- Open Access
Coherence as a Resource for Phase Estimation
Phys. Rev. Lett. 136, 180201 – Published 4 May, 2026
DOI: https://doi.org/10.1103/s6d5-lc6g
Abstract
Quantum phase estimation is a core task in quantum technologies ranging from metrology to quantum computing, where it appears as a key subroutine in various algorithms. Here, we quantitatively connect the performance of phase estimation protocols with quantum coherence. To achieve this, we construct and characterize resource theories of quantum networks that cannot generate coherence. Given multiple copies of a unitary encoding an unknown phase and access to a fixed coherent state, we estimate the phase using such networks. For a unified and general approach, we assess the quality of the estimate using a generic cost function that penalizes deviations from the true value. We determine the minimal average cost that can be achieved in this manner and explicitly derive optimal protocols. From this, we construct a family of coherence measures that directly connect a state’s coherence with its value for phase estimation, demonstrating that every bit of coherence helps. This establishes coherence as a resource that quantifies the performance of phase estimation, and, thus, of any quantum technology relying on it as a subroutine.
Physics Subject Headings (PhySH)
Article Text
Supplemental Material
References (91)
- P. W. Shor, Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM J. Comput. 26, 1484 (1997).
- A. Y. Kitaev, Quantum measurements and the Abelian stabilizer problem, arXiv:quant-ph/9511026.
- A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Phys. Rev. Lett. 103, 150502 (2009).
- R. Cleve, A. Ekert, C. Macchiavello, and M. Mosca, Quantum algorithms revisited, Proc. R. Soc. A 454, 339 (1998).
- A. Y. Kitaev, Quantum computations: Algorithms and error correction, Russ. Math. Surv. 52, 1191 (1997).
- W. van Dam, G. M. D’Ariano, A. Ekert, C. Macchiavello, and M. Mosca, Optimal quantum circuits for general phase estimation, Phys. Rev. Lett. 98, 090501 (2007).
- M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, England, 2010).
- V. Giovannetti, S. Lloyd, and L. Maccone, Quantum metrology, Phys. Rev. Lett. 96, 010401 (2006).
- A. Aspuru-Guzik, A. D. Dutoi, P. J. Love, and M. Head-Gordon, Simulated quantum computation of molecular energies, Science 309, 1704 (2005).
- D. Munoz-Lahoz, J. Calsamiglia, J. A. Bergou, and E. Bagan, Phase estimation with limited coherence, arXiv:2207.05656.
- R. Lecamwasam, S. Assad, J. J. Hope, P. K. Lam, J. Thompson, and M. Gu, Relative entropy of coherence quantifies performance in Bayesian metrology, PRX Quantum 5, 030303 (2024).
- E. Chitambar and G. Gour, Quantum resource theories, Rev. Mod. Phys. 91, 025001 (2019).
- T. Baumgratz, M. Cramer, and M. B. Plenio, Quantifying coherence, Phys. Rev. Lett. 113, 140401 (2014).
- A. Streltsov, G. Adesso, and M. B. Plenio, Colloquium: Quantum coherence as a resource, Rev. Mod. Phys. 89, 041003 (2017).
- D. Bruß and C. Macchiavello, Multipartite entanglement in quantum algorithms, Phys. Rev. A 83, 052313 (2011).
- H. Pashayan, J. J. Wallman, and S. D. Bartlett, Estimating outcome probabilities of quantum circuits using quasiprobabilities, Phys. Rev. Lett. 115, 070501 (2015).
- S. Bravyi, G. Smith, and J. A. Smolin, Trading classical and quantum computational resources, Phys. Rev. X 6, 021043 (2016).
- H.-L. Shi, S.-Y. Liu, X.-H. Wang, W.-L. Yang, Z.-Y. Yang, and H. Fan, Coherence depletion in the grover quantum search algorithm, Phys. Rev. A 95, 032307 (2017).
- F. Ahnefeld, T. Theurer, D. Egloff, J. M. Matera, and M. B. Plenio, Coherence as a resource for Shor’s algorithm, Phys. Rev. Lett. 129, 120501 (2022).
