Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers

Recent developments in engineering and algorithms have made real-world applications in quantum computing possible in the near future. Existing quantum programming languages and compilers use a quantum assembly language composed of 1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this quantum assembly to electric signals (called control pulses) that implement the specified computation on specific physical devices. However, there is a mismatch between the operations defined by the 1- and 2-qubit logical ISA and their underlying physical implementation, so the current practice of directly translating logical instructions into control pulses results in inefficient, high-latency programs. To address this inefficiency, we propose a universal quantum compilation methodology that aggregates multiple logical operations into larger units that manipulate up to 10 qubits at a time. Our methodology then optimizes these aggregates by (1) finding commutative intermediate operations that result in more efficient schedules and (2) creating custom control pulses optimized for the aggregate (instead of individual 1- and 2-qubit operations). Compared to the standard gate-based compilation, the proposed approach realizes a deeper vertical integration of high-level quantum software and low-level, physical quantum hardware. We evaluate our approach on important near-term quantum applications on simulations of superconducting quantum architectures. Our proposed approach provides a mean speedup of $5\times$, with a maximum of $10\times$. Because latency directly affects the feasibility of quantum computation, our results not only improve performance but also have the potential to enable quantum computation sooner than otherwise possible.Comment: 13 pages, to apper in ASPLO

Compiler Optimization for Quantum Computing Using Reinforcement Learning

Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the development of compilers for quantum computing is still in its infancy-lacking mutual consolidation on the best sequence of passes, compatibility, adaptability, and flexibility. In this work, we take advantage of decades of classical compiler optimization and propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. Through distinct constraints and a unifying interface, the framework supports the combination of techniques from different compilers and optimization tools in a single compilation flow. Experimental evaluations show that the proposed framework-set up with a selection of compilation passes from IBM's Qiskit and Quantinuum's TKET-significantly outperforms both individual compilers in over 70% of cases regarding the expected fidelity. The framework is available on GitHub (https://github.com/cda-tum/MQTPredictor).Comment: 6 pages, 3 figure

QudCom: Towards Quantum Compilation for Qudit Systems

Qudit-based quantum computation offers unique advantages over qubit-based systems in terms of noise mitigation capabilities as well as algorithmic complexity improvements. However, the software ecosystem for multi-state quantum systems is severely limited. In this paper, we highlight a quantum workflow for describing and compiling qudit systems. We investigate the design and implementation of a quantum compiler for qudit systems. We also explore several key theoretical properties of qudit computing as well as efficient optimization techniques. Finally, we provide demonstrations using physical quantum computers as well as simulations of the proposed quantum toolchain

Reducing the CNOT count for Clifford+T circuits on NISQ architectures

While mapping a quantum circuit to the physical layer one has to consider the numerous constraints imposed by the underlying hardware architecture. Connectivity of the physical qubits is one such constraint that restricts two-qubit operations such as CNOT to "connected" qubits. SWAP gates can be used to place the logical qubits on admissible physical qubits, but they entail a significant increase in CNOT-count, considering the fact that each SWAP gate can be implemented by 3 CNOT gates. In this paper we consider the problem of reducing the CNOT-count in Clifford+T circuits on connectivity constrained architectures such as noisy intermediate-scale quantum (NISQ) (Preskill, 2018) computing devices. We "slice" the circuit at the position of Hadamard gates and "build" the intermediate portions. We investigated two kinds of partitioning - (i) a simple method of partitioning the gates of the input circuit based on the locality of H gates and (ii) a second method of partitioning the phase polynomial of the input circuit. The intermediate {CNOT,T} sub-circuits are synthesized using Steiner trees, significantly improving on the methods introduced by Nash, Gheorghiu, Mosca[2020] and Kissinger, de Griend[2019]. We compared the performance of our algorithms while mapping different benchmark circuits as well as random circuits to some popular architectures such as 9-qubit square grid, 16-qubit square grid, Rigetti 16-qubit Aspen, 16-qubit IBM QX5 and 20-qubit IBM Tokyo. We found that for both the benchmark and random circuits our first algorithm that uses the simple slicing technique dramatically reduces the CNOT-count compared to naively using SWAP gates. Our second slice-and-build algorithm also performs very well for benchmark circuits.Comment: 41 pages, 2 figures, 2 tables. Added appendix with example

Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow

Realizing a conceptual quantum algorithm on an actual physical device necessitates the algorithm's quantum circuit description to undergo certain transformations in order to adhere to all constraints imposed by the hardware. In this regard, the individual high-level circuit components are first synthesized to the supported low-level gate-set of the quantum computer, before being mapped to the target's architecture---utilizing several optimizations in order to improve the compilation result. Specialized tools for this complex task exist, e.g., IBM's Qiskit, Google's Cirq, Microsoft's QDK, or Rigetti's Forest. However, to date, the circuits resulting from these tools are hardly verified, which is mainly due to the immense complexity of checking if two quantum circuits indeed realize the same functionality. In this paper, we propose an efficient scheme for quantum circuit equivalence checking---specialized for verifying results of the IBM Qiskit quantum circuit compilation flow. To this end, we combine characteristics unique to quantum computing, e.g., its inherent reversibility, and certain knowledge about the compilation flow into a dedicated equivalence checking strategy. Experimental evaluations confirm that the proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less, whereas state-of-the-art techniques frequently time-out or require substantially more runtime. A corresponding open source implementation of the proposed method is publicly available at https://github.com/iic-jku/qcec.Comment: 10 pages, to be published at International Conference on Quantum Computing and Engineering (QCE20

QContext: Context-Aware Decomposition for Quantum Gates

In this paper we propose QContext, a new compiler structure that incorporates context-aware and topology-aware decompositions. Because of circuit equivalence rules and resynthesis, variants of a gate-decomposition template may exist. QContext exploits the circuit information and the hardware topology to select the gate variant that increases circuit optimization opportunities. We study the basis-gate-level context-aware decomposition for Toffoli gates and the native-gate-level context-aware decomposition for CNOT gates. Our experiments show that QContext reduces the number of gates as compared with the state-of-the-art approach, Orchestrated Trios.Comment: 10 page

Phase gadget synthesis for shallow circuits

We give an overview of the circuit optimisation methods used by tket, a compiler system for quantum software developed by Cambridge Quantum Computing Ltd. We focus on a novel technique based around phase gadgets, a family of multi-qubit quantum operations which occur naturally in a wide range of quantum circuits of practical interest. The phase gadgets have a simple presentation in the ZX-calculus, which makes it easy to reason about them. Taking advantage of this, we present an efficient method to translate the phase gadgets back to CNOT gates and single qubit operations suitable for execution on a quantum computer with significant reductions in gate count and circuit depth. We demonstrate the effectiveness of these methods on a quantum chemistry benchmarking set based on variational circuits for ground state estimation of small molecules