Over the last decade, Quantum Computing hardware has rapidly developed and become a very intriguing, promising, and active research field among scientists world-wide. To achieve the desired quantum functionalities, quantum algorithms require translation from a high-level description to a machine-specific physical operation sequence. In contrast to classical compilers, state-of-the-art quantum compilers are in their infancy. We believe there is a research need for a quantum compiler that can deal with generic unitary operators and generate basic unitary operations according to quantum machines' diverse underlying technologies and characteristics. In this work, we introduce the Quantum Netlist Compiler (QNC) that converts arbitrary unitary operators or desired initial states of quantum algorithms to OpenQASM-2.0 circuits enabling them to run on actual quantum hardware. Extensive simulations were run on the IBM quantum systems. The results show that QNC is well suited for quantum circuit optimization and produces circuits with competitive success rates in practice.
Over the last decade, Quantum Computing hardware has rapidly developed and become a very intriguing, promising, and active research field among scientists world-wide. To achieve the desired quantum functionalities, quantum algorithms require translation from a high-level description to a machine-specific physical operation sequence. In contrast to classical compilers, state-of-the-art quantum compilers are in their infancy. We believe there is a research need for a quantum compiler that can deal with generic unitary operators and generate basic unitary operations according to quantum machines' diverse underlying technologies and characteristics. In this work, we introduce the Quantum Netlist Compiler (QNC) that converts arbitrary unitary operators or desired initial states of quantum algorithms to OpenQASM-2.0 circuits enabling them to run on actual quantum hardware. Extensive simulations were run on the IBM quantum systems. The results show that QNC is well suited for quantum circuit optimization and produces circuits with competitive success rates in practice.
We introduce an open source software package UniversalQCompiler written in Mathematica that allows the decomposition of arbitrary quantum operations into a sequence of single-qubit rotations (with arbitrary rotation angles) and controlled-NOT (C-NOT) gates. Together with the existing package QI, this allows quantum information protocols to be analysed and then compiled to quantum circuits. Our decompositions are based on Phys. Rev. A 93, 032318 (2016), and hence, for generic operations, they are near optimal in terms of the number of gates required. UniversalQCompiler allows the compilation of any isometry (in particular, it can be used for unitaries and state preparation), quantum channel, positive-operator valued measure (POVM) or quantum instrument, although the run time becomes prohibitive for large numbers of qubits. The resulting circuits can be displayed graphically within Mathematica or exported to LaTeX. We also provide functionality to translate the circuits to OpenQASM, the quantum assembly language used, for instance, by the IBM Q Experience.
The recent times of fast development of quantum computers has been followed by many researchers who want to run the larger and more difficult quantum algorithms on it and they have promised such speedup several times. Notwithstanding this, commercial quantum computers are currently fraught with instability and errors. This paper is centered on development of the customized compiler for quantum computing with aid of Qiskit Terra, a QIS framework. We target central classes such as circuit representation structures, programming techniques based on OpenQASM, challenges in the QC architecture, high-level routing optimization, and the structure of the Qiskit Terra compiler. In every circuit, the optimization is provided by methods of Qiskit Terra, including Trivial Layout, Noise Adaptive Layout, Dense Layout, and Custom Layout. These optimization levels address many problems that emerge when circuits are being compiled and executed, and thus, the optimization levels seek to boost circuit efficiency, reduce the count of gates, and enhance the overall performance. The introduction of numerous optimization passes starting from unroller passes that translate gates, the layout passes that map virtual to physical qubits, and the optimization algorithm aimed at the runtime reduction. Modern quantum compilation technologies are being currently developed based on economic (and theoretical) analyses to underpin the investigation and development of optimal quantum computation procedures.
Quantum computing is a promising technology for high-performance computation, but requires mature toolflows that can map large-scale quantum programs onto targeted hardware. In this paper, we present a scalable compiler for large-scale quantum applications, and show the opportunities for reducing compilation and analysis time, as well as output code size. We discuss the similarities and differences between compiling for a quantum computer as opposed to a classical computer, and present a state-of-the-art approach for compilation of classical circuits into quantum circuits. Our work also highlights the importance of high-level quantum compilation for logical circuit translation, quantitative analysis of algorithms, and optimization of circuit lengths.
Abstract Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state preparation, and quantum dynamic simulation. Traditional quantum compilation usually optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets, such as thermal state preparation, time-dependent dynamic simulation, and others. To address this, we develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the effectiveness of the algorithm, highlighting the importance of multi-target optimization in advancing quantum computing. This work lays the groundwork for further development and evaluation of multi-target quantum compilation algorithms.
Shuttling-based trapped-ion quantum computers (QCCD) rank among the most mature hardware platforms. Existing and envisaged devices exhibit architectural differences in terms of register layout and connectivity. Platform-specific compilers are required to translate quantum circuits into resource-optimized hardware-level command chains. Hardware platforms and compiler software stacks are mostly developed independently of one another by different research communities. This work aims to bridge these communities by abstracting QCCD architectures and native operations, enabling computer scientists to address compilation problems using generic methods without requiring a deep background in the physical operating principles of the platform. The resulting design space is organized into a small set of architectural archetypes, each posing its own compilation problem. We then select and apply compilation methods to one- and two-dimensional QCCD architectures, finding that the routing overhead scales approximately cubically with the number of qubits for the one-dimensional architecture and approximately quadratically for the two-dimensional one. Extending this to compilation with a quantum error-correction layer reveals additional differences and similarities between the physical and logical levels. We conclude that co-design is a requirement rather than an option, and this work provides the vocabulary, archetypes, and compilation abstraction it needs.
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, technology-dependent quantum instructions. Many quantum algorithms, including Shor's and Grover's algorithms, require to compute 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 gates. During the optimization process, such functions are evaluated hundreds of times. For this reason, we extract them 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.