- M. Naseri, T. V. Kondra, S. Goswami, M. Fellous-Asiani, and A. Streltsov, Entanglement and coherence in the Bernstein-Vazirani algorithm, Phys. Rev. A 106, 062429 (2022).
- N. Anand, I. H. Kim, and J. Preskill, Entanglement accelerates quantum simulation, Nat. Phys. 21, 1338 (2025).
- R. Jozsa and N. Linden, On the role of entanglement in quantum-computational speed-up, Proc. R. Soc. A 459, 2011 (2003).
- G. Vidal, Efficient classical simulation of slightly entangled quantum computations, Phys. Rev. Lett. 91, 147902 (2003).
- M. Howard, J. Wallman, V. Veitch, and J. Emerson, Contextuality supplies the ‘magic’ for quantum computation, Nature (London) 510, 351 (2014).
- J. Aberg, Quantifying superposition, arXiv:quant-ph/0612146.
- A. Winter and D. Yang, Operational resource theory of coherence, Phys. Rev. Lett. 116, 120404 (2016).
- B. Yadin, J. Ma, D. Girolami, M. Gu, and V. Vedral, Quantum processes which do not use coherence, Phys. Rev. X 6, 041028 (2016).
- E. Chitambar and G. Gour, Critical examination of incoherent operations and a physically consistent resource theory of quantum coherence, Phys. Rev. Lett. 117, 030401 (2016).
- E. Chitambar and G. Gour, Comparison of incoherent operations and measures of coherence, Phys. Rev. A 94, 052336 (2016).
- E. Chitambar and G. Gour, Erratum: Comparison of incoherent operations and measures of coherence [phys. rev. a 94, 052336 (2016)], Phys. Rev. A 95, 019902 (2017).
- I. Marvian and R. W. Spekkens, How to quantify coherence: Distinguishing speakable and unspeakable notions, Phys. Rev. A 94, 052324 (2016).
- G. Gour, Resources of the quantum world, arXiv:2402.05474.
- Z.-W. Liu, X. Hu, and S. Lloyd, Resource destroying maps, Phys. Rev. Lett. 118, 060502 (2017).
- M. García Díaz, K. Fang, X. Wang, M. Rosati, M. Skotiniotis, J. Calsamiglia, and A. Winter, Using and reusing coherence to realize quantum processes, Quantum 2, 100 (2018).
- T. Theurer, D. Egloff, L. Zhang, and M. B. Plenio, Quantifying operations with an application to coherence, Phys. Rev. Lett. 122, 190405 (2019).
- G. Chiribella, G. M. D’Ariano, and P. Perinotti, Quantum circuit architecture, Phys. Rev. Lett. 101, 060401 (2008).
- G. Chiribella, G. M. D’Ariano, and P. Perinotti, Theoretical framework for quantum networks, Phys. Rev. A 80, 022339 (2009).
- A. Jamiołkowski, Linear transformations which preserve trace and positive semidefiniteness of operators, Rep. Math. Phys. 3, 275 (1972).
- M.-D. Choi, Completely positive linear maps on complex matrices, Linear Algebra Appl. 10, 285 (1975).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/s6d5-lc6g, which includes an introduction to quantum supermaps, a characterization of incoherent supermaps, the proofs of the results in the main text, and Refs. [41,42].
- G. Chiribella, G. M. D’Ariano, and P. Perinotti, Transforming quantum operations: Quantum supermaps, Europhys. Lett. 83, 30004 (2008).
- S. Serra, On the extreme spectral properties of Toeplitz matrices generated by functions with several minima/maxima, BIT 36, 135 (1996).
- F. A. Pollock, C. Rodríguez-Rosario, T. Frauenheim, M. Paternostro, and K. Modi, Non-Markovian quantum processes: Complete framework and efficient characterization, Phys. Rev. A 97, 012127 (2018).
- Z.-W. Liu and A. Winter, Resource theories of quantum channels and the universal role of resource erasure, arXiv:1904.04201.
