Exponential Lindbladian fast forwarding and exponential amplification of certain Gibbs state properties
Pith reviewed 2026-05-25 07:32 UTC · model grok-4.3
The pith
Lindbladian simulation with unitary jumps achieves additive query complexity and exponential fast-forwarding for block-diagonal Pauli structures
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
For Lindbladians with unitary jump operators the simulation algorithm attains additive query complexity O(t + log(ε^{-1})); when the operators possess block-diagonal Pauli structure this extends to exponential fast-forwarding with logarithmic circuit depth, and the same techniques produce an exponential reduction in complexity for estimating certain Gibbs-state matrix elements whenever the input states obey the stated coherence conditions.
What carries the argument
Quantum algorithm for Lindbladian simulation with unitary jump operators, modified for block-diagonal Pauli structure to enable fast-forwarding via parallel access and then applied to coherent Gibbs-state estimation
If this is right
- Query complexity stays additive O(t + log(ε^{-1})) even after the circuit-depth reduction via parallel access.
- Exponential circuit-depth improvement applies directly to estimating Gibbs-state matrix elements under the coherence conditions.
- The same fast-forwarding yields better resource scaling for ground-state overlap testing and amplitude estimation.
- The level of improvement varies continuously with the amount of coherence present in the input states |ψ1⟩ and |ψ2⟩.
Where Pith is reading between the lines
- The exponential fast-forwarding may extend to other open-system models that admit analogous block structures in their jump operators.
- Coherence in the input states functions as a quantifiable resource that trades off against the inverse-temperature parameter β in thermal estimation tasks.
- Small-system numerical simulations could directly test the predicted logarithmic depth scaling for concrete Pauli-structured Lindbladians.
- The approach suggests possible speed-ups in related tasks such as preparing approximate thermal states when coherence is available.
Load-bearing premise
The jump operators must be unitary and, for exponential fast-forwarding, must have block-diagonal Pauli structure; the input states must satisfy the coherence conditions such as the given overlap between all-zero and all-plus states after the thermal factor.
What would settle it
Run the algorithm on a small number of qubits with block-diagonal Pauli jump operators and verify whether the circuit depth required for fixed error scales logarithmically rather than linearly with simulation time t.
read the original abstract
Fast-forwarding refers to the ability to simulate a system of time $t$ using significantly fewer than $t$ queries or circuit depth. While various Hamiltonian systems are known to circumvent the no fast-forwarding theorem, analogous results for dissipative dynamics, governed by Lindbladians, remain largely unexplored. We first present a quantum algorithm for simulating purely dissipative Lindbladians with unitary jump operators, achieving additive query complexity $\mathcal{O}\left(t + \log(\varepsilon^{-1})\right)$ up to error~$\varepsilon$, improving previous algorithms. When the jump operators have certain structures (i.e., block-diagonal Paulis), the algorithm can be modified to achieve exponential fast-forwarding, attaining circuit depth $\mathcal{O}\left(\log\left(t + \log(\varepsilon^{-1})\right)\right)$, while preserving query complexity via parallel access. Using these fast-forwarding techniques, we develop a quantum algorithm for estimating Gibbs state properties of the form $\langle \psi_1 | e^{-\beta(H + I)} | \psi_2 \rangle$, up to additive error $\epsilon$, with $H$ the Hamiltonian and $\beta$ the inverse temperature. For input states exhibiting certain coherence conditions -- e.g.,~$\langle 0|^{\otimes n} e^{-\beta(H + I)} |+\rangle^{\otimes n}$ -- our method achieves exponential improvement in complexity (measured by circuit depth), $\mathcal{O} (2^{-n/2} \epsilon^{-1} \log \beta ),$ compared to the quantum singular value transformation-based approach, with complexity $\tilde{\mathcal{O}} (\epsilon^{-1} \sqrt{\beta} )$. We show how to apply this exponential improvement to applications such as the ground state overlap testing and amplitude estimation. For general $| \psi_1 \rangle$ and $| \psi_2 \rangle$, we also show how the level of improvement is changed with the coherence resource in $| \psi_1 \rangle$ and $| \psi_2 \rangle$.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript claims quantum algorithms for simulating purely dissipative Lindbladians with unitary jump operators that achieve additive query complexity O(t + log(ε^{-1})), with a modification for block-diagonal Pauli jump operators yielding exponential fast-forwarding to circuit depth O(log(t + log(ε^{-1}))) while preserving query complexity via parallel access. These techniques are applied to estimate Gibbs-state matrix elements ⟨ψ₁|e^{-β(H+I)}|ψ₂⟩ to additive error ε, achieving complexity O(2^{-n/2} ε^{-1} log β) under specific coherence conditions on the input states (e.g., overlap with |0⟩^{⊗n} and |+⟩^{⊗n}), compared to Õ(ε^{-1} √β) for QSVT-based methods; applications to ground-state overlap testing and amplitude estimation are indicated, with a general discussion of coherence resources.
