Quantum Circuit Compilation : An Emerging Application for Automated Reasoning

Quantum computing is an information processing paradigm with the potential to solve certain problems faster than any algorithm running on classical computer architectures. In the next few years, new processors will be developed that support quantum computations exceeding the simulation ability of even the largest classical computer systems. A number of academic and industrial groups are developing prototypes of such devices, also known as NISQ (Noisy Intermediate Scale Quantum) processors. Much as software must be compiled to run on classical computers, quantum algorithms must be compiled to take into account the constraints of particular NISQ devices. Especially in these early prototypes, algorithm performance degrades with runtime due to noise; for this reason, minimizing the runtime of the compiled algorithm (which is represented by a ”quantum circuit”) is critical. We describe a software framework to enable an automated reasoning approach to Quantum Circuit Compilation for NISQ architectures (QCC-NISQ), and our current implementation of it as part of software suite for automated, architecture-aware, compilation for emerging quantum computers. The key components of this suite are a circuit synthesizer, a QCC solver, and a visualizer. These tools provide critical support for the continued development of practical quantum computers and research into quantum algorithms.

[1]  J. Christopher Beck,et al.  Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation , 2018, ICAPS.

[2]  Angelo Oddi,et al.  Greedy Randomized Search for Scalable Compilation of Quantum Circuits , 2018, CPAIOR.

[3]  Rupak Biswas,et al.  From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz , 2017, Algorithms.

[4]  Dmitri Maslov,et al.  Ground-state energy estimation of the water molecule on a trapped ion quantum computer , 2019, ArXiv.

[5]  Alexandru Paler.  On the Influence of Initial Qubit Placement During NISQ Circuit Compilation , 2017, QTOP@NetSys.

[6]  A. Harrow,et al.  Efficient distributed quantum computing , 2012, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Ryan LaRose,et al.  Overview and Comparison of Gate Level Quantum Software Platforms , 2018, Quantum.

[8]  Gian Giacomo Guerreschi,et al.  Two-step approach to scheduling quantum circuits , 2018, Quantum Science and Technology.

[9]  Travis S. Humble,et al.  Hybrid Programming for Near-Term Quantum Computing Systems , 2018, 2018 IEEE International Conference on Rebooting Computing (ICRC).

[10]  Mazyar Mirrahimi,et al.  Extending the lifetime of a quantum bit with error correction in superconducting circuits , 2016, Nature.

[11]  Yudong Cao,et al.  A framework for algorithm deployment on cloud-based quantum computers , 2018, 1810.10576.

[12]  Moinuddin K. Qureshi,et al.  Not All Qubits Are Created Equal: A Case for Variability-Aware Policies for NISQ-Era Quantum Computers , 2018, ASPLOS.

[13]  Gushu Li,et al.  Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices , 2018, ASPLOS.

[14]  Anupam Chattopadhyay,et al.  Depth-Optimal Quantum Circuit Placement for Arbitrary Topologies , 2017, ArXiv.

[15]  Akihiro Kishimoto,et al.  On the Complexity of Quantum Circuit Compilation , 2018, SOCS.

[16]  Ashley Montanaro,et al.  Achieving quantum supremacy with sparse and noisy commuting quantum computations , 2016, 1610.01808.

[17]  Martin Rötteler,et al.  Q#: Enabling scalable quantum computing and development with a high-level domain-specific language , 2018, ArXiv.

[18]  Angelo Oddi,et al.  An Innovative Genetic Algorithm for the Quantum Circuit Compilation Problem , 2019, AAAI.

[19]  Matthias Troyer,et al.  A software methodology for compiling quantum programs , 2016, ArXiv.

[20]  Nathan Killoran,et al.  PennyLane: Automatic differentiation of hybrid quantum-classical computations , 2018, ArXiv.

[21]  Ryan LaRose,et al.  Quantum-assisted quantum compiling , 2018, Quantum.

[22]  Margaret Martonosi,et al.  Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers , 2019, ASPLOS.

[23]  Matthias Troyer,et al.  ProjectQ: An Open Source Software Framework for Quantum Computing , 2016, ArXiv.

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

[25]  Michele Mosca,et al.  Quantum circuit optimizations for NISQ architectures , 2019, Quantum Science and Technology.

[26]  Colin P. Williams,et al.  Optimal quantum circuits for general two-qubit gates (5 pages) , 2003, quant-ph/0308006.

[27]  Stephen Brierley,et al.  Efficient implementation of quantum circuits with limited qubit interactions , 2015, Quantum Inf. Comput..

[28]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[29]  Andrew M. Childs,et al.  Circuit Transformations for Quantum Architectures , 2019, TQC.

[30]  Tyson Jones,et al.  Quantum compilation and circuit optimisation via energy dissipation , 2018 .

[31]  Aleks Kissinger,et al.  CNOT circuit extraction for topologically-constrained quantum memories , 2019, Quantum Inf. Comput..

[32]  Robert Wille,et al.  Compiling SU(4) quantum circuits to IBM QX architectures , 2018, ASP-DAC.

[33]  Frederic T. Chong,et al.  Toward a scalable, silicon-based quantum computing architecture , 2003 .

[34]  Lev S. Bishop.  QASM 2.0: A Quantum Circuit Intermediate Representation , 2017 .

[35]  Jeremy Frank,et al.  Compiling quantum circuits to realistic hardware architectures using temporal planners , 2017, ArXiv.