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arxiv 0910.4157 v4 pith:WD4MA43I submitted 2009-10-22 quant-ph

Black-box Hamiltonian simulation and unitary implementation

classification quant-ph
keywords black-boxunitaryelementshamiltoniansmethodscomplexityimplementingmatrix
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present general methods for simulating black-box Hamiltonians using quantum walks. These techniques have two main applications: simulating sparse Hamiltonians and implementing black-box unitary operations. In particular, we give the best known simulation of sparse Hamiltonians with constant precision. Our method has complexity linear in both the sparseness D (the maximum number of nonzero elements in a column) and the evolution time t, whereas previous methods had complexity scaling as D^4 and were superlinear in t. We also consider the task of implementing an arbitrary unitary operation given a black-box description of its matrix elements. Whereas standard methods for performing an explicitly specified N x N unitary operation use O(N^2) elementary gates, we show that a black-box unitary can be performed with bounded error using O(N^{2/3} (log log N)^{4/3}) queries to its matrix elements. In fact, except for pathological cases, it appears that most unitaries can be performed with only O(sqrt{N}) queries, which is optimal.

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Cited by 3 Pith papers

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  2. Quantum Data Fitting Algorithm for Non-sparse Matrices

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    Quantum data fitting algorithm for non-sparse N x N Hermitian matrices achieves O(κ² √N polylog(N) / (ε log κ)) runtime via QSVE, eigenvalue sign recovery, and regularization.

  3. Lower overhead fault-tolerant building blocks for noisy quantum computers

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    New combinatorial proofs and circuit designs for quantum error correction reduce physical qubit overhead by up to 10x and time overhead by 2-6x for codes including Steane, Golay, and surface codes.