We figured out a new way to read out the quantum states of molecular spin qubits using electrical conductance measurements. The trick is to put pairs of molecular qubits on top of special carbon-based nanowires (like graphene or carbon nanotubes) and measure how easily electrons flow through. When the qubits are in an entangled singlet state, electrons pass through easily, but when they're in a triplet state, the current gets blocked. Using advanced quantum simulations, we showed this "quantum spin valve" effect works even with many electrons flowing at once, not just single electrons.
The key discovery is that materials with flat electronic bands, where electrons have very similar energies, make this measurement much more reliable. In flat-band materials like twisted graphene or carbon nanotubes under a magnetic field, we can distinguish the quantum states twice as well as in regular materials. This opens up a practical path for reading out molecular qubits without needing the complex gating and tunneling control required for semiconductor qubits, potentially making molecular quantum computers easier to build and scale up.
We developed a method to entangle molecular spin qubits by scattering a single electron off them. The challenge is that when electrons interact with qubits, the physics gets complicated, so you can't just treat the electron like a simple particle anymore. We solved this using a mathematical technique called Green's functions that handles all the quantum interactions properly. Our approach can model real molecular qubit systems by incorporating actual material properties like magnetic interactions from first-principles calculations. The key finding is that by measuring the spin of the scattered electron afterward, you can probabilistically control how entangled the qubits become, essentially using the electron as a messenger to link the qubits together in a controllable way.
My training is in computational molecular physics, which leverages modern computing technology to describe the physics of many-electron quantum mechanical systems such as molecules. There are millions of known molecules, each with different physical properties, and each of these can be tweaked in a multitude of ways, such as charge doping, ligand addition, or strain, leading to even more variety. Furthermore, all this occurs in nanometer scale systems where quantization effects become important, making molecules attractive candidates for quantum technologies.
My particular interests lie at the intersection of molecule-based quantum technology and quantum computing, namely the realization of molecular spin qubits (MSQs). These offer the opportunity to leverage quantum mechanics to store and process information extremely efficiently. Such a realization is challenging because of the stringent physical conditions quantum computers must satisfy. As such, more theoretical work is needed to determine how well the MSQ candidates that have been experimentally proposed would operate in a real quantum computer, and that is where I come in. My current project explores what types of molecules are best suited for scattering based quantum entanglement. I have written what I hope is an accessible introduction to the nuts and bolts of this work here.
The past two decades have seen intense research into how quantum mechanical systems can store and process information. However, existing quantum computers remain functionally limited, in large part due to their small number of information storage units (qubits). Molecular physics offers a new frontier of scalable hardware for quantum computers.
What I enjoy most about working in quantum computing hardware is that we are developing technology, not just doing basic science. Superconducting qubits are the most viable quantum hardware out there right now, but have not yet matured, and other hardware such as MSQs are even less developed. However, we have the advantage of being able to look at the other technologies out there as a roadmap of the engineering challenges that need to be overcome. At the same time, we need to justify how our less mature hardware could some day be better than the current state of the art. This requires taking a really discerning look at potential weaknesses of the other technologies. I find this mindset of doing science as if I'm comparing and contrasting commercial technologies to be incredibly rewarding.