Significance. If the algorithmic constructions and analyses hold, the work would constitute a meaningful advance in quantum algorithms for open-system simulation by establishing fast-forwarding results for structured Lindbladians and coherence-dependent exponential improvements for Gibbs-property estimation. The explicit restrictions to unitary jumps and coherence conditions are appropriately foregrounded.
major comments (3)
- [Abstract / Introduction] Abstract and introduction: the central complexity claims (additive O(t + log(ε^{-1})) query complexity, exponential fast-forwarding to O(log(t + log(ε^{-1}))) depth, and O(2^{-n/2} ε^{-1} log β) Gibbs estimation) are stated without any derivation, error analysis, or proof sketch, which is load-bearing for assessing correctness of the fast-forwarding and amplification results.
- [Lindbladian simulation section] The section presenting the Lindbladian simulation algorithm: no explicit construction, Trotterization or product-formula analysis, or query-complexity accounting is supplied to support the claimed additive (rather than multiplicative) dependence on t.
- [Gibbs estimation section] The section on Gibbs-state estimation: the exponential improvement is conditioned on specific coherence overlaps (e.g., ⟨0|^{⊗n} e^{-β(H+I)} |+⟩^{⊗n}), yet no quantitative relation between the overlap magnitude and the stated complexity is derived or bounded.
minor comments (2)
- Notation: the distinction between query complexity and circuit depth should be made uniform across all stated bounds; the parallel-access model for preserving query complexity under exponential fast-forwarding requires a brief definition.
- The comparison to QSVT is given only asymptotically; a short table contrasting the two approaches under the stated coherence assumptions would improve readability.
Simulated Author's Rebuttal
We thank the referee for the careful review and for recognizing the potential significance of the fast-forwarding and coherence-dependent results. We address each major comment below and will revise the manuscript accordingly to improve clarity and completeness.
read point-by-point responses
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Referee: [Abstract / Introduction] Abstract and introduction: the central complexity claims (additive O(t + log(ε^{-1})) query complexity, exponential fast-forwarding to O(log(t + log(ε^{-1}))) depth, and O(2^{-n/2} ε^{-1} log β) Gibbs estimation) are stated without any derivation, error analysis, or proof sketch, which is load-bearing for assessing correctness of the fast-forwarding and amplification results.
Authors: We agree that the abstract and introduction would benefit from additional context on the derivations. In the revised version we will expand the introduction with concise proof sketches that outline the key steps establishing the additive query complexity, the parallel-access construction for exponential depth reduction under block-diagonal Pauli jumps, and the coherence-dependent scaling for the Gibbs estimator. Full technical details and error analyses will continue to reside in the dedicated sections. revision: yes
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Referee: [Lindbladian simulation section] The section presenting the Lindbladian simulation algorithm: no explicit construction, Trotterization or product-formula analysis, or query-complexity accounting is supplied to support the claimed additive (rather than multiplicative) dependence on t.
Authors: The manuscript contains a high-level algorithmic description, but we concur that an explicit construction and rigorous accounting are required for verification. We will revise the Lindbladian simulation section to supply the concrete circuit construction for unitary-jump Lindbladians, specify the product formula employed, and provide a complete query-complexity analysis demonstrating the additive O(t + log(ε^{-1})) scaling together with the associated error bounds. revision: yes
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Referee: [Gibbs estimation section] The section on Gibbs-state estimation: the exponential improvement is conditioned on specific coherence overlaps (e.g., ⟨0|^{⊗n} e^{-β(H+I)} |+⟩^{⊗n}), yet no quantitative relation between the overlap magnitude and the stated complexity is derived or bounded.
