- Open Access
Circuit connectivity boosts by quantum-classical-quantum interfaces
Phys. Rev. Research 4, 043221 – Published 29 December, 2022
DOI: https://doi.org/10.1103/PhysRevResearch.4.043221
Abstract
High-connectivity circuits are a major roadblock for current quantum hardware. We propose a hybrid classical-quantum algorithm to simulate such circuits without swap-gate ladders. As the main technical tool, we introduce quantum-classical-quantum interfaces. These replace an experimentally problematic gate (e.g., a long-range one) with single-qubit random measurements followed by state preparations sampled according to a classical quasiprobability simulation of the noiseless gate. Each interface introduces a multiplicative statistical overhead which, remarkably, is independent of the on-chip qubit distance. Hence, by applying interfaces to the longest-range gates in a target circuit, significant reductions in circuit depth and gate infidelity can be attained. We numerically show the efficacy of our method for a Bell-state circuit for two increasingly distant qubits and a variational ground-state solver for the transverse-field Ising model on a ring. Our findings provide a versatile toolbox for error-mitigation and circuit boosts tailored for noisy, intermediate-scale quantum computation.
Physics Subject Headings (PhySH)
Article Text
References (56)
- A. Montanaro, Quantum algorithms: an overview, npj Quantum Inf. 2, 15023 (2016).
- J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature (London) 549, 195 (2017).
- A. Bouland, W. van Dam, H. Joorati, I. Kerenidis, and A. Prakash, Prospects and challenges of quantum finance, arXiv:2011.06492.
- J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
- M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Sci. Technol. 4, 043001 (2019).
- K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, W.-K. Mok, S. Sim, L.-C. Kwek, and A. Aspuru-Guzik, Noisy intermediate-scale quantum (NISQ) algorithms, Rev. Mod. Phys. 94, 015004 (2022).
- M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles, Variational quantum algorithms, Nat. Rev. Phys. 3, 625 (2021).
- E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028.
- A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O'Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5, 4213 (2014).
- K. Temme, S. Bravyi, and J. M. Gambetta, Error Mitigation for Short-Depth Quantum Circuits, Phys. Rev. Lett. 119, 180509 (2017).
- S. Endo, S. C. Benjamin, and Y. Li, Practical Quantum Error Mitigation for Near-Future Applications, Phys. Rev. X 8, 031027 (2018).
- Y. Li and S. C. Benjamin, Efficient Variational Quantum Simulator Incorporating Active Error Minimization, Phys. Rev. X 7, 021050 (2017).
- A. Kandala, K. Temme, A. D. Córcoles, A. Mezzacapo, J. M. Chow, and J. M. Gambetta, Error mitigation extends the computational reach of a noisy quantum processor, Nature (London) 567, 491 (2019).
- S. Bravyi, G. Smith, and J. A. Smolin, Trading Classical and Quantum Computational Resources, Phys. Rev. X 6, 021043 (2016).
- V. Dunjko, Y. Ge, and J. I. Cirac, Computational Speedups Using Small Quantum Devices, Phys. Rev. Lett. 121, 250501 (2018).
- T. Peng, A. W. Harrow, M. Ozols, and X. Wu, Simulating Large Quantum Circuits on a Small Quantum Computer, Phys. Rev. Lett. 125, 150504 (2020).
- W. Tang, T. Tomesh, M. Suchara, J. Larson, and M. Martonosi, CutQC: Using small quantum computers for large quantum circuit evaluations, in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2021) (Association for Computing Machinery, New York, 2021), pp. 473–486.
- M. A. Perlin, Z. H. Saleem, M. Suchara, and J. C. Osborn, Quantum circuit cutting with maximum-likelihood tomography, npj Quantum Inf. 7, 64 (2021).
- K. Mitarai and K. Fujii, Constructing a virtual two-qubit gate by sampling single-qubit operations, New J. Phys. 23, 023021 (2021).
- J. Carrasquilla, G. Torlai, R. G. Melko, and L. Aolita, Reconstructing quantum states with generative models, Nat. Mach. Intell. 1, 155 (2019).
- H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nat. Phys. 16, 1050 (2020).
- C. Ferrie and J. Emerson, Frame representations of quantum mechanics and the necessity of negativity in quasi-probability representations, J. Phys. A: Math. Theor. 41, 352001 (2008).
- C. Ferrie, Quasi-probability representations of quantum theory with applications to quantum information science, Rep. Prog. Phys. 74, 116001 (2011).
- A. Mari and J. Eisert, Positive Wigner Functions Render Classical Simulation of Quantum Computation Efficient, Phys. Rev. Lett. 109, 230503 (2012).
- V. Veitch, N. Wiebe, C. Ferrie, and J. Emerson, Efficient simulation scheme for a class of quantum optics experiments with non-negative Wigner representation, New J. Phys. 15, 013037 (2013).
- H. Pashayan, J. J. Wallman, and S. D. Bartlett, Estimating Outcome Probabilities of Quantum Circuits Using Quasiprobabilities, Phys. Rev. Lett. 115, 070501 (2015).
- N. Hatano and M. Suzuki, Representation basis in quantum Monte Carlo calculations and the negative-sign problem, Phys. Lett. A 163, 246 (1992).
