quantum computers

Table of Contents

1. Quantum Computers   quantum_computers

https://quantum.country/
A free introduction to quantum computing and quantum mechanics

1.2. Control Problem of Quantum Mechanics

1.2.2. Consiguen resolver problema de control

https://link.medium.com/6WQAorKaljb
Control problem of quantum computers
Spin-based silicon quantum electronic circuits offer a scalable platform for quantum computation, combining the manufacturability of semiconductor devices with the long coherence times afforded by spins in silicon

1.2.3. More skepticism on quantum computers

“More skepticism on quantum computers” https://link.medium.com/epPGAmfP02

1.2.3.1. https://spectrum.ieee.org/the-case-against-quantum-computing
  • Too much variables to control (\(10^{300}\) continuous parameters)
  • Correction creating a logical qubit from many physical qubits does not solve the problem
1.2.3.2. Topological or bust!

1.3. Quantum Computers applied to Machine Learning   ML ml_physics

  1. optimize a loss function with stochastic gradient descent and
  2. use kernel methods to extend linear learning tasks to non-linear learning tasks

Many machine learning algorithms optimize a loss function with stochastic gradient descent and use kernel methods to extend linear learning tasks to non-linear learning tasks. Both ideas have been discussed in the context of quantum computing, especially for near-term quantum computing with variational methods and the use of the Hilbert space to encode features of data. In this work, we discuss a quantum algorithm with provable learning guarantee in the fault-tolerant quantum computing model. In a well-defined learning model, this quantum algorithm is able to provide a polynomial speedup for a large range of parameters of the underlying concept class. We discuss two types of speedups, one for evaluating the kernel matrix and one for evaluating the gradient in the stochastic gradient descent procedure. Our work contributes to the study of quantum learning with kernels and noise.

1.4. Algorithmic Qubits: A Better Single-Number Metric

Links to github repository implementing benchmarks for quantum computers

1.5. Implementations

1.5.1. Graphene valleytronics: Paving the way to small-sized room-temperature quantum computers (storage of information at least)

https://phys.org/news/2021-09-graphene-valleytronics-paving-small-sized-room-temperature.html
they present a way to perform valley operations in monolayer or pristine graphene, which was assumed to be impossible by other researchers in the field.
this enables the use of graphene’s valleys to effectively “write” information
One of the most attractive aspects of conducting valley operations in graphene is that it’s possible to do so at room temperature

1.5.2. Hasta la vista, ordenadores cuánticos grandes y complicados; los ordenadores cuánticos de escritorio están decididos a pasaros por encima

https://www.xataka.com/investigacion/vista-ordenadores-cuanticos-grandes-complicados-ordenadores-cuanticos-escritorio-estan-decididos-a-pasaros-encima
Qubits estables a temperatura ~ ambiente usando spines de núcleos atómicos en vez de spines de electrones
Tiempo de coherencia de 100-150 µs

Sería muy interesante si fuera posible. El documento habla, además de las enormes ventajas y oportunidades que presenta su desarrollo, sobre la dificultad que supone escalar la idea a más de 5 qubits porque, para conseguirlo, debe ser capaces de mejorar la fabricación de los diamantes con los qubits en su interior, algo que no tienen resuelto.

Key to scaling beyond a handful of qubits and nodes is the precise fabrication of arrays of NV centers that are separated by a few nanometers. This precision is required to magnetically-couple the electron spins of the NV centers so that they may mediate the inter-node multi-qubit operations. However, this precision cannot be achieved with high yield using the existing ‘top-down’ nitrogen ion-implantation techniques for creating NV centers, owing to the limits of implantation mask fabrication and the scattering of implanted ions.
One of Quantum Brilliance’s key inventions is a ‘bottom-up’ atomically-precise fabrication technique for diamond that circumvents these limitations through designer surface chemistry and lithography. The technique draws inspiration from the atom-scale fabrication techniques for silicon that were pioneered in Australia.

1.5.3. Topological

1.5.3.1. Topological Quantum Computing

https://link.medium.com/Qp3r8he662

1.5.3.2. A Short Introduction to Topological Quantum Computation   arxiv

https://arxiv.org/abs/1705.04103

1.6. Un «¡Hola, mundo!» en siete lenguajes de programación cuántica

https://www.microsiervos.com/archivo/ordenadores/hola-mundo-siete-lenguajes-programacion-cuantica.html

Author: Julian Lopez Carballal

Created: 2024-09-16 Mon 06:59