Quantum Logic Synthesis with Formal Verification

Quantum computers capable of practical information processing are emerging rapidly. As these devices become more advanced, tools will be needed for converting generalized quantum algorithms into formally-verified forms that are executable on real quantum machines. In this work, a prototype tool is presented that transforms quantum algorithms into executable specifications where optimization procedures yield 9-24 % cost improvement on a range of benchmarks. Additionally, the tool incorporates formal verification internally with Quantum Multiple-valued Decision Diagrams to confirm that the generated technology-dependent executable is functionally equivalent to the original, technology-independent algorithm. Experimental results are provided that target the Rigetti family of quantum processing units although the tool may also target other architectures.

[1]  Ross Duncan,et al.  On the qubit routing problem , 2019, TQC.

[2]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[3]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[4]  Barenco,et al.  Elementary gates for quantum computation. , 1995, Physical review. A, Atomic, molecular, and optical physics.

[5]  N. Didier,et al.  Analytical modeling of parametrically-modulated transmon qubits , 2017, 1706.06566.

[6]  Robert Wille,et al.  Improved synthesis of Clifford+T quantum functionality , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[7]  D. Michael Miller,et al.  QMDD: A Decision Diagram Structure for Reversible and Quantum Circuits , 2006, 36th International Symposium on Multiple-Valued Logic (ISMVL'06).

[8]  William J. Zeng,et al.  A Practical Quantum Instruction Set Architecture , 2016, ArXiv.

[9]  Sabrina Hong,et al.  Demonstration of universal parametric entangling gates on a multi-qubit lattice , 2017, Science Advances.

[10]  Blake R. Johnson,et al.  Unsupervised Machine Learning on a Hybrid Quantum Computer , 2017, 1712.05771.