- E. Y. Loh, J. E. Gubernatis, R. T. Scalettar, S. R. White, D. J. Scalapino, and R. L. Sugar, Sign problem in the numerical simulation of many-electron systems, Phys. Rev. B 41, 9301 (1990).
- M. Troyer and U.-J. Wiese, Computational Complexity and Fundamental Limitations to Fermionic Quantum Monte Carlo Simulations, Phys. Rev. Lett. 94, 170201 (2005).
- C. Radhakrishna Rao, Calculus of Generalized Inverses of Matrices Part I: General Theory, Sankhya: The Indian Journal of Statistics, Series A (1961-2002) (Indian Statistical Institute, Kolkata, India, 1967), Vol. 29, pp. 317–342.
- M. Marvian, D. A. Lidar, and I. Hen, On the computational complexity of curing non-stoquastic Hamiltonians, Nat. Commun. 10, 1571 (2019).
- D. Hangleiter, I. Roth, D. Nagaj, and J. Eisert, Easing the Monte Carlo sign problem, Sci. Adv. 6, eabb8341 (2020).
- M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, 10th ed. (Cambridge University Press, Cambridge, 2011).
- A. Peres, Quantum Theory: Concepts and Methods, Fundamental Theories of Physics Vol. 72 (Kluwer Academic, Dordrecht, 1993).
- S. Aaronson, Shadow tomography of quantum states, in Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing (STOC 2018) (Association for Computing Machinery, New York, 2018), pp. 325–338.
- R. Penrose, A generalized inverse for matrices, Math. Proc. Cambridge Philos. Soc. 51, 406 (1955).
- G. Torlai, C. J. Wood, A. Acharya, G. Carleo, J. Carrasquilla, and L. Aolita, Quantum process tomography with unsupervised learning and tensor networks, arXiv:2006.02424.
- J. F. C. Leonardo Guerini, R. Wiersema and L. Aolita, Quasiprobabilistic state-overlap estimator for NISQ devices, arXiv:2112.11618.
- J. Carrasquilla, D. Luo, F. Pérez, A. Milsted, B. K. Clark, M. Volkovs, and L. Aolita, Probabilistic simulation of quantum circuits using a deep-learning architecture, Phys. Rev. A 104, 032610 (2021).
- C. Piveteau and D. Sutter, Circuit knitting with classical communication, arXiv:2205.00016.
- A. H. Werner, D. Jaschke, P. Silvi, M. Kliesch, T. Calarco, J. Eisert, and S. Montangero, Positive Tensor Network Approach for Simulating Open Quantum Many-Body Systems, Phys. Rev. Lett. 116, 237201 (2016).
- F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. S. L. Brandao, D. A. Buell, B. Burkett, Y. Chen, Z. Chen, B. Chiaro, R. Collins, W. Courtney, A. Dunsworth, E. Farhi, B. Foxen, A. Fowler et al., Quantum supremacy using a programmable superconducting processor, Nature (London) 574, 505 (2019).
- W. W. Ho and T. H. Hsieh, Efficient variational simulation of non-trivial quantum states, SciPost Phys. 6, 029 (2019).
- D. Wierichs, C. Gogolin, and M. Kastoryano, Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer, Phys. Rev. Res. 2, 043246 (2020).
- R. Wiersema, C. Zhou, Y. de Sereville, J. F. Carrasquilla, Y. B. Kim, and H. Yuen, Exploring Entanglement and Optimization within the Hamiltonian Variational Ansatz, PRX Quantum 1, 020319 (2020).
- N. J. Cerf and S. E. Koonin, Monte Carlo simulation of quantum computation, Math. Comput. Simul. 47, 143 (1998).
- Honeywell Quantum, Mid-circuit measurements on the System Model H1, 2021, https://www.honeywell.com/us/en/company/quantum/quantum-computer/.
- IBM Quantum, Mid-circuit Measurements Tutorial, 2021, https://quantum-computing.ibm.com/lab/docs/iql/manage/systems/midcircuit-measurement/.
- https://vectorinstitute.ai/#partners.
- C. M. Caves, Quantum error correction and reversible operations, J. Supercond. 12, 707 (1999).
- J. F. Clauser, M. A. Horne, A. Shimony, and R. A. Holt, Proposed Experiment to Test Local Hidden-Variable Theories, Phys. Rev. Lett. 23, 880 (1969).
- F. Verstraete, J. J. García-Ripoll, and J. I. Cirac, Matrix Product Density Operators: Simulation of Finite-Temperature and Dissipative Systems, Phys. Rev. Lett. 93, 207204 (2004).
- E. Moore, On the reciprocal of the general algebraic matrix, Bull. Am. Math. Soc. 26, 394 (1920).
- N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21, 1087 (1953).
- C. Sherlock, P. Fearnhead, and G. O. Roberts, The random walk Metropolis: Linking theory and practice through a case study, Stat. Sci. 25, 172 (2010).
- A. Gelman, W. R. Gilks, and G. O. Roberts, Weak convergence and optimal scaling of random walk Metropolis algorithms, Ann. Appl. Probab. 7, 110 (1997).