- G. Gour and C. M. Scandolo, Dynamical resources, arXiv:2101.01552.
- G. Gour and C. M. Scandolo, Entanglement of a bipartite channel, Phys. Rev. A 103, 062422 (2021).
- G. D. Berk, A. J. P. Garner, B. Yadin, K. Modi, and F. A. Pollock, Resource theories of multi-time processes: A window into quantum non-Markovianity, Quantum 5, 435 (2021).
- G. D. Berk, S. Milz, F. A. Pollock, and K. Modi, Extracting quantum dynamical resources: Consumption of non-Markovianity for noise reduction, npj Quantum Inf. 9, 104 (2023).
- P. Taranto, S. Milz, M. Murao, M. T. Quintino, and K. Modi, Higher-order quantum operations, arXiv:2503.09693.
- L. Vandenberghe and S. Boyd, Semidefinite programming, SIAM Rev. 38, 49 (1996).
- S. Boyd and L. Vandenberghe, Convex Optimization (Cambridge University Press, Cambridge, England, 2004).
- C. H. Bennett, H. J. Bernstein, S. Popescu, and B. Schumacher, Concentrating partial entanglement by local operations, Phys. Rev. A 53, 2046 (1996).
- M. B. Plenio and S. Virmani, An introduction to entanglement measures, Quantum Inf. Comput. 7, 1 (2007).
- R. Horodecki, P. Horodecki, M. Horodecki, and K. Horodecki, Quantum entanglement, Rev. Mod. Phys. 81, 865 (2009).
- A. Holevo, Probabilistic and Statistical Aspects of Quantum Theory (Edizioni della Normale Pisa, 2011).
- W. van Dam, G. M. D’Ariano, A. Ekert, C. Macchiavello, and M. Mosca, Optimal phase estimation in quantum networks, J. Phys. A 40, 7971 (2007).
- C. Napoli, T. R. Bromley, M. Cianciaruso, M. Piani, N. Johnston, and G. Adesso, Robustness of coherence: An operational and observable measure of quantum coherence, Phys. Rev. Lett. 116, 150502 (2016).
- M. Piani, M. Cianciaruso, T. R. Bromley, C. Napoli, N. Johnston, and G. Adesso, Robustness of asymmetry and coherence of quantum states, Phys. Rev. A 93, 042107 (2016).
- R. Takagi, B. Regula, K. Bu, Z.-W. Liu, and G. Adesso, Operational advantage of quantum resources in subchannel discrimination, Phys. Rev. Lett. 122, 140402 (2019).
- R. Takagi and B. Regula, General resource theories in quantum mechanics and beyond: Operational characterization via discrimination tasks, Phys. Rev. X 9, 031053 (2019).
- P. Skrzypczyk and N. Linden, Robustness of measurement, discrimination games, and accessible information, Phys. Rev. Lett. 122, 140403 (2019).
- P. Skrzypczyk, I. Šupić, and D. Cavalcanti, All sets of incompatible measurements give an advantage in quantum state discrimination, Phys. Rev. Lett. 122, 130403 (2019).
- R. Uola, T. Kraft, J. Shang, X.-D. Yu, and O. Gühne, Quantifying quantum resources with conic programming, Phys. Rev. Lett. 122, 130404 (2019).
- J. Mori, Operational characterization of incompatibility of quantum channels with quantum state discrimination, Phys. Rev. A 101, 032331 (2020).
- A. F. Ducuara and P. Skrzypczyk, Operational interpretation of weight-based resource quantifiers in convex quantum resource theories, Phys. Rev. Lett. 125, 110401 (2020).
- A. F. Ducuara, P. Lipka-Bartosik, and P. Skrzypczyk, Multiobject operational tasks for convex quantum resource theories of state-measurement pairs, Phys. Rev. Res. 2, 033374 (2020).
- R. Uola, T. Bullock, T. Kraft, J.-P. Pellonpää, and N. Brunner, All quantum resources provide an advantage in exclusion tasks, Phys. Rev. Lett. 125, 110402 (2020).
