Quantum Algorithm Research Seminar (QARS)
Papers and resources
A bank of papers and references discussed in (or related to) the seminars, grouped by topic.
Suggested entry format:
- [Title](link) — Author(s), venue/year — *discussed YYYY-MM-DD* (optional note)
State Learning, Testing, and Tomography
State Learning I: Classical Shadows
- Predicting Many Properties of a Quantum System from Very Few Measurements — Huang, Kueng, Preskill (Nat. Phys. 2020)
- Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows — Hu et al. (Nat. Commun. 2025)
State Learning II: QRAM
- A survey on the complexity of learning quantum states — Anshu, Arunachalam (Nat. Rev. Phys. 2024)
- Foundations for learning from noisy quantum experiments — Huang et al. (2022)
- Exponential separations between learning with and without quantum memory — Chen, Cotler, Huang, Li (FOCS 2021)
- Optimal tradeoffs for estimating Pauli observables — Chen, Gong, Ye (FOCS 2024)
- Triply efficient shadow tomography — King et al. (PRX Quantum 2025)
State Testing
- Few Single-Qubit Measurements Suffice to Certify Any Quantum State — Gupta, He, O’Donnell (2025)
- Certifying almost all quantum states with few single-qubit measurements — Huang, Preskill, Soleimanifar (Nat. Phys. 2025)
- Universal and Efficient Quantum State Verification via Schmidt Decomposition and Mutually Unbiased Bases — Li, Zhu (Quantum 2026)
Quantum Simulation & Many-body Methods
Hamiltonian Simulation
- Theory of Trotter Error with Commutator Scaling — Childs, Su, Tran, Wiebe, Zhu (PRX 2021)
- Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution — Motta et al. (Nat. Phys. 2019)
- Correcting and extending Trotterized quantum many-body dynamics — Gentinetta et al. (PRX Quantum 2025)
- Making Trotterization Adaptive and Energy-Self-Correcting for NISQ Devices and Beyond — Zhao et al. (PRX Quantum 2023)
- Learning Circuits with Infinite Tensor Networks — Gibbs, Cincio (2025)
Analog & Digital-Analog Quantum Computation
- Universal Dynamics with Globally Controlled Analog Quantum Simulators — Hu et al. (2025)
- Quantum advantage and stability to errors in analogue quantum simulators — Trivedi et al. (Nat. Commun. 2024)
- Practical quantum advantage in quantum simulation — Daley, Bloch et al. (Nature 2022)
- Digital-analog quantum computing of fermion-boson models in superconducting circuits — Yamamoto et al. (npj QI 2025)
- Thermalization and criticality on an analogue–digital quantum simulator — Andersen et al. (Nature 2025)
- Constructive interference at the edge of quantum ergodic dynamics — Google Quantum AI et al. (2025)
- A quantum processor based on coherent transport of entangled atom arrays — Bluvstein et al. (Nature 2022)
Tensor Networks for Quantum Computing
- Tensor networks for quantum computing — Berezutskii et al. (Nat. Rev. Phys. 2025)
- Tensor Network Techniques for Quantum Computation — Collura et al. (2025)
- The density-matrix renormalization group in the age of matrix product states — Schollwöck (Ann. Phys. 2011) (introductory DMRG reference)
Quantum Gibbs Sampling
- Quantum generalizations of Glauber and Metropolis dynamics — Gilyén, Chen, Doriguello, Kastoryano (2024)
- Local minima in quantum systems — Chen, Huang, Preskill, Zhou (Nat. Phys. 2025)
- Dissipative Preparation of Many-Body Quantum States: Towards Practical Quantum Advantage — Lin (2025)
- End-to-End Efficient Quantum Thermal and Ground State Preparation Made Simple — Ding, Zhan, Preskill, Lin (2025)
- Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus — Zapusek, Rojkov, Reiter (2025)
- Rapid quantum ground state preparation via dissipative dynamics — Zhan, Ding, Huhn, Gray, Preskill, Chan, Lin (PRX 2026)
- Rapid initial state preparation for the quantum simulation of strongly correlated molecules — Berry et al. (PRX Quantum 2025)
- Single-ancilla ground state preparation via Lindbladians — Ding, Chen, Lin (Phys. Rev. Research 2024)
Quantum Algorithms for Nonlinear Differential Equations
- Efficient quantum algorithm for dissipative nonlinear differential equations — Liu, Kolden, Krovi, Loureiro, Trivisa, Childs (PNAS 2021)
- Quantum simulation of a noisy classical nonlinear dynamics — Bravyi et al. (2025)
