Researchers Speed Up Quantum Computing with Advances from Algorithms to Sensors

Researchers Speed Up Quantum Computing with Advances from Algorithms to Sensors

Lately, quantum research has been reaching new heights; whether it’s initiatives in Germany, the push for a quantum internet, or photonic chips, this field is booming.


The push for quantum technology is a global effort, with many countries investing large amounts of funding.

The push for quantum technology is a global effort, with many countries investing large amounts of funds. Image used courtesy of Qureca


The momentum keeps rolling as recent research into quantum computing and related technologies has brought three innovative potential approaches for advancing the quantum field. 

These developments include overcoming a significant quantum computing challenge, creating a candidate quantum sensor for detecting dark matter particles, and developing a platform for running quantum software on classic semiconductor hardware. 

This article will dive into each one to see what’s new and what the future may hold. 


A Solution to a Critical Quantum Computing Problem

The first research to be covered tries to answer one of quantum computing’s major challenges. 

The challenge is that, for a quantum computer to have any practical application, it must use thousands if not millions of qubits to operate. Current quantum computers have yet to accomplish this as one of the biggest challenges in quantum computing is controlling a large number of qubits without having to use extensive wiring and electrical energy. 

Controlling millions of qubits in this way is nearly impossible, as the space constraints combined with the heat dissipation from high current consumption will cause a rise in temperatures. These temperatures can make it impossible to get reliable qubit readouts in what is supposed to be a near absolute zero environment (for proper functionality without interference, current quantum computer technologies require temperatures in the millikelvin to operate).

The proposed solutions come from engineers at UNSW Sydney. These researchers have developed a possible method that involves using a crystal material component for magnetically controlling multiple qubits at once. 


Images of (a) 3D render of the microwave control field, (b) the device stack, and (c) the photo of the device. Image used courtesy of Vahapoglu et al


The component in question is called a dielectric resonator capable of focusing microwave frequency magnetic control fields into a qubit system uniformly. This uniformity means that millions of qubits could be controlled simultaneously using just a single element.

However, this solution wasn’t just the efforts of just one team. It was a joint effort between a team led by Professor Jarryd Pla (responsible for the innovation in question) and another led by Professor Andrew Dzurak (who has developed quantum chips using silicon manufacturing technologies in the past). 

Although quantum computers still face many engineering challenges, developing a way to control millions of qubits with just one component uniformly could be a valuable step forward in taking the quantum processor to the next level.

The next area of research hopes to create a new type of quantum sensor.


Quantum Sensors for Detecting Dark Matter

The second research this article will talk about has to do with dark matter.

Dark matter is an umbrella term used to describe hypothetical particles in the universe that still haven’t been seen or scientifically proven, even though astrophysical observations and unexplained gravitational effects imply their presence. Physicists have been trying to directly detect dark matter to prove its existence and understand its composition via many different scientific methods.

Hoping to answer the physicist’s call, researchers from the National Institute of Standards and Technology (NIST) are inching closer to this objective by developing a quantum ion trap sensor for measuring specific electric fields. The sensor in question is crystal-based, made out of 150 beryllium ions forming a two-dimensional structure trapped in a magnetic field. 


John Bollinger (left) and Matt Affolter (right) using the quantum sensor. Image used courtesy of R. Jacobson/NIST


The NIST physicists hypothesize that this sensor could detect theoretical subatomic particles such as axions and dark photons, which are possible dark matter components. 

It would work by detecting the weak electric field of these hypothetical particles through measuring movements in the crystal’s ion structure, specifically in their spin, which is a quantum property describing the intrinsic angular momentum of a particle.

This sensor could be an interesting technology as it uses similar principles to ion trap quantum computers, which means that this research could benefit from advancements in the quantum computing field. However, what’s more important is that quantum chips could also benefit from the improvements made in developing these types of sensors.

Currently, the usefulness and practicality of any evidence of dark matter particles are unknown; however, the researchers behind this project believe that with some improvements, this experiment could become a fundamental resource in detecting and understanding what the unexplained and unaccounted matter in our universe is.

From here, let’s dive into the final piece of research striving to make waves in quantum computing. 


Running Quantum Software on Classical Computers

Quantum computers are an important future technology as the speed at which they can calculate complex mathematical problems is orders of magnitude faster than regular computers. However, the apparent physical limitations and difficulties that quantum processors still face are leading researchers into multiple different directions in overcoming these challenges.

An interesting approach is not using quantum chips but rather simulating quantum algorithms in classical semiconductor computers. EPFL Professor Giuseppe Carleo and Matija Medvidović (a graduate student from Columbia University and the Flatiron Institute) are attempting to do to bring quantum computing closer to reality.


A QUOA circuit. Image used courtesy of Carleo and Medvidović


The software in question is called a quantum approximate optimization algorithm or QAOA for short, which is used to solve mathematical optimization problems by picking the best solution from a set of possible solutions. 

Due to the nature of quantum computers, this type of quantum algorithm would theoretically be able to solve complex calculations in seconds compared to algorithms that run on semiconductor processors. However, simulating these algorithms requires machine learning to properly emulate and execute a limited version of a working quantum processor. 

This research was done using professor Carleo’s artificial neural network tool developed back in 2016 and is the first time a QAOA was simulated in a classical computer. 

Developing simulated quantum software in the absence of quantum computers is a big step towards understanding and advancing quantum computing. This research could allow some of the most promising quantum computing algorithms to be experimented with and worked on even before developing a powerful quantum computer.


Looking Towards the Future 

These three technologies are just some of the most recent advancements in the quantum field as companies and universities are entering it, each with unique ideas and approaches. 

Research is fundamental as quantum computing seeks to become a useful future technology promising advancements in many other fields. 

Lowering the entry point into quantum computing by developing software and hardware solutions to the complex infrastructure problem that this technology exhibit is one key way for it to become more accessible to a wider range of researchers. Solving problems like the wiring of qubits and even simulating quantum algorithms like QAOA outside of quantum chips is a valuable step forward in speeding up quantum computing development.

Quantum sensors are also crucial for advancing quantum technologies. However, this specific use in detecting hypothetical dark matter particles becomes not only interesting from a physics point of view but also from an engineering point of view in the hopes of finding practical applications and uses for the properties of these potential particles.

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