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Quantum Numbers02:43

Quantum Numbers

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It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
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Hückel's Rule Diagram of π MOs: Frost Circle01:08

Hückel's Rule Diagram of π MOs: Frost Circle

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The Frost circle or the inscribed polygon method is a graphical method for determining the relative energies of π molecular orbitals (MOs) for planar, fully conjugated, and monocyclic compounds. This method was first described by A. A. Frost and Boris Musulin in 1953.
A Frost circle is constructed by drawing a polygon whose number of edges is equal to the number of carbons of the given cyclic system, with one of the vertices pointing down. Then, a circle is drawn enclosing the polygon so...
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Surface Tension and Surface Energy01:16

Surface Tension and Surface Energy

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When a paint brush is immersed in water, the bristles wave freely inside the water. When it is taken out, the bristles stick together. The reason behind this effect is surface tension.
Consider a beaker filled with liquid. The bulk molecules in the liquid experience equal attractive forces on all sides with the surrounding molecules. However, the surface molecules experience a net attractive force downward due to the bulk molecules. The surface of the liquid behaves like a stretched membrane,...
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Prismatic Beams: Problem Solving01:15

Prismatic Beams: Problem Solving

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In the design of a supported timber beam subjected to a distributed load, both the beam's physical dimensions and the timber's characteristics, such as its grade and species, are critical. These factors determine the allowable stress values, which are crucial for calculating the necessary beam depth to ensure structural integrity and safety.
The design begins with analyzing the beam as a free body to identify moments and force balances, thereby determining support reactions. Next, the...
203
Metallic Solids02:37

Metallic Solids

18.7K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
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Equipotential Surfaces and Conductors01:16

Equipotential Surfaces and Conductors

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For a conductor in which all charges are at rest, the conductor's surface is equipotential. The electric field is always perpendicular to equipotential surfaces. Therefore, in a conductor with static charges, the electric field just outside the conductor is always perpendicular to the conductor's surface. Any tangential component of the electric field will cause charges to move inside the conductor, which will violate the electrostatic nature of the system. In an electrostatic...
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Updated: Sep 10, 2025

Rendering SiO2/Si Surfaces Omniphobic by Carving Gas-Entrapping Microtextures Comprising Reentrant and Doubly Reentrant Cavities or Pillars
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MITS: A Quantum Sorcerer's Stone for Designing Surface Codes.

Avimita Chatterjee1, Debarshi Kundu1, Swaroop Ghosh2

  • 1Department of Computer Science & Engineering, The Pennsylvania State University, State College, PA 16801, USA.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

Optimizing quantum error correction (QEC) requires efficient parameter tuning. A new reverse engineering tool, MITS, automatically finds optimal QEC settings, minimizing resource usage for quantum computing.

Keywords:
distancelogical error ratemachine learningphysical error ratequantum error correction codesroundssurface codesthreshold

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Area of Science:

  • Quantum Computing
  • Quantum Information Science

Background:

  • Quantum Error Correction (QEC) parameter optimization is vital due to diverse physical noise in quantum computers.
  • Traditional forward simulation methods for deriving logical error rates can be resource-intensive.
  • Manual adjustment of QEC parameters is inefficient due to daily quantum error rate fluctuations.

Purpose of the Study:

  • To introduce MITS, a novel reverse engineering tool for STIM.
  • To automate the determination of optimal QEC parameters based on specific quantum hardware noise models and target logical error rates.
  • To minimize qubit and gate utilization by aligning QEC settings with hardware constraints.

Main Methods:

  • Developed MITS, a reverse engineering tool that interfaces with STIM.
  • Investigated various heuristic and machine learning models for parameter optimization.
  • Utilized XGBoost and Random Forest regression models.

Main Results:

  • MITS automatically determines optimal QEC settings for given noise models and target error rates.
  • XGBoost and Random Forest models demonstrated high effectiveness, achieving Pearson correlation coefficients of 0.98 and 0.96, respectively.
  • The approach minimizes qubit and gate usage by precisely matching logical error rates with hardware constraints.

Conclusions:

  • MITS offers an efficient solution for optimizing QEC parameters in quantum computing.
  • Automated parameter tuning using machine learning significantly improves resource efficiency.
  • This method is crucial for advancing practical quantum error correction strategies.