At INL we have groups that solve quantum problems for a living. There is a good reason for that.
Nature has resources that had to be tamed before they could be used. Take electricity. It is everywhere. Every atom has electric currents. Cavemen were aware of electric phenomena such as lightning, but to them, the concepts of electric charges and currents would sound as weird as “quantum entanglement” sounds to us now. It took experimentation and theory to understand and harness electricity. So, at INL we want to understand quantum entanglement and use it .
The group of Theory of Quantum Nanostructures is working to engineer and exploit “quantum entanglement”, something that would have applications in quantum computation and quantum sensing.
Quantum entanglement is a metric of how different parts in a quantum system do not behave independently, even if they are far apart. Entanglement occurs naturally in all quantum systems, but the right type of entanglement needs to be prepared, very much like electrical currents are everywhere, but they are only useful in the right amount.
There are two main approaches to achieving useful quantum entanglement. On one hand, quantum computers are made of “qubits” that, most of the time, do not talk to each other. Useful entanglement is prepared by driving the qubits out very far from equilibrium using carefully engineered electromagnetic radiation pulses that connect qubits, pairwise, for short periods of time. A second approach explored at TQN – Theory of Quantum Nanostructures is to have systems where different parts interact strongly with each other, in very specific and fine-tuned ways, leading to useful entanglement in thermodynamic equilibrium. For instance, the TQN group has recently shown that an array of triangular-shaped graphene molecules hosts a special quantum state, called Valence Bond Solid, whose entanglement can be used to carry out the so-called measurement-based quantum computing.
Part of the work of the TQN – Theory of Quantum Nanostructures, and many other groups worldwide, is to model quantum systems using conventional computers to crack down the equations. It is known that this approach has serious shortcomings that could be bypassed using quantum computers. So, we are exploring quantum algorithms that could help us using quantum computers to understand quantum systems. This would close the circle: using quantum computing to understand quantum systems that can be used to do better quantum computers.
Article by Joaquín Fernández-Rossier