EXPONENTIAL IMPROVEMENT IN PRECISION FOR SIMULATING SPARSE HAMILTONIANS
DOI10.1017/fms.2017.2zbMath1364.68213arXiv1312.1414OpenAlexW2593381501WikidataQ59438236 ScholiaQ59438236MaRDI QIDQ2971052
Rolando Somma, Robin Kothari, Richard Cleve, Andrew M. Childs, Dominic W. Berry
Publication date: 4 April 2017
Published in: Forum of Mathematics, Sigma, Proceedings of the forty-sixth annual ACM symposium on Theory of computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1312.1414
Quantum computation (81P68) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17) Quantum algorithms and complexity in the theory of computing (68Q12)
Related Items (38)
Uses Software
Cites Work
- On the relationship between continuous- and discrete-time quantum walk
- Quantum Arthur-Merlin games
- Efficient quantum algorithms for simulating sparse Hamiltonians
- Efficient algorithms for privately releasing marginals via convex relaxations
- Quantum algorithms for learning symmetric juntas via the adversary bound
- Concrete resource analysis of the quantum linear-system algorithm used to compute the electromagnetic scattering cross section of a 2D target
- The Geometry of Differential Privacy: The Small Database and Approximate Cases
- Faster Algorithms for Privately Releasing Marginals
- Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
- Privately Releasing Conjunctions and the Statistical Query Barrier
- On the geometry of differential privacy
- Interactive privacy via the median mechanism
- The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
- Lower Bounds in Differential Privacy
- Iterative Constructions and Private Data Release
- Zero-knowledge against quantum attacks
- Universal Computation by Multiparticle Quantum Walk
- EXPONENTIAL IMPROVEMENT IN PRECISION FOR SIMULATING SPARSE HAMILTONIANS
- Characterizing the sample complexity of private learners
- Faster private release of marginals on small databases
- Discrete-query quantum algorithm for NAND trees
- Any AND-OR Formula of Size N Can Be Evaluated in Time $N^{1/2+o(1)}$ on a Quantum Computer
- Simulating Sparse Hamiltonians with Star Decompositions
- Universal Quantum Simulators
- Simulating quantum dynamics on a quantum computer
- Product formula methods for time-dependent Schrodinger problems
- Bounds on the Sample Complexity for Private Learning and Private Data Release
- Adiabatic quantum state generation and statistical zero knowledge
- Exponential algorithmic speedup by a quantum walk
- Differential Privacy and the Fat-Shattering Dimension of Linear Queries
- Our Data, Ourselves: Privacy Via Distributed Noise Generation
- New Efficient Attacks on Statistical Disclosure Control Mechanisms
- General theory of fractal path integrals with applications to many-body theories and statistical physics
- Collusion-secure fingerprinting for digital data
- On the complexity of differentially private data release
- Efficient discrete-time simulations of continuous-time quantum query algorithms
- Advances in Cryptology – CRYPTO 2004
- Quantum lower bounds by polynomials
- Quantum Query Complexity of State Conversion
- Answering n {2+o(1)} counting queries with differential privacy is hard
- Exponentially more precise quantum simulation of fermions in second quantization
- Theory of Cryptography
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