Strong empiricalAlgorithmsAlgorithmBlock encoding · polynomial matrix transformation
Quantum singular value transformation
A framework for applying bounded polynomial transformations to singular values of a block-encoded matrix.
Every quantum algorithm worth knowing about, written down the same way: what it takes, what it returns, what it costs, and who proved it.
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13 public entries
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A framework for applying bounded polynomial transformations to singular values of a block-encoded matrix.
Solve the heat equation in a rectangular region of spatial dimension d, in the sense of approximately computing the amount of heat in a given region.
Given a first-order linear differential equation d/dt x = A(t)x + b(t) with N-dimensional vectors x and b and an N×N matrix A, and given an initial condition x(0), produce the solution x(t) at a later time t to precision ε, in the sense that the normalized vector x(t)/‖x(t)‖ returned is at distance at most ε from the exact solution.
Encode a linear kinetic plasma problem — modeling electrostatic linear waves, driven by a spatially localized external current, in a one-dimensional Maxwellian electron plasma — into a quantum circuit that solves the resulting linear system.
Solve the transport equation — for variable grid sizes and discrete particle velocities, in two and three spatial dimensions — on a fault-tolerant universal quantum computer, including the reflection of particles at the walls, edges and corners of obstacles.
Simulate the wave equation under Dirichlet and Neumann boundary conditions on a quantum computer, using Hamiltonian simulation and quantum linear system algorithms as subroutines.
Given a system of nonlinear ordinary differential equations, in the primary algorithm's case a dissipative quadratic n-dimensional system, produce the solution at a chosen evolution time T to error ε, encoded in the amplitudes of a quantum state.
A linear-algebra reference that forces the catalog to show input loading, conditioning, and output observability.
Adapt the Harrow-Hassidim-Lloyd (HHL) quantum linear-systems algorithm, most of whose components current noisy quantum hardware cannot reach, into a form that near-term devices can actually execute, and demonstrate it on an application.
Given quantum access to a historical record of asset returns, determine the optimal risk-return tradeoff curve of a portfolio and provide a way to sample from the optimal portfolio.
Given a function f and a positive semi-definite matrix A whose eigenvalues are λⱼ, estimate the spectral sum Tr[f(A)] = Σⱼ f(λⱼ), a family whose typical examples the paper gives as the von Neumann entropy, the trace of A⁻¹, the log-determinant and the Schatten p-norm, the last of which it says does not require the matrix to be positive semi-definite.
Given query access to the entries of a d × d Hermitian matrix A, output a classical description of a good approximation of its top eigenvector, the eigenvector belonging to the largest eigenvalue.
Given oracle access to a weighted graph on N vertices of maximum degree d whose edge weights are conductances, so that an edge of weight w carries resistance 1/w, estimate the effective resistance between a chosen pair of vertices s and t to within a factor of 1 + ε. Wang poses it as ENA-ER: for a network with |V| = N, deg(G) ≤ d, edge conductances normalized to 1 ≤ w_e ≤ c for every edge — equivalently edge resistances in [1/c, 1] — and spectral gap λ2(L_G) ≥ λ > 0, estimate R_eff(s, t) up to multiplicative error ε, succeeding with probability at least 2/3, given a procedure Pv that on input a vertex index and a number k returns the k-th edge incident to that vertex and a procedure Pe that on input an edge index returns that edge's two endpoints and its weight, both of which Wang assumes can be implemented in time poly(log(N)).