Authors: We will augment the Gibbs estimation section with an explicit derivation that relates the magnitude of the coherence overlaps (including the example ⟨0|^{⊗n} e^{-β(H+I)} |+⟩^{⊗n} and generalizations) to the achieved complexity. This will include quantitative bounds showing how the overlap controls the effective circuit depth and query cost, together with the corresponding scaling for general input states. revision: yes
Circularity Check
No significant circularity; derivations are explicit algorithmic constructions
full rationale
The paper presents quantum algorithms for Lindbladian simulation and Gibbs state property estimation conditioned on unitary jump operators (with block-diagonal Pauli structure for exponential fast-forwarding) and specific input coherence conditions. These improvements are derived from explicit constructions (additive query complexity O(t + log(1/ε)) and circuit depth O(log(t + log(1/ε))), with exponential gains for coherent states like ⟨0|⊗n e^{-β(H+I)} |+⟩⊗n). No steps reduce by construction to fitted parameters, self-definitions, or load-bearing self-citations; the central claims remain independent of the target results and are externally falsifiable via the stated assumptions.
Axiom & Free-Parameter Ledger
axioms (2)
- domain assumption Lindblad master equation with unitary jump operators governs the dynamics
- standard math Quantum circuit model with query and depth complexity measures
Forward citations
Cited by 3 Pith papers
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Reference graph
Works this paper leans on
-
[1]
technique in each step to ensure that the success probability does not decay exponentially. Although this is a time-efficient approach, the overall complexity becomes multiplicative, as the overall cost would be the cost per step (which is O(log(T/ε))) times the overall number of steps (which is O(T)). As a comparison, our algorithm does not need to perfo...
-
[2]
Improved simulation of stabilizer circuits
Scott Aaronson and Daniel Gottesman. Improved simulation of stabilizer circuits. Physical Review A—Atomic, Molecular, and Optical Physics , 70(5):052328, 2004
work page 2004
-
[3]
Quantum approximate counting, simplified
Scott Aaronson and Patrick Rall. Quantum approximate counting, simplified. In Symposium on Sim- plicity in Algorithms, pages 24–32. SIAM, 2020
work page 2020
-
[4]
Quantum circuits with mixed states
Dorit Aharonov, Alexei Kitaev, and Noam Nisan. Quantum circuits with mixed states. In Proceedings of the 30th Annual ACM symposium on Theory of Computing, pages 20–30, 1998
work page 1998
-
[5]
Quantum many-body systems in thermal equilibrium
´Alvaro M Alhambra. Quantum many-body systems in thermal equilibrium. PRX Quantum, 4(4): 040201, 2023
work page 2023
-
[6]
Mohammad H Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, and Roger Melko. Quan- tum Boltzmann machine. Physical Review X, 8(2):021050, 2018
work page 2018
-
[7]
Fast-forwarding of hamiltonians and exponentially precise measure- ments
Yosi Atia and Dorit Aharonov. Fast-forwarding of hamiltonians and exponentially precise measure- ments. Nature Communications, 8(1):1572, 2017
work page 2017
-
[8]
Koenraad MR Audenaert and Stefan Scheel. On random unitary channels. New Journal of Physics, 10 (2):023011, 2008
work page 2008
-
[9]
High-temperature gibbs states are unen- tangled and efficiently preparable
Ainesh Bakshi, Allen Liu, Ankur Moitra, and Ewin Tang. High-temperature gibbs states are unen- tangled and efficiently preparable. In 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), pages 1027–1036. IEEE, 2024
work page 2024
-
[10]
Optimizing random local Hamiltonians by dissipation
Joao Basso, Chi-Fang Chen, and Alexander M Dalzell. Optimizing random local Hamiltonians by dissipation. arXiv preprint arXiv:2411.02578, 2024
-
[11]
Strengths and weaknesses of quantum computing
Charles H Bennett, Ethan Bernstein, Gilles Brassard, and Umesh Vazirani. Strengths and weaknesses of quantum computing. SIAM Journal on Computing, 26(5):1510–1523, 1997
