Journal
NEW JOURNAL OF PHYSICS
Volume 22, Issue 6, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1367-2630/ab8aaf
Keywords
machine learning; quantum circuits; quantum computing; supervised learning
Categories
Funding
- European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Grant [308253 PACOMANEDIA]
- UK EPSRC [EP/N031105/1, EP/S000267/1]
- DfE-SFI Investigator Programme [15/IA/2864]
- H2020 Collaborative Project TEQ [766900]
- Leverhulme Trust through the Research Project Grant UltraQuTe [RGP-2018-266]
- Royal Society Wolfson Fellowship scheme through project ExTraQCT [RSWF\R3\183013]
- Italian MIUR 'Rita Levi Montalcini' program for your young researchers
- Fondazione Angelo della Riccia
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We present a general framework to tackle the problem of finding time-independent dynamics generating target unitary evolutions. We show that this problem is equivalently stated as a set of conditions over the spectrum of the time-independent gate generator, thus translating the task into an inverse eigenvalue problem. We illustrate our methodology by identifying suitable time-independent generators implementing Toffoli and Fredkin gates without the need for ancillae or effective evolutions. We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques. In turn, this allows us to solve problems that are not amenable, in general, to direct analytical solution, providing at the same time a high degree of flexibility over the types of gate-design problems that can be approached. As a significant example, we find generators for the Toffoli gate using onlydiagonalpairwise interactions, which are easier to implement in some experimental architectures. To showcase the flexibility of the supervised learning approach, we give an example of a non-trivialfour-qubit gate that is implementable using only diagonal, pairwise interactions.
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