A synthetic dimension, in which a discrete degree of freedom in a well-controlled quantum system can be mapped to the states of particles moving in a real-space lattice potential, is a powerful tool for quantum simulation because it provides control over the Hamiltonian and the ability to create configurations difficult to access in real space. I will describe the creation of a synthetic...
We propose a protocol for the generation of effective universal nonlinear Kerr Hamiltonians in a collective-spin system coupled to bosonic modes of a cavity QED apparatus. We expand the effective collective spin Hamiltonian beyond the second-order term (the well-studied one-axis-twisting) and map it to an effective Kerr Hamiltonian using the Holstein-Primakoff transformation. We give examples...
We have pioneered quantum machine learning to make a breakthrough in quantum machine learning on the target of particle physics data challenges. We have investigated from quantum support vector machines of collision event classification to quantum anomaly detection on novel event discovery. We will share our success and failure and outlook of upcoming quantum centric supercomputing.
Spin-boson models are common throughout physics. Trapper ion quantum computers are built off internal degrees of freedom in the ions (qubits) and external degrees of freedom (phonons). For quantum computation, the phonons are used as an information bus for generating entanglement between qubits, but are not used to store quantum information. We have recently used the spin and motional modes to...
Analog quantum simulators are purpose-built devices that imitate the behavior of complex quantum systems. Compared to universal error-corrected digital quantum computers, they are expected to have less stringent requirements, and are capable of natively representing the degrees of freedom and interactions in the target system with reduced overhead. In this talk, I will present our recent work...
