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HHL sparse-query and state-preparation cost
Statement
With the exact sorted QA20 XOR location/value interface and separately supplied uniform preparation, value-decoding, flag arithmetic and the certified QA25 phase-generation mode, the sparse-oracle specialization is: for a -sparse Hermitian instance with , supported gap , and target state error , the phase-estimation HHL method uses sparse-row/value queries and calls to and separately to , multiplied by their supplied costs. For fixed constant , this specializes to the displayed query order without an additional failure logarithm; for variable , include the factor . Arithmetic, gate/bit time, phase generation, uniform preparation, reflections and the OP5 sine table remain separately charged; the displayed asymptotic concerns sparse-oracle calls. Padding to the register of the sorted-access construction extends by zero, and the supplied input preparation remains supported on the original labels. Under direct simulation access, the simulator contribution is instead the explicit sum in HHL cost under direct controlled-simulation access.
Facts & Assumptions
Given: The finite OP5 parameters, exact sorted sparse access, norm/gap promise, and separately charged operations in the Statement.
Clean sparse-state preparation from sorted XOR locations constructs an exact block encoding of with normalization by taking entry bound , using location calls and value calls per encoding, adjoint or coherent control.
Qubitization query and gate resources and Correctness of the small-time-aware simulator give clean-input simulation error at most and signal calls for each nontrivial signed time, with exact identity on the stated small-time branch. Classical phase generation and other gate costs are separate.
Unitary hybrids on a preserved clean-work subspace supplies coherent-control, reference and actual-adjoint bounds and a hybrid along the ideal clean-work trajectory.
HHL finite sine-window phase-estimation algorithm and HHL cost under direct controlled-simulation access give , the least power of two at least , , , , fewer than absolute simulation time per or , and at most calls in each direction.
OP5 HHL returns the QLSP state within the requested error gives the requested state and amplification success guarantee with the declared OP5 precision budgets, including clean-workspace simulation.
Unitary and differential identities for finite matrix dynamics: is unitary and differentiable with derivative . With , the inner-product norm triangle inequality (The inner-product norm is definite, homogeneous, and satisfies the triangle inequality) gives by integrating the derivative on each unit vector and taking the operator-norm supremum.
The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: bounds for every unit vector .
Proof
Apply [F1] with , valid because . The extra padded system labels carry zero eigenvalues; the input remains in the original supported subspace, so its nonzero gap and inverse-state target do not change. The full unitary oracle circuits, controls and reversals act on all physical labels, including invalid labels, as constructed in [F1]; no padded-branch overlap contributes a matrix value.
Put . For each phase time , certified arithmetic selects a rational with and a rational simulator tolerance with ; their construction cost remains in the separately charged arithmetic/phase-generation coordinates. Invoke QA25 at rational signed time and tolerance . By [F2], its clean-input error from is at most , and by [F6]–[F7] the time-rounding error from is at most . Thus each effective forward call has clean-input error at most . Use its actual adjoint for the reverse call; [F3] gives the same clean-input error without assuming full-space accuracy on dirty inputs. The effective errors sum to as required in OP5, so [F5] applies with the stated state and failure guarantees.
In one or , sum the loose signal-call bound in [F2] over all signed rational times . Since , their absolute values sum to less than by [F4], so this uses signal calls; also makes . Multiplying by [F1]'s location/value conversion and then by the at most preparation/inverse-preparation calls gives sparse-oracle calls, with a possibly smaller constant value-query count. Literal reversals and coherent controls change only constants already covered by [F1]–[F3].
Since , . With , , and , the expression in step 3.1 is . For fixed constant this gives the displayed order. The separate and counts are at most each by [F4]. Padding only appends clean high index bits to their input/output register convention; no extra sparse query is needed for this embedding. Their actual implementation and any controls used remain charged as supplied costs. All other direct-simulation resource coordinates stay in [F4]'s explicit sum, so no table, phase-generation, gate or bit-time claim is inferred from the sparse-query bound.
Depends on
- HHL finite sine-window phase-estimation algorithm
- OP5 HHL returns the QLSP state within the requested error
- HHL cost under direct controlled-simulation access
- Sparse Hamiltonian row and value oracles
- Hamiltonian simulation error and resources
- Circuit-family and computation cost model
- Clean sparse-state preparation from sorted XOR locations
- Unitary hybrids on a preserved clean-work subspace
- Small-time-aware qubitized simulator
- Correctness of the small-time-aware simulator
- Qubitization query and gate resources
- Unitary and differential identities for finite matrix dynamics
- The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||
- The inner-product norm is definite, homogeneous, and satisfies the triangle inequality
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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Sources
- Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd, Quantum algorithm for linear systems of equations (standard reference, not scraped)
- Andrew M. Childs, Robin Kothari, and Rolando D. Somma, Quantum algorithm for systems of linear equations with exponentially improved dependence on precision (standard reference, not scraped)
- Andrew M. Childs, Lecture Notes on Quantum Algorithms (standard reference, not scraped)