We propose a novel deterministic method for preparing arbitrary quantum states. When our protocol is compiled into CNOT and arbitrary single-qubit gates, it prepares an -dimensional state in depth and (a metric that accounts for the fact that oftentimes some ancilla qubits need not be active for the entire circuit) , which are both optimal. When compiled into the gate set, we show that it requires asymptotically fewer quantum resources than previous methods. Specifically, it prepares an arbitrary state up to error with optimal depth of and spacetime allocation , improving over and , respectively. We illustrate how the reduced spacetime allocation of our protocol enables rapid preparation of many disjoint states with only constant-factor ancilla overhead – ancilla qubits are reused efficiently to prepare a product state of -dimensional states in depth rather than , achieving effectively constant depth per state. We highlight several applications where this ability would be useful, including quantum machine learning, Hamiltonian simulation, and solving linear systems of equations. We provide quantum circuit descriptions of our protocol, detailed pseudocode, and gate-level implementation examples using Braket.
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Concentration bounds for quantum states and limitations on the QAOA from polynomial approximations
We prove concentration bounds for the following classes of quantum states: (i) output states of shallow quantum circuits, answering an open question from \cite{DMRF22}; (ii) injective matrix product states; (iii) output states of dense Hamiltonian evolution, i.e. states of the form e ι H ( p ) ⋯ e ι H ( 1 ) | ψ 0 ⟩ for any n -qubit product state | ψ 0 ⟩ , where each H ( i ) can be any local commuting Hamiltonian satisfying a norm constraint, including dense Hamiltonians with interactions between any qubits. Our proofs use polynomial approximations to show that these states are close to local operators. This implies that the distribution of the Hamming weight of a computational basis measurement (and of other related observables) concentrates.An example of (iii) are the states produced by the quantum approximate optimisation algorithm (QAOA). Using our concentration results for these states, we show that for a random spin model, the QAOA can only succeed with negligible probability even at super-constant level p = o ( log log n ) , assuming a strengthened version of the so-called overlap gap property. This gives the first limitations on the QAOA on dense instances at super-constant level, improving upon the recent result [BGMZ22].
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- Award ID(s):
- 2013303
- PAR ID:
- 10434653
- Date Published:
- Journal Name:
- Quantum
- Volume:
- 7
- ISSN:
- 2521-327X
- Page Range / eLocation ID:
- 999
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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