work page 1997
-
[12]
Quantum computational advantage with constant-temperature Gibbs sampling
Thiago Bergamaschi, Chi-Fang Chen, and Yunchao Liu. Quantum computational advantage with constant-temperature Gibbs sampling. In2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), pages 1063–1085. IEEE, 2024
work page 2024
-
[13]
Efficient quantum algorithms for simulating sparse Hamiltonians
Dominic W Berry, Graeme Ahokas, Richard Cleve, and Barry C Sanders. Efficient quantum algorithms for simulating sparse Hamiltonians. Communications in Mathematical Physics, 270:359–371, 2007
work page 2007
-
[14]
Expo- nential improvement in precision for simulating sparse Hamiltonians
Dominic W Berry, Andrew M Childs, Richard Cleve, Robin Kothari, and Rolando D Somma. Expo- nential improvement in precision for simulating sparse Hamiltonians. InProceedings of the 46th Annual ACM Symposium on Theory of Computing, pages 283–292, 2014
work page 2014
-
[15]
Sim- ulating Hamiltonian dynamics with a truncated Taylor series
Dominic W Berry, Andrew M Childs, Richard Cleve, Robin Kothari, and Rolando D Somma. Sim- ulating Hamiltonian dynamics with a truncated Taylor series. Physical Review Letters, 114(9):090502, 2015
work page 2015
-
[16]
Quantum speed-ups for solving semidefinite programs
Fernando GSL Brandao and Krysta M Svore. Quantum speed-ups for solving semidefinite programs. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS) , pages 415–426. IEEE, 2017. 13
work page 2017
-
[17]
Quantum amplitude amplification and estimation
Gilles Brassard, Peter Høyer, Michele Mosca, and Alain Tapp. Quantum amplitude amplification and estimation. Contemporary Mathematics, 305:53–74, 2002
work page 2002
-
[18]
On the complexity of quantum partition functions
Sergey Bravyi, Anirban Chowdhury, David Gosset, and Pawel Wocjan. On the complexity of quantum partition functions. arXiv preprint arXiv:2110.15466, 2021
-
[19]
Lower bounds for parallel quantum counting
Paul Burchard. Lower bounds for parallel quantum counting. arXiv preprint arXiv:1910.04555, 2019
-
[20]
Quantum Thermal State Preparation
Chi-Fang Chen, Michael J Kastoryano, Fernando GSL Brand ˜ao, and Andr´as Gily´en. Quantum thermal state preparation. arXiv preprint arXiv:2303.18224, 2023
work page internal anchor Pith review Pith/arXiv arXiv 2023
-
[21]
An efficient and exact noncommutative quantum Gibbs sampler
Chi-Fang Chen, Michael J Kastoryano, and Andr ´as Gily ´en. An efficient and exact noncommutative quantum Gibbs sampler. arXiv preprint arXiv:2311.09207, 2023
-
[22]
Local minima in quantum systems
Chi-Fang Chen, Hsin-Yuan Huang, John Preskill, and Leo Zhou. Local minima in quantum systems. In Proceedings of the 56th Annual ACM Symposium On Theory Of Computing, pages 1323–1330, 2024
work page 2024
-
[23]
On the impossibility of general parallel fast-forwarding of Hamiltonian simulation
Nai-Hui Chia, Kai-Min Chung, Yao-Ching Hsieh, Han-Hsuan Lin, Yao-Ting Lin, and Yu-Ching Shen. On the impossibility of general parallel fast-forwarding of Hamiltonian simulation. In Proceedings of the conference on Proceedings of the 38th Computational Complexity Conference, pages 1–45, 2023
work page 2023
-
[24]
Efficient simulation of sparse Markovian quantum dynamics
Andrew M Childs and Tongyang Li. Efficient simulation of sparse Markovian quantum dynamics. Quantum Information & Computation, 17(11-12):901–947, 2017
work page 2017
-
[25]
Hamiltonian simulation using linear combinations of unitary operations
Andrew M Childs and Nathan Wiebe. Hamiltonian simulation using linear combinations of unitary operations. Quantum Information and Computation, 12(11&12):901–924, 2012
work page 2012
-
[26]
Theory of trotter error with commutator scaling
Andrew M Childs, Yuan Su, Minh C Tran, Nathan Wiebe, and Shuchen Zhu. Theory of trotter error with commutator scaling. Physical Review X, 11(1):011020, 2021
work page 2021
-
[27]
Completely positive linear maps on complex matrices
Man-Duen Choi. Completely positive linear maps on complex matrices. Linear Algebra and its Appli- cations, 10(3):285–290, 1975