- M. Masini, T. Theurer, and M. B. Plenio, Coherence of operations and interferometry, Phys. Rev. A 103, 042426 (2021).
- R. Wagner, A. Camillini, and E. F. Galvão, Coherence and contextuality in a Mach-Zehnder interferometer, Quantum 8, 1240 (2024).
- M. Hillery, Coherence as a resource in decision problems: The Deutsch-Jozsa algorithm and a variation, Phys. Rev. A 93, 012111 (2016).
- J. M. Matera, D. Egloff, N. Killoran, and M. B. Plenio, Coherent control of quantum systems as a resource theory, Quantum Sci. Technol. 1, 01LT01 (2016).
- T. Biswas, M. García Díaz, and A. Winter, Interferometric visibility and coherence, Proc. R. Soc. A 473, 20170170 (2017).
- S.-Q. Zhou, H. Jin, J.-M. Liang, S.-M. Fei, Y. Xiao, and Z. Ma, Coherence fraction in Grover’s search algorithm, Phys. Rev. A 110, 062429 (2024).
- K. Bu, U. Singh, S.-M. Fei, A. K. Pati, and J. Wu, Maximum relative entropy of coherence: An operational coherence measure, Phys. Rev. Lett. 119, 150405 (2017).
- K. Bu, N. Anand, and U. Singh, Asymmetry and coherence weight of quantum states, Phys. Rev. A 97, 032342 (2018).
- A. S. Holevo, Bounds for the quantity of information transmitted by a quantum communication channel, Prob. Peredachi Inf. 9, 3 (1973).
- M. M. Wilde, Quantum Information Theory (Cambridge University Press, Cambridge, England, 2013).
- M. J. W. Hall and H. M. Wiseman, Does nonlinear metrology offer improved resolution? answers from quantum information theory, Phys. Rev. X 2, 041006 (2012).
- A. Streltsov, U. Singh, H. S. Dhar, M. N. Bera, and G. Adesso, Measuring quantum coherence with entanglement, Phys. Rev. Lett. 115, 020403 (2015).
- T. Theurer, S. Satyajit, and M. B. Plenio, Quantifying dynamical coherence with dynamical entanglement, Phys. Rev. Lett. 125, 130401 (2020).
- D. Wineland, J. Bollinger, W. Itano, F. Moore, and D. Heinzen, Spin squeezing and reduced quantum noise in spectroscopy, Phys. Rev. A 46, R6797(R) (1992).
- S. L. Braunstein and C. M. Caves, Statistical distance and the geometry of quantum states, Phys. Rev. Lett. 72, 3439 (1994).
- S. F. Huelga, C. Macchiavello, T. Pellizzari, A. K. Ekert, M. B. Plenio, and J. I. Cirac, Improvement of frequency standards with quantum entanglement, Phys. Rev. Lett. 79, 3865 (1997).
- R. Augusiak, J. Kołodyński, A. Streltsov, M. N. Bera, A. Acín, and M. Lewenstein, Asymptotic role of entanglement in quantum metrology, Phys. Rev. A 94, 012339 (2016).
- N. Linden and S. Popescu, Good dynamics versus bad kinematics: Is entanglement needed for quantum computation?, Phys. Rev. Lett. 87, 047901 (2001).
- M. Van den Nest, Universal quantum computation with little entanglement, Phys. Rev. Lett. 110, 060504 (2013).
- C. H. Bennett and G. Brassard, Quantum cryptography: Public key distribution and coin tossing, Theor. Comput. Sci. 560, 7 (2014).
- L. Chen and Y. Yang, Optimal quantum metrology under energy constraints, arXiv:2506.09436.
- M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 2.1 (2014).
- J. Löfberg, YALMIP: A toolbox for modeling and optimization in MATLAB, in In Proceedings of the CACSD Conference (Taipei, Taiwan, 2004).
- J. F. Sturm, Using SeDuMi 1.02, a Matlab toolbox for optimization over symmetric cones, Optim. Methods Software 11, 625 (1999).