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Qadence: a differentiable interface for digital-analog programs (2401.09915v1)

Published 18 Jan 2024 in quant-ph, cs.LG, cs.PL, and cs.SE

Abstract: Digital-analog quantum computing (DAQC) is an alternative paradigm for universal quantum computation combining digital single-qubit gates with global analog operations acting on a register of interacting qubits. Currently, no available open-source software is tailored to express, differentiate, and execute programs within the DAQC paradigm. In this work, we address this shortfall by presenting Qadence, a high-level programming interface for building complex digital-analog quantum programs developed at Pasqal. Thanks to its flexible interface, native differentiability, and focus on real-device execution, Qadence aims at advancing research on variational quantum algorithms built for native DAQC platforms such as Rydberg atom arrays.

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References (39)
  1. Qiskit contributors. Qiskit: An open-source framework for quantum computing, 2023.
  2. Cirq Developers. Cirq, July 2023.
  3. Pennylane: Automatic differentiation of hybrid quantum-classical computations, 2022.
  4. Digital-analog quantum computation. Physical Review A, 101(2), feb 2020.
  5. Universal quantum computation and simulation using any entangling hamiltonian and local unitaries. Physical Review A, 65(4), apr 2002.
  6. Digital-analog quantum algorithm for the quantum fourier transform. Phys. Rev. Res., 2:013012, Jan 2020.
  7. A quantum approximate optimization algorithm, 2014.
  8. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature, 549(7671):242–246, sep 2017.
  9. Quantum computing with neutral atoms. Quantum, 4:327, September 2020.
  10. Quantum simulation of 2d antiferromagnets with hundreds of rydberg atoms. Nature, 595(7866):233–238, 2021.
  11. Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays. Quantum, 6:629, jan 2022.
  12. Bloqade.jl: Package for the quantum computation and quantum simulation based on the neutral-atom architecture., 2023.
  13. Simuq: A framework for programming quantum hamiltonian simulation with analog compilation. Proceedings of the ACM on Programming Languages, 8(POPL):2425–2455, January 2024.
  14. Qadence contributors. Qadence: A Digital-analog quantum programming interface., 2023.
  15. Pytorch: An imperative style, high-performance deep learning library, 2019.
  16. Generalized quantum circuit differentiation rules. Physical Review A, 104(5), nov 2021.
  17. Yao.jl: Extensible, efficient framework for quantum algorithm design. Quantum, 4:341, oct 2020.
  18. Quipper: a scalable quantum programming language. ACM SIGPLAN Notices, 48(6):333–342, June 2013.
  19. Sympy: symbolic computing in python. PeerJ Computer Science, 3:e103, January 2017.
  20. Exploring network structure, dynamics, and function using networkx. In Gaël Varoquaux, Travis Vaught, and Jarrod Millman, editors, Proceedings of the 7th Python in Science Conference, pages 11 – 15, Pasadena, CA USA, 2008.
  21. Pyqtorch: A pytorch-based quantum state vector simulator., 2023.
  22. Emu-c. In preparation, 2024.
  23. Horqrux: A jax-based quantum state vector simulator., 2023.
  24. Jax contributors. Jax.
  25. Cloud on-demand emulation of quantum dynamics with tensor networks, 2023.
  26. Román Orús. A practical introduction to tensor networks: Matrix product states and projected entangled pair states. Annals of Physics, 349:117–158, October 2014.
  27. Hand-waving and interpretive dance: an introductory course on tensor networks. Journal of Physics A: Mathematical and Theoretical, 50(22):223001, May 2017.
  28. Tensorly-quantum: Quantum machine learning with tensor methods, 2021.
  29. Hyper-optimized tensor network contraction. Quantum, 5:410, March 2021.
  30. Efficient calculation of gradients in classical simulations of variational quantum algorithms, 2020.
  31. Automatic differentiation in machine learning: A survey. J. Mach. Learn. Res., 18(1):5595–5637, jan 2017.
  32. Evaluating derivatives : principles and techniques of algorithmic differentiation. Society for Industrial and Applied Mathematics, 2008.
  33. Blueprint for a digital-analog variational quantum eigensolver using rydberg atom arrays. Phys. Rev. A, 107:042602, Apr 2023.
  34. Solving nonlinear differential equations with differentiable quantum circuits. Physical Review A, 103(5), may 2021.
  35. Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 12 2014.
  36. Wikipedia. QUBO, 2024.
  37. Quantum optimization with arbitrary connectivity using rydberg atom arrays. PRX Quantum, 4:010316, Feb 2023.
  38. Quantum computation by adiabatic evolution, 2000.
  39. Digital-analog quantum computation with arbitrary two-body hamiltonians. arXiv preprint arXiv:2307.00966, 2023.
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