Quantum Algorithms
Grover’s Search
- Amplitude amplification and estimation require inverses — Tang, Wright (2025)
- Grover’s algorithm is an approximation of imaginary-time evolution — Suzuki, Gluza, Son, Tiang, Ng, Holmes (2025)
- Opening the Black Box Inside Grover’s Algorithm — Stoudenmire, Waintal (PRX 2024)
Optimization
- Optimization by Decoded Quantum Interferometry — Jordan, Shutty, Wootters et al. (Nature 2025)
- Lower bounding the MaxCut of high girth 3-regular graphs using the QAOA
- Towards large-scale quantum optimization solvers with few qubits — Sciorilli, Borges, Patti et al. (Nat. Commun. 2025)
- (Sub)Exponential Quantum Speedup for Optimization — Leng et al. (2025)
Grand Unification of Algorithms
- A Grand Unification of Quantum Algorithms — Martyn, Rossi, Tan, Chuang (PRX Quantum 2021)
Complexity & Quantum Advantage
Quantum Advantage
- The vast world of quantum advantage — Huang, Choi, McClean, Preskill (2025)
- (Sub)Exponential Quantum Speedup for Optimization — Leng et al. (2025)
- Demonstrating an unconditional separation between quantum and classical information resources — Kretschmer et al. (2025)
- A Framework for Quantum Advantage — Lanes et al. (2025)
- Is there evidence for exponential quantum advantage in quantum chemistry? — Lee, Lee et al. (Nat. Commun. 2023, “Evaluating the evidence…”)
Complexity
- A Brief Introduction to Quantum Query Complexity — Hamoudi (2025)
- Strongly interacting fermions are non-trivial yet non-glassy — Anschuetz et al. (2024)
Nonlocal Games & Interactive Proofs
- Application-level Benchmarking of Quantum Computers using Nonlocal Game Strategies — Furches, Chehade, Hamilton, Wiebe, Ortiz Marrero (2023)
- Interactive proofs for verifying (quantum) learning and testing — Caro et al. (2024)
Pseudoentanglement
- Quantum Pseudoentanglement — Aaronson, Bouland, Fefferman, Ghosh, Vazirani, Zhang, Zhou (ITCS 2024)
- Dynamics of Pseudoentanglement — Feng, Ippoliti (JHEP 2025)
- Pseudoentanglement from Tensor Networks — Cheng, Ippoliti et al. (PRL 2025)
Random Unitaries (unitary designs & pseudorandom unitaries)
- Unitary designs in nearly optimal depth — Cui, Schuster, Brandao, Huang (2025)
- Short remarks on shallow unitary circuits — Haah (Quantum 2025)
- Random Unitaries in Constant (Quantum) Time — Foxman, Parham, Vasconcelos, Yuen (ITCS 2026)
- Random unitaries in extremely low depth — Schuster, Haferkamp, Huang (Science 2025)
Quantum Computing Models & Architecture
Distributed Quantum Computing
- Review of Distributed Quantum Computing: From single QPU to High Performance Quantum Computing — Barral et al. (Comp. Sci. Rev. 2025)
- Combining quantum processors with real-time classical communication — Carrera Vázquez et al. (Nature 2024)
- Distributed Quantum Simulation — (2024)
- Distributed Quantum Dynamics on Near-Term Quantum Processors — Bohun et al. (2025)
Blind Quantum Computation
- Private quantum computation: an introduction to blind quantum computing and related protocols — Fitzsimons (npj QI 2017)
- Universal blind quantum computation — Broadbent, Fitzsimons, Kashefi (FOCS 2009)
Quantum Programming
- The Structure and Interpretation of Quantum Programs I: Foundations — Wakeham (2025)
Quantum Memory
- A quantum random access memory (QRAM) using a polynomial encoding of binary strings — Mukhopadhyay (Sci. Rep. 2025) —
From Noisy to Fault-Tolerant
- Strengths and Weaknesses of Quantum Computing — Bennett, Bernstein, Brassard, Vazirani (SIAM J. Comput. 1997)
- Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization — Yu et al. (2025)
- Using Quantum Metrological Bounds in Quantum Error Correction: A Simple Proof of the Approximate Eastin-Knill Theorem — Kubica, Demkowicz-Dobrzański (PRL 2021)
Quantum Machine Learning
Backpropagation (ML-QML)
- Learning representations by back-propagating errors — Rumelhart, Hinton, Williams (Nature 1986)
- How the backpropagation algorithm works — Nielsen, ch. 2 of Neural Networks and Deep Learning
- On quantum backpropagation, information reuse, and cheating measurement collapse — Abbas, King, Huang, Huggins, Movassagh, Gilboa, McClean (NeurIPS 2023)
- Theory of the backpropagation neural network — Hecht-Nielsen (IJCNN 1989)