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.
Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Independent Researcher Cambridge, USA（独立研究者 美国剑桥）；Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu, Taiwan（电子物理系 国立阳明交通大学 台北县）
Numerical Evaluation of ZX Calculus Optimization for Solovay Kitaev Quantum Circuit Synthesis
University of Colombo（科伦坡大学）；The University of Western Australia（西澳大学）；University Paris City and University of Reunion（巴黎城市大学与留尼汪大学）；PEACCEL, AI for Biologics（PEACCEL生物制剂人工智能）
Worst-case depth hierarchy for shallow quantum circuits
Hon Hai (Foxconn) Quantum Computing Research Center（鸿海（富士康）量子计算研究中心）；University of Oxford（牛津大学）；The University of Hong Kong（香港大学）；University of Technology Sydney（悉尼科技大学）
