects in diamond, which can be prodded with visible light and microwaves to emit photons that carry quantum information. The process, which Englund and his team describe in Nature, is a hybrid approach, in which carefully selected "quantum micro chiplets" containing multiple diamond-based qubits are placed on an aluminum nitride photonic integrated circuit.

"In the past 20 years of quantum engineering, it has been the ultimate vision to manufacture such artificial qubit systems at volumes comparable to integrated electronics," Englund says. "Although there has been remarkable progress in this very active area of research, fabrication and materials complications have thus far yielded just two to three emitters per photonic system."

Using their hybrid method, Englund and colleagues were able to build a 128-qubit system—the largest integrated artificial atom-photonics chip yet.

Quality control for chiplets

The artificial atoms in the chiplets consist of color centers in diamonds, defects in diamond's carbon lattice where adjacent carbon atoms are missing, with their spaces either filled by a different element or left vacant. In the MIT chiplets, the replacement elements are germanium and silicon. Each center functions as an atom-like emitter whose spin states can form a qubit. The artificial atoms emit colored particles of light, or photons, that carry the quantum information represented by the qubit.

Quantum information scientists have introduced a new method for machine-learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.

Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.

More recently, the kernel method has been introduced in quantum machine learning with great success. The ability of quantum computers to efficiently access and manipulate data in the quantum feature space can open opportunities for quantum techniques to enhance various existing machine learning methods.

The idea of the classification algorithm with a nonlinear kernel is that given a quantum test state, the protocol calculates the weighted power sum of the fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements (see Figure 1). This requires only a small number of quantum data operations regardless of the size of data. The novelty of this approach lies in the fact that labeled training data can be densely packed into a quantum state and then compared to the test data.

From the given problem, we have the quantum numbers:

$$\begin{align} n = 4, \qquad l = 0, \qquad m_l = 0, \qquad m_s = - \frac{1}{2}, 0,

Zirconium Zr has an electron configuration of 1s22s22p63s23p63d104s24p64d25s21s22s22p63s23p63d104s24p64d25s2

By counting the number of electrons in s orbitals, which are spherical in shape, we can arrive with the electrons that occupy spherical orbitals.

electrons=10electrons=10

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The experimental investigation of ultracold quantum matter makes it possible to study quantum mechanical phenomena that are otherwise inaccessible. A team led by the Innsbruck physicist Francesca Ferlaino has now mixed quantum gases of two strongly magnetic elements, erbium and dysprosium, and created a dipolar quantum mixture.

A few years ago, it seemed unfeasible to extend the techniques of atom manipulation and deep cooling in the ultracold regime to many-valence-electron atomic species. The reason is the increasing complexity in the atomic spectrum and the unknown scattering properties. However, a team of researchers, led by Ben Lev at Stanford University and an Austrian team directed by Francesca Ferlaino at the University of Innsbruck demonstrated quantum degeneracy of rare-earth species. Ferlaino's group focused the research on erbium and developed a powerful, yet surprisingly simple approach to produce a Bose-Einstein condensate.