work page 1975
-
[28]
Efficient Quantum Algorithms for Simulating Lindblad Evolution
Richard Cleve and Chunhao Wang. Efficient Quantum Algorithms for Simulating Lindblad Evolution. In 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017) , volume 80, page 17. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2017
work page 2017
-
[29]
Richard Cleve, Artur Ekert, Chiara Macchiavello, and Michele Mosca. Quantum algorithms revisited. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences , 454 (1969):339–354, 1998
work page 1969
-
[30]
Introduction to Many-Body Physics
Piers Coleman. Introduction to Many-Body Physics. Cambridge University Press, 2015
work page 2015
-
[31]
Diagonal gates in the Clifford hierarchy
Shawn X Cui, Daniel Gottesman, and Anirudh Krishna. Diagonal gates in the Clifford hierarchy. Physical Review A, 95(1):012329, 2017
work page 2017
-
[32]
Quantum com- puting and polynomial equations over Z2
CM Dawson, HL Haselgrove, AP Hines, D Mortimer, MA Nielsen, and TJ Osborne. Quantum com- puting and polynomial equations over Z2. Quantum Information and Computation, 5(2):102–112, 2005
work page 2005
-
[33]
Single-ancilla ground state preparation via Lindbladians
Zhiyan Ding, Chi-Fang Chen, and Lin Lin. Single-ancilla ground state preparation via Lindbladians. Physical Review Research, 6(3):033147, 2024
work page 2024
-
[34]
Simulating open quantum systems using Hamiltonian simula- tions
Zhiyan Ding, Xiantao Li, and Lin Lin. Simulating open quantum systems using Hamiltonian simula- tions. PRX Quantum, 5(2):020332, 2024
work page 2024
-
[35]
Efficient quantum Gibbs samplers with Kubo–Martin– Schwinger detailed balance condition
Zhiyan Ding, Bowen Li, and Lin Lin. Efficient quantum Gibbs samplers with Kubo–Martin– Schwinger detailed balance condition. Communications in Mathematical Physics, 406(3):67, 2025
work page 2025
-
[36]
Simulating physics with computers
Richard P Feynman. Simulating physics with computers. In Feynman and computation, pages 133–153. cRc Press, 2018
work page 2018
-
[37]
Andr ´as Gily´en, Yuan Su, Guang Hao Low, and Nathan Wiebe. Quantum singular value transforma- tion and beyond: exponential improvements for quantum matrix arithmetics. InProceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, pages 193–204, 2019
work page 2019
-
[38]
Completely positive dynamical semigroups of n-level systems
Vittorio Gorini, Andrzej Kossakowski, and Ennackal Chandy George Sudarshan. Completely positive dynamical semigroups of n-level systems. Journal of Mathematical Physics, 17(5):821–825, 1976
work page 1976
-
[39]
The Heisenberg representation of quantum computers
Daniel Gottesman. The Heisenberg representation of quantum computers. arXiv preprint quant- ph/9807006, 1998
-
[40]
Iterative quantum amplitude esti- mation
Dmitry Grinko, Julien Gacon, Christa Zoufal, and Stefan Woerner. Iterative quantum amplitude esti- mation. npj Quantum Information, 7(1):52, 2021
work page 2021
-
[41]
A fast quantum mechanical algorithm for database search
Lov K Grover. A fast quantum mechanical algorithm for database search. In Proceedings of the 28th Annual ACM Symposium on Theory of Computing, pages 212–219, 1996
work page 1996
-
[42]
Fast-forwarding quantum evolution
Shouzhen Gu, Rolando D Somma, and Burak S ¸ahino˘glu. Fast-forwarding quantum evolution. Quan- tum, 5:577, 2021
work page 2021
-
[43]
Designing open 14 quantum systems with known steady states: Davies generators and beyond
Jinkang Guo, Oliver Hart, Chi-Fang Chen, Aaron J Friedman, and Andrew Lucas. Designing open 14 quantum systems with known steady states: Davies generators and beyond. Quantum, 9:1612, 2025
work page 2025
-
[44]
Process tomography for unitary quantum channels
Gus Gutoski and Nathaniel Johnston. Process tomography for unitary quantum channels. Journal of Mathematical Physics, 55(3), 2014
work page 2014
-
[45]
Jeongwan Haah, Matthew B Hastings, Robin Kothari, and Guang Hao Low. Quantum algorithm for simulating real time evolution of lattice Hamiltonians.SIAM Journal on Computing, 52(6):FOCS18–250, 2021
