Giulia Meuli receives the EDEE best doctoral Thesis Award

Abstract

Practical realizations of quantum computers are poised to deliver outstanding computational capabilities far beyond the reach of any classical supercomputer. While classical systems are reliably implemented using CMOS technology, the fabrication of quantum hardware has not yet reached a commercially viable level of maturity. Nowadays, many different technologies are being developed as competing candidates to build the first error-corrected quantum computer.

Among the software resources required to operate such a system, quantum compilers translate a high-level description of a quantum algorithm into low-level, technologydependent quantum instructions. Many quantum algorithms, including Shor’s and Grover’s algorithms, require computing some classical logic functions on the quantum computer. The first part of this thesis deals with the problem of compiling quantum circuits that perform such classical Boolean functions, called oracles, into a fault-tolerant set of instructions. Oracle circuits often demand many resources in terms of the number of qubits and gates. The focus is on compiling quantum circuits starting from multi-level logic networks representing large Boolean functions while minimizing the resource footprint of the obtained quantum circuits. At the same time, the trade-off between memory resources and the number of operations typical of such a compilation process is explored. As part of the effort in reducing the resources required to perform a given quantum program, I also present some quantum circuit optimization techniques.

The researched algorithms leverage data structures and techniques borrowed and adapted from classical logic synthesis, e.g., SAT solvers, LUT mapping, and multi-level logic networks. I implemented all the compilation algorithms presented in this thesis as part of open-source projects. In particular, I develop and maintain caterpillar—a C++ header-only library dedicated to the quantum compilation of oracles and quantum memory management.

The second part of this thesis describes how to equip quantum programming language compilers with automatic accuracy management. Despite the availability of many of such languages, resource estimation of quantum algorithms does not yet support taking approximation errors into account. This general methodology is applicable to any programming language and I demonstrate its integration into the Q# compiler. The technique consists of providing language support to ease the accuracy-aware programming task. The user can define accuracy parameters that will be automatically optimized according to a constraint and a cost function directly generated from the source code. In the presented practical evaluations, the constraint function is the overall allowed accuracy, while the cost function is application-dependent and related to the number of operations. During the optimization process, such functions are evaluated hundreds of times. For this reason, they are extracted as (near-)symbolic expressions, whose evaluation time does not depend on the quantum algorithm size. The algorithms and the methodologies presented in this thesis are part of a widespread effort of the research community to build a complete and efficient software stack to program and control the first practical universal quantum computer.