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MAY 2021 I DESIGN007 MAGAZINE 39 ENIG offers a surface finish that is ideal for complex surface components that cannot tol- erate uneven surfaces. It is also lead-free and durable. e process can be more expensive and comes with some risk. Phosphorus has been known to build up between the gold and nickel layers, sometimes resulting in fractured sur- faces and faulty connections, oen called black pad. Many of the newer chemistry formulations have all but eliminated this possible defect. Whether your priority is cost-effectiveness, manufacturability, compliance, or some com- bination of the three, any of these surface fin- ishes will allow a good solder joint to be formed. Which method you choose will be a function of your application's requirements. DESIGN007 Matt Stevenson is the VP of sales and marketing at Sunstone Circuits. To read past columns or contact Stevenson, click here. Scientists from the University's Quantum Engi- neering Technology Labs (QETLabs) have devel- oped an algorithm that provides valuable insights into the physics underlying quantum systems—pav- ing the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation. In physics, systems of particles and their evolu- tion are described by mathematical models, requir- ing the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from observations is made even harder by the nature of quantum states, which collapse when attempts are made to inspect them. In the paper, "Learning Models of Quantum Sys- tems From Experiments," published in Nature Phys- ics, quantum mechanics from Bristol's QET Labs describe an algorithm which overcomes these chal- lenges by acting as an autonomous agent, using machine learning to reverse engineer Hamiltonian models. The team developed a new protocol to formu- late and validate approximate models for quantum systems of interest. Their algorithm works autono- mously, designing and performing experiments on the targeted quantum system, with the resultant data being fed back into the algorithm. It proposes candidate Hamiltonian models to describe the tar- get system, and distinguishes between them using statistical metrics, namely Bayes factors. Excitingly, the team were able to suc- cessfully demonstrate the algorithm's ability on a real-life quantum experiment involving defect centres in a diamond, a well-studied platform for quantum informa- tion processing and quantum sensing. "This level of automation makes it pos- sible to entertain myriads of hypothetical models before selecting an optimal one, a task that would be otherwise daunting for systems whose complexity is ever increas- ing," said Andreas Gentile, formerly of Bristol's QETLabs, now at Qu & Co. (Source: University of Bristol) Machine Learning Algorithm Helps Unravel the Physics Underlying Quantum Systems

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