A hybrid quantum-classical approach to mitigating measurement errors

When noisy intermediate scalable quantum (NISQ) devices are applied in information processing, all of the stages through preparation, manipulation, and measurement of multipartite qubit states contain various types of noise that are generally hard to be verified in practice. In this work, we present a scheme to deal with unknown quantum noise and show that it can be used to mitigate errors in measurement readout with NISQ devices. Quantum detector tomography that identifies a type of noise in a measurement can be circumvented. The scheme applies single-qubit operations only, that are with relatively higher precision than measurement readout or two-qubit gates. A classical post-processing is then performed with measurement outcomes. The scheme is implemented in quantum algorithms with NISQ devices: the Bernstein-Vazirani algorithm and a quantum amplitude estimation algorithm in IBMQ yorktown and IBMQ essex. The enhancement in the statistics of the measurement outcomes is presented for both of the algorithms with NISQ devices.

[1]  Shor,et al.  Scheme for reducing decoherence in quantum computer memory. , 1995, Physical review. A, Atomic, molecular, and optical physics.

[2]  Naoki Yamamoto,et al.  Amplitude estimation without phase estimation , 2019, Quantum Information Processing.

[3]  Joonwoo Bae,et al.  Quantum state discrimination and its applications , 2015, 1707.02571.

[4]  Robert König,et al.  Quantum advantage with shallow circuits , 2017, Science.

[5]  Michal Oszmaniec,et al.  Mitigation of readout noise in near-term quantum devices by classical post-processing based on detector tomography , 2019, Quantum.

[6]  Umesh V. Vazirani,et al.  Quantum complexity theory , 1993, STOC.

[7]  P. Alam ‘U’ , 2021, Composites Engineering: An A–Z Guide.

[8]  Andris Ambainis,et al.  Understanding Quantum Algorithms via Query Complexity , 2017, Proceedings of the International Congress of Mathematicians (ICM 2018).

[9]  H. Yuen Quantum detection and estimation theory , 1978, Proceedings of the IEEE.

[10]  Daniel A. Lidar,et al.  Demonstration of Fidelity Improvement Using Dynamical Decoupling with Superconducting Qubits. , 2018, Physical review letters.

[11]  Michael A. Nielsen,et al.  The Solovay-Kitaev algorithm , 2006, Quantum Inf. Comput..

[12]  Anthony D. Castellano,et al.  Genuine 12-Qubit Entanglement on a Superconducting Quantum Processor. , 2018, Physical review letters.

[13]  D. Gross,et al.  Evenly distributed unitaries: On the structure of unitary designs , 2006, quant-ph/0611002.

[14]  D. Deutsch,et al.  Rapid solution of problems by quantum computation , 1992, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

[15]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[16]  Chahan M. Kropf,et al.  Preserving measurements for optimal state discrimination over quantum channels , 2018, Physical Review A.

[17]  A. Kitaev Quantum computations: algorithms and error correction , 1997 .

[18]  Moinuddin K. Qureshi,et al.  Mitigating Measurement Errors in Quantum Computers by Exploiting State-Dependent Bias , 2019, MICRO.

[19]  Scott Aaronson,et al.  Quantum Approximate Counting, Simplified , 2019, SOSA.

[20]  Kristan Temme,et al.  Error Mitigation for Short-Depth Quantum Circuits. , 2016, Physical review letters.

[21]  Kristan Temme,et al.  Error mitigation extends the computational reach of a noisy quantum processor , 2019, Nature.

[22]  J. Cirac,et al.  Distillability and partial transposition in bipartite systems , 1999, quant-ph/9910022.

[23]  Anthony Chefles Quantum state discrimination , 2000 .

[24]  Ying Li,et al.  Quantum computation with universal error mitigation on a superconducting quantum processor , 2018, Science Advances.

[25]  V. Paulsen Completely Bounded Maps and Operator Algebras: Contents , 2003 .

[26]  P. Alam ‘A’ , 2021, Composites Engineering: An A–Z Guide.

[27]  Lov K. Grover Quantum Mechanics Helps in Searching for a Needle in a Haystack , 1997, quant-ph/9706033.

[28]  W. Marsden I and J , 2012 .

[29]  S. Benjamin,et al.  Practical Quantum Error Mitigation for Near-Future Applications , 2017, Physical Review X.

[30]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[31]  Andrew W. Cross,et al.  Quantum learning robust against noise , 2014, 1407.5088.

[32]  Yonina C. Eldar,et al.  Designing optimal quantum detectors via semidefinite programming , 2003, IEEE Trans. Inf. Theory.

[33]  H. S. Allen The Quantum Theory , 1928, Nature.

[34]  S. Barnett,et al.  Quantum state discrimination , 2008, 0810.1970.

[35]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm , 2014, 1411.4028.

[36]  D. Deutsch Quantum theory, the Church–Turing principle and the universal quantum computer , 1985, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[37]  János A. Bergou,et al.  Quantum state discrimination and selected applications , 2007 .

[38]  Robert S. Kennedy,et al.  Optimum testing of multiple hypotheses in quantum detection theory , 1975, IEEE Trans. Inf. Theory.

[39]  Spiros Kechrimparis,et al.  Channel Coding of a Quantum Measurement , 2019, IEEE Journal on Selected Areas in Communications.

[40]  Travis S. Humble,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[41]  G. Brassard,et al.  Quantum Amplitude Amplification and Estimation , 2000, quant-ph/0005055.

[42]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[43]  Stefan Woerner,et al.  Quantum risk analysis , 2018, npj Quantum Information.

[44]  János A. Bergou,et al.  Discrimination of quantum states , 2004 .

[45]  S. Debnath,et al.  Demonstration of a small programmable quantum computer with atomic qubits , 2016, Nature.

[46]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.