Quantum Computing Software

Quantum computers have the potential to produce tremendous improvements in computational performance for a wide range of problems in various fields, including optimisation, machine learning, chemistry, finance, and healthcare. As the performance of quantum hardware improves, so does the possibility of implementing quantum algorithms that outperform their classical counterparts. There is currently an ongoing race amongst various academic research groups, companies, and national research labs to produce the first demonstration of a practical quantum advantage – a milestone in which a quantum computer solves a problem of practical interest with a performance that classical computers cannot match.

Such a demonstration would involve:
1) using actual quantum hardware to run such a quantum algorithm,
2) showing that classical computers cannot perform as well.

We take a holistic hardware-agnostic full-stack approach that aims to harnesses the power of quantum computers to produce a practical quantum advantage. We aim to develop end-to-end full-stack solutions that bridge the gap between end-users problem statements and quantum-accelerated solutions optimally deployed on near-term quantum hardware by leveraging our existing expertise in HPC modelling and simulations and AI, as well as our vibrant developments in the field of quantum computing. 

Selected Publications

• Variational Quantum-Based Simulation of Waveguide Modes, IEEE Transactions of Microwave Theory and Techniques, DOI: 10.1109/TMTT.2022.3151510
  
  
• Variational quantum evolution equation solver, Scientific Reports, https://doi.org/10.1038/s41598-022-14906-3 
  
  
• Exploring variational quantum eigensolver ansatzes for the long-range XY model, https://arxiv.org/abs/2109.00288 
  
  
• Computing Electronic Correlation Energies using linear Depth Quantum Circuits, https://arxiv.org/abs/2207.03949 
  
  
• Shallow quantum circuits for efficient preparation of Slater determinants and correlated states on a quantum computer, https://arxiv.org/abs/2301.07477 
  
  
• Benchmark Quantum(-inspired) Annealing Hardware on Practical Use Cases, IEEE Transactions on Computers https://doi.org/10.1109/TC.2022.3219257
  
  
• When Quantum Annealing Meets Multitasking: Potentials, Challenges and Opportunities, Array https://doi.org/10.1016/j.array.2023.100282
  
  
• Effects of quantum resources and noise on the statistical complexity of quantum circuits. Quantum Science and Technology, 8, 4 2023
  
  
• Connecting geometry and performance of two-qubit parameterized quantum circuits. Quantum, 6, 2022
  
  
• Quantum entanglement recognition. Physical Review Research, 3, 9 2021
  
  
• Convex optimization for nonequilibrium steady states on a hybrid quantum processor. Physical Review Letters, 130:240601, 6 2023
  
  
• Quantum uncertainty principles for measurements with interventions. Physical Review Letters, 130:240201, 6 2023
  
  
• An Expressive Ansatz for Low-Depth Quantum Optimisation, https://arxiv.org/abs/2302.04479v1
  
  
• Coherent ground-state transport of neutral atoms, Physical Review A, 105, 032417, 2022
  
  
• Statistical complexity of quantum circuits, Physical Review A, 105, 062431, 2022
  
  
• Classical Shadows With Noise, Quantum 6, 776, 2022
  
  
• Wasserstein Complexity of Quantum Circuits, https://arxiv.org/abs/2208.06306v1
  
  
• Classical shadows with Pauli-invariant unitary ensembles, http://export.arxiv.org/abs/2202.03272