work page 2021
-
[46]
Query-optimal estimation of uni- tary channels in diamond distance
Jeongwan Haah, Robin Kothari, Ryan O’Donnell, and Ewin Tang. Query-optimal estimation of uni- tary channels in diamond distance. In 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS), pages 363–390. IEEE, 2023
work page 2023
-
[47]
Ryszard Horodecki, Paweł Horodecki, Michał Horodecki, and Karol Horodecki. Quantum entangle- ment. Reviews of Modern Physics, 81(2):865–942, 2009
work page 2009
-
[48]
Probabilistic unitary formulation of open quantum system dynamics
Le Hu and Andrew N Jordan. Probabilistic unitary formulation of open quantum system dynamics. Physical Review A, 110(6):062205, 2024
work page 2024
-
[49]
The complexity of the local Hamiltonian problem
Julia Kempe, Alexei Kitaev, and Oded Regev. The complexity of the local Hamiltonian problem. In International Conference on Foundations of Software Technology and Theoretical Computer Science , pages 372–383. Springer, 2004
work page 2004
-
[50]
Quantum measurements and the Abelian stabilizer problem
A Yu Kitaev. Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant- ph/9511026, 1995
-
[51]
Dissipative quantum Church-Turing theorem
Martin Kliesch, Thomas Barthel, Christian Gogolin, Michael Kastoryano, and Jens Eisert. Dissipative quantum Church-Turing theorem. Physical Review Letters, 107(12):120501, 2011
work page 2011
-
[52]
Simulating markovian open quantum systems using higher-order series expansion
Xiantao Li and Chunhao Wang. Simulating markovian open quantum systems using higher-order series expansion. In 50th International Colloquium on Automata, Languages, and Programming, ICALP 2023, page 87. Schloss Dagstuhl-Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2023
work page 2023
-
[53]
Daniel A Lidar and Todd A Brun. Quantum Error Correction. Cambridge university press, 2013
work page 2013
-
[54]
Lin Lin. Dissipative Preparation of Many-Body Quantum States: Towards Practical Quantum Advan- tage. arXiv preprint arXiv:2505.21308, 2025
-
[55]
On the generators of quantum dynamical semigroups
Goran Lindblad. On the generators of quantum dynamical semigroups. Communications in Mathemat- ical Physics, 48:119–130, 1976
work page 1976
-
[56]
Seth Lloyd. Universal quantum simulators. Science, 273(5278):1073–1078, 1996
work page 1996
-
[57]
Hamiltonian Simulation by Uniform Spectral Amplification
Guang Hao Low and Isaac L Chuang. Hamiltonian simulation by uniform spectral amplification. arXiv preprint arXiv:1707.05391, 2017
work page internal anchor Pith review Pith/arXiv arXiv 2017
-
[58]
Optimal Hamiltonian simulation by quantum signal processing
Guang Hao Low and Isaac L Chuang. Optimal Hamiltonian simulation by quantum signal processing. Physical Review Letters, 118(1):010501, 2017
work page 2017
-
[59]
Hamiltonian simulation by qubitization
Guang Hao Low and Isaac L Chuang. Hamiltonian simulation by qubitization. Quantum, 3:163, 2019
work page 2019
-
[60]
Quantum computational chemistry
Sam McArdle, Suguru Endo, Al ´an Aspuru-Guzik, Simon C Benjamin, and Xiao Yuan. Quantum computational chemistry. Reviews of Modern Physics, 92(1):015003, 2020
work page 2020
-
[61]
Quantum and classical query complexities of functions of matrices
Ashley Montanaro and Changpeng Shao. Quantum and classical query complexities of functions of matrices. In Proceedings of the 56th Annual ACM Symposium on Theory of Computing , pages 573–584, 2024
work page 2024
-
[62]
On low-depth algorithms for quantum phase estimation
Hongkang Ni, Haoya Li, and Lexing Ying. On low-depth algorithms for quantum phase estimation. Quantum, 7:1165, 2023
work page 2023
-
[63]
Quantum Computation and Quantum Information
Michael A Nielsen and Isaac L Chuang. Quantum Computation and Quantum Information. Cambridge university press, 2010
work page 2010
-
[64]
Rom ´an Or´us. A practical introduction to tensor networks: Matrix product states and projected entan- gled pair states. Annals of physics, 349:117–158, 2014
work page 2014
-
[65]
