![]() For a large system of even a few dozen ions, the optimization can take several hours on a powerful (classical) computer. ![]() However, to program the interaction parameters between qubits to simulate a specific quantum system, a non-linear optimization is necessary to determine the laser parameters. In this case, if the intensities and frequencies of laser beams shining on individual ion-qubits are known, the interactions between qubits can be calculated easily. ![]() This is an example of a so-called inverse problem, where calculating the output variable from a set of input variables is trivial but predicting the input variables from a target set of output variable-values is highly non-trivial sometimes the problem can even be ill-posed. The possibility to tune the qubit-qubit interactions creates enormous opportunity to use the ion system as a simulator for many-particle quantum phenomena.Įxamples include studying quantum magnetism and quantum phase transitions, computing molecular energies in quantum chemistry with potential applications in drug-discovery as well as simulating fundamental forces in nature to answer open questions like “why do we have more matter than anti-matter?įollowing a number of informal discussions, Melko and Islam realized that a neural network could be trained to program a target set of interaction patterns between ion-qubits in a quantum simulator. A remarkable feature of the trapped ion system is the presence of long-range interactions with, in principle, the interaction between any pair of qubits being controllable. By appropriately controlling the intensity and frequency of the laser beams, ion-qubits can be made to interact with each other in a programmable way. Each ion can be made to behave like a qubit – a quantum register of states labeled 0 and 1 and their infinitely many superpositions – that can be manipulated by shining with laser beams. At sub-milliKelvin temperature, the motion of each ion is nearly frozen as they are trapped and levitated inside an ion-trapping apparatus in an ultra-high vacuum environment. Rajibul Islam and colleagues are developing a quantum information processor made of laser-cooled atomic ytterbium ions. ![]() In his laboratory for Quantum Information with Trapped Ions (QITI) at the Institute for Quantum Computing at the University of Waterloo, Prof. Roger Melko is leading a group of researchers at the University of Waterloo and PIQuIL to understand the complex physics of many-particle systems, such as exotic states of matter and phase transitions using machine learning techniques.Ī quantum information processor harnesses the power of quantum superposition and quantum entanglement for increased computational capability. Depending on the complexity of the problem, a neural network can have complicated connections between the input and output variables through one or multiple ‘hidden layers’. Most commonly, ‘artificial neural networks’ are trained with lots of data, either real or synthetic, to learn patterns in data and to estimate the functional relationship between the input and output variables. Machine learning methods have replaced traditional instruction-driven computer algorithms in many areas affecting our daily lives, such as facial recognition and self-driving cars. ![]()
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