Quantum simulation of Lindbladian dynamics via repeated interactions
Matthew Pocrnic, Dvira Segal, and Nathan Wiebe. Quantum simulation of Lindbladian dynamics via repeated interactions. Journal of Physics A: Mathematical and Theoretical, 58(30):305302, 2025
work page 2025
-
[66]
Lecture notes for physics 229: Quantum information and computation
John Preskill. Lecture notes for physics 229: Quantum information and computation. California Insti- tute of Technology, 16(1):1–8, 1998
work page 1998
-
[67]
Gibbs Sampling gives Quantum Advantage at Constant Tem- peratures with O(1)-Local Hamiltonians
Joel Rajakumar and James D Watson. Gibbs Sampling gives Quantum Advantage at Constant Tem- peratures with O(1)-Local Hamiltonians. arXiv preprint arXiv:2408.01516, 2024
-
[68]
Amplitude estimation from quantum signal processing
Patrick Rall and Bryce Fuller. Amplitude estimation from quantum signal processing. Quantum, 7: 937, 2023
work page 2023
-
[69]
Optimal quantum algorithm for Gibbs state preparation
Cambyse Rouz ´e, Daniel Stilck Franc ¸a, and ´Alvaro M Alhambra. Optimal quantum algorithm for Gibbs state preparation. arXiv preprint arXiv:2411.04885, 2024. 15
-
[70]
Efficient thermalization and universal quantum computing with quantum gibbs samplers
Cambyse Rouz ´e, Daniel Stilck Franc ¸a, and´Alvaro M Alhambra. Efficient thermalization and universal quantum computing with quantum gibbs samplers. In Proceedings of the 57th Annual ACM Symposium on Theory of Computing, pages 1488–1495, 2025
work page 2025
-
[71]
Faster algorithms via approximation theory
Sushant Sachdeva, Nisheeth K Vishnoi, et al. Faster algorithms via approximation theory. Foundations and Trends® in Theoretical Computer Science, 9(2):125–210, 2014
work page 2014
-
[72]
Quantum simulation of open quantum systems using a unitary decomposition of operators
Anthony W Schlimgen, Kade Head-Marsden, LeeAnn M Sager, Prineha Narang, and David A Mazz- iotti. Quantum simulation of open quantum systems using a unitary decomposition of operators. Physical Review Letters, 127(27):270503, 2021
work page 2021
-
[73]
Estimating quantum amplitudes can be exponentially improved
Zhong-Xia Shang and Qi Zhao. Estimating quantum amplitudes can be exponentially improved. arXiv preprint arXiv:2408.13721, 2024
-
[74]
Zhong-Xia Shang, Zi-Han Chen, Ming-Cheng Chen, Chao-Yang Lu, and Jian-Wei Pan. A polynomial- time quantum algorithm for solving the ground states of a class of classically hard Hamiltonians. arXiv preprint arXiv:2401.13946, 2024
-
[75]
Design nearly optimal quantum algorithm for linear differential equations via Lindbladians
Zhong-Xia Shang, Naixu Guo, Dong An, and Qi Zhao. Design nearly optimal quantum algorithm for linear differential equations via Lindbladians. arXiv preprint arXiv:2410.19628, 2024
-
[76]
Algorithms for quantum computation: discrete logarithms and factoring
Peter W Shor. Algorithms for quantum computation: discrete logarithms and factoring. In Proceedings 35th Annual symposium on foundations of computer science, pages 124–134. IEEE, 1994
work page 1994
-
[77]
Colloquium: Quantum coherence as a resource
Alexander Streltsov, Gerardo Adesso, and Martin B Plenio. Colloquium: Quantum coherence as a resource. Reviews of Modern Physics, 89(4):041003, 2017
work page 2017
-
[78]
Quantum SDP-solvers: Better upper and lower bounds
Joran Van Apeldoorn, Andr ´as Gily´en, Sander Gribling, and Ronald de Wolf. Quantum SDP-solvers: Better upper and lower bounds. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), pages 403–414. IEEE, 2017
work page 2017
-
[79]
Polynomial-time classical sampling of high-temperature quantum Gibbs states
Chao Yin and Andrew Lucas. Polynomial-time classical sampling of high-temperature quantum Gibbs states. arXiv preprint arXiv:2305.18514, 2023
-
[80]
Exponentially reduced circuit depths in Lindbla- dian simulation
Wenjun Yu, Xiaogang Li, Qi Zhao, and Xiao Yuan. Exponentially reduced circuit depths in Lindbla- dian simulation. arXiv preprint arXiv:2